Evaluation Results

Model Performance by Category
api/deepseek/deepseek-chat-v3-0324	86	86	91	88	100	87	87	85	100	92	100	100	100	100	100	100	100	100	100	84	92	88	100	100	100
api/claude-3-7-sonnet-20250219	86	75	95	90	100	100	100	100	85	92	100	100	100	100	100	100	100	100	75	64	100	92	100	100	100
api/claude-3-5-sonnet-20241022	86	68	93	87	100	100	100	42	92	78	100	100	50	100	100	100	100	100	100	67	94	90	100	100	100
api/google/gemma-3-27b-it	86	61	87	75	100	100	100	35	78	85	100	50	50	100	100	100	75	100	75	64	90	75	0	100	0
api/gpt-4o	86	56	79	86	100	87	100	21	64	92	50	50	50	100	100	100	75	100	100	62	83	88	0	100	0
api/meta-llama/llama-3-70b-instruct	86	39	72	41	87	87	75	21	35	14	0	50	0	100	100	100	75	100	75	37	81	45	0	50	0
api/gemini-2.0-flash	86	15	13	15	50	50	50	14	14	14	0	0	0	0	0	0	0	0	0	13	11	13	0	0	0
api/gemini-2.5-pro-exp-03-25	86	2	2	2	0	0	0	7	7	7	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0

add_calculated_column

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Add a new column 'family_size' that is the sum of SibSp and Parch columns plus 1 (for the passenger themselves)
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars column arithmetic: df.with_columns((pl.col("a") + pl.col("b")).alias("sum"))

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size")
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.col("family_size").alias(pl.col("SibSp") + pl.col("Parch") + 1))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpnx3ttpna.py", line 6, in 
    df = df.with_column(pl.col("family_size").alias(pl.col("SibSp") + pl.col("Parch") + 1))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("SibSp").add(pl.col("Parch")).add(1).alias("family_size")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size")
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size")
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def add_family_size(df: pl.DataFrame) -> pl.DataFrame:
    """Adds a new column 'family_size' to the DataFrame.

    Args:
        df: Input Polars DataFrame.

    Returns:
        Polars DataFrame with the added 'family_size' column.
    """
    df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))
    return df

if __name__ == '__main__':
    # Example Usage (assuming titanic.csv exists in the data folder)
    try:
        df = pl.read_csv("data/titanic.csv")
        df = add_family_size(df)
        print(df.head())
    except FileNotFoundError:
        print("Error: titanic.csv not found in the data folder.  Create dummy data for demonstration.")
        # Create a dummy DataFrame for demonstration
        data = {
            'PassengerId': [1, 2, 3],
            'Survived': [0, 1, 0],
            'Pclass': [3, 1, 3],
            'Name': ['A', 'B', 'C'],
            'Sex': ['male', 'female', 'male'],
            'Age': [22.0, 38.0, 26.0],
            'SibSp': [1, 1, 0],
            'Parch': [0, 0, 0],
            'Ticket': ['A/5 21171', 'PC 17599', 'STON/O2. 3101282'],
            'Fare': [7.25, 71.2833, 7.925],
            'Cabin': [None, 'C85', None],
            'Embarked': ['S', 'C', 'S']
        }
        df = pl.DataFrame(data)
        df = add_family_size(df)
        print(df.head())

Output:

shape: (5, 13)
┌─────────────┬──────────┬────────┬───────┬───┬───────┬───────┬──────────┬─────────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name  ┆ … ┆ Fare  ┆ Cabin ┆ Embarked ┆ family_size │
│ ---         ┆ ---      ┆ ---    ┆ ---   ┆   ┆ ---   ┆ ---   ┆ ---      ┆ ---         │
│ i64         ┆ i64      ┆ i64    ┆ str   ┆   ┆ f64   ┆ str   ┆ str      ┆ i64         │
╞═════════════╪══════════╪════════╪═══════╪═══╪═══════╪═══════╪══════════╪═════════════╡
│ 1           ┆ 0        ┆ 3      ┆ test1 ┆ … ┆ 7.25  ┆ NaN   ┆ S        ┆ 2           │
│ 2           ┆ 1        ┆ 1      ┆ test2 ┆ … ┆ 71.28 ┆ C85   ┆ C        ┆ 1           │
│ 3           ┆ 0        ┆ 2      ┆ test3 ┆ … ┆ 7.92  ┆ NaN   ┆ S        ┆ 1           │
│ 4           ┆ 1        ┆ 1      ┆ test4 ┆ … ┆ 53.1  ┆ C123  ┆ S        ┆ 1           │
│ 5           ┆ 0        ┆ 3      ┆ test5 ┆ … ┆ null  ┆ NaN   ┆ S        ┆ 2           │
└─────────────┴──────────┴────────┴───────┴───┴───────┴───────┴──────────┴─────────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size")
)

Status: ✓ Success

add_constant_column

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Add a new column 'status' with the constant value "passenger" for all rows using pl.lit()
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of using pl.lit: df.with_columns(pl.lit("value").alias("new_col"))

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.col("*"), pl.lit("passenger").alias("status"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpe1ubf2ql.py", line 6, in 
    df = df.with_column(pl.col("*"), pl.lit("passenger").alias("status"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.lit("passenger").alias("status"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpn6pbrncn.py", line 6, in 
    df = df.with_column(pl.lit("passenger").alias("status"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.lit("passenger").alias("status")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# First load the data using Polars as follows
df = pl.read_csv("data/titanic.csv")

# Add a new column 'status' with the constant value "passenger" for all rows using pl.lit()
df = df.with_columns(
    pl.lit("passenger").alias("status")
)

# Optional: Print the DataFrame schema and head to verify
# print(df.schema)
# print(df.head())

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# First load the data using Polars as follows
# Create a dummy csv file for demonstration if it doesn't exist
try:
    df = pl.read_csv("data/titanic.csv")
except FileNotFoundError:
    print("data/titanic.csv not found. Creating a dummy DataFrame for demonstration.")
    # Create a dummy DataFrame if the file is not found
    data = {
        "PassengerId": [1, 2, 3],
        "Survived": [0, 1, 1],
        "Pclass": [3, 1, 3],
        "Name": ["Braund, Mr. Owen Harris", "Cumings, Mrs. John Bradley (Florence Briggs Thayer)", "Heikkinen, Miss. Laina"],
        "Sex": ["male", "female", "female"],
        "Age": [22.0, 38.0, 26.0],
        "SibSp": [1, 1, 0],
        "Parch": [0, 0, 0],
        "Ticket": ["A/5 21171", "PC 17599", "STON/O2. 3101282"],
        "Fare": [7.25, 71.2833, 7.925],
        "Cabin": [None, "C85", None],
        "Embarked": ["S", "C", "S"]
    }
    df = pl.DataFrame(data)


# Add a new column 'status' with the constant value "passenger" for all rows using pl.lit()
df = df.with_columns(pl.lit("passenger").alias("status"))

# Optional: Print the DataFrame schema and head to verify
# print(df.schema)
# print(df.head())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Assume df is already loaded:
# df = pl.read_csv("data/titanic.csv")

# Add a new column 'status' with the constant value "passenger" for all rows
df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_column(pl.lit("passenger").alias("status"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpoj371pxi.py", line 3, in 
    df = df.with_column(pl.lit("passenger").alias("status"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.lit("passenger").alias("status"))

Status: ✓ Success

apply_user_function

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Apply a custom function to calculate BMI (weight/height^2) for each passenger
# Use Age as height and Fare as weight for this example
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of applying a custom function:
# df.with_columns(pl.col("value").map_elements(lambda x: x*2,return_dtype=pl.Float64).alias("double"))

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_bmi(height, weight):
    if height <= 0 or weight <= 0:
        return None
    return weight / (height ** 2)

df = pl.read_csv("data/titanic.csv")

df = df.with_columns([
    pl.struct(['Age', 'Fare'])
    .map_elements(lambda x: calculate_bmi(x['Age'], x['Fare']))
    .alias('BMI')
])

Error:

sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpyg7pwl7a.py", line 12, in 
    df = df.with_columns([
         ^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: TypeError: '<=' not supported between instances of 'NoneType' and 'int'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

def calculate_bmi(height, weight):
    if height is None or weight is None or height == 0:
        return None
    return weight / (height ** 2)

df = df.with_columns(
    pl.struct(['Age', 'Fare'])
    .map_elements(lambda x: calculate_bmi(x['Age'], x['Fare']))
    .alias('BMI')
)

Error:

sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("Age") * pl.col("Age"))).alias("BMI")
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load data
df = pl.read_csv("data/titanic.csv")

# Define BMI calculation function
def calculate_bmi(age, fare):
    if age is None or fare is None:
        return None
    # Using Age as height and Fare as weight
    return fare / (age ** 2) if age > 0 else None

# Apply custom function to calculate BMI
df = df.with_columns(
    pl.struct(["Age", "Fare"])
    .map_elements(lambda x: calculate_bmi(x["Age"], x["Fare"]))
    .alias("BMI")
)

# Display result
print(df.head())

Output:

shape: (5, 13)
┌─────────────┬──────────┬────────┬────────────────────┬───┬─────────┬───────┬──────────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name               ┆ … ┆ Fare    ┆ Cabin ┆ Embarked ┆ BMI      │
│ ---         ┆ ---      ┆ ---    ┆ ---                ┆   ┆ ---     ┆ ---   ┆ ---      ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str                ┆   ┆ f64     ┆ str   ┆ str      ┆ f64      │
╞═════════════╪══════════╪════════╪════════════════════╪═══╪═════════╪═══════╪══════════╪══════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr. Owen   ┆ … ┆ 7.25    ┆ null  ┆ S        ┆ 0.014979 │
│             ┆          ┆        ┆ Harris             ┆   ┆         ┆       ┆          ┆          │
│ 2           ┆ 1        ┆ 1      ┆ Cumings, Mrs. John ┆ … ┆ 71.2833 ┆ C85   ┆ C        ┆ 0.049365 │
│             ┆          ┆        ┆ Bradley (Fl…       ┆   ┆         ┆       ┆          ┆          │
│ 3           ┆ 1        ┆ 3      ┆ Heikkinen, Miss.   ┆ … ┆ 7.925   ┆ null  ┆ S        ┆ 0.011723 │
│             ┆          ┆        ┆ Laina              ┆   ┆         ┆       ┆          ┆          │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle, Mrs.     ┆ … ┆ 53.1    ┆ C123  ┆ S        ┆ 0.043347 │
│             ┆          ┆        ┆ Jacques Heath (…   ┆   ┆         ┆       ┆          ┆          │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr. William ┆ … ┆ 8.05    ┆ null  ┆ S        ┆ 0.006571 │
│             ┆          ┆        ┆ Henry              ┆   ┆         ┆       ┆          ┆          │
└─────────────┴──────────┴────────┴────────────────────┴───┴─────────┴───────┴──────────┴──────────┘

Error:

sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Define a function to calculate BMI using Age as height and Fare as weight
def calculate_bmi(row):
    # Check if Age and Fare are not null
    if row["Age"] is not None and row["Fare"] is not None and row["Age"] > 0:
        return row["Fare"] / (row["Age"] ** 2)
    else:
        return None

# Apply the custom function to calculate BMI
df = df.with_columns(
    pl.struct(["Age", "Fare"])
    .map_elements(calculate_bmi, return_dtype=pl.Float64)
    .alias("BMI")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Calculate BMI using Age as height and Fare as weight
df = df.with_columns(
    (pl.col("Fare") / (pl.col("Age") ** 2)).alias("BMI")
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
def calculate_bmi(row):
    height = row['Age']
    weight = row['Fare']
    if height and weight:
        return weight / (height**2)
    return None

df = df.with_columns(
    pl.struct(['Age', 'Fare']).apply(calculate_bmi).alias('BMI')
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpimhm5ias.py", line 11, in 
    pl.struct(['Age', 'Fare']).apply(calculate_bmi).alias('BMI')
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'apply'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    pl.struct(["Age", "Fare"]).map_elements(
        lambda x: x["Fare"] / (x["Age"]**2) if x["Age"] != 0 else None,
        return_dtype=pl.Float64
    ).alias("BMI")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp92vyvm3u.py", line 3, in 
    df = df.with_columns(
         ^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: TypeError: unsupported operand type(s) for ** or pow(): 'NoneType' and 'int'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Calculate BMI
df = df.with_columns(
    (pl.col("Fare") / (pl.col("Age") ** 2)).alias("BMI")
)

df

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

def calculate_bmi(row):
    try:
        height = row["Age"]
        weight = row["Fare"]
        bmi = weight / (height ** 2)
        return bmi
    except (TypeError, ValueError):
        return None

df = df.with_columns(pl.struct(["Age", "Fare"]).map_elements(calculate_bmi).alias("BMI"))

Error:

sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns((pl.col("Fare") / (pl.col("Age")**2)).alias("BMI"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (df["Fare"] / (df["Age"] * df["Age"])).alias("BMI")
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

def calculate_bmi(age, fare):
    return fare / (age ** 2)

df = df.with_column(pl col("Age").arr.pow(2).alias("height_squared"))
df = df.with_column(pl.col("Fare") / pl.col("height_squared")).alias("BMI"))

Error:

unmatched ')' (, line 12)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")

def calculate_bmi(age, fare):
    return fare / (age ** 2)

df = df.with_columns(pl.struct(pl.col("Age"), pl.col("Fare")).map(lambda x: calculate_bmi(x["Age"], x["Fare"])).alias("BMI"))

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

def calc_bmi(age, fare):
    return fare / (age ** 2)

df = df.with_columns(pl.col("Age").apply(lambda x: calc_bmi(x, df["Fare"])).alias("BMI"))

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("Age") ** 2)).alias("BMI")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("Age") ** 2).alias("BMI")
)

Error:

'(' was never closed (, line 6)

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("Age") ** 2)).alias("BMI")
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_bmi(height, weight):
  """Calculates BMI given height and weight."""
  if height <= 0 or weight <= 0:
    return None
  return weight / (height ** 2)

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.struct(["Age", "Fare"])
    .map(lambda s: calculate_bmi(s["Age"], s["Fare"]))
    .alias("BMI")
)

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp2b3x8e57.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_bmi(row):
    """Calculates BMI using Age as height and Fare as weight."""
    age = row["Age"]
    fare = row["Fare"]
    if age > 0:  # Avoid division by zero or negative values.  Ensure age is plausible.
        bmi = fare / (age ** 2)
        return bmi
    else:
        return None  # Or some appropriate missing value indicator


df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.struct(["Age", "Fare"]).map_elements(
        lambda x: calculate_bmi(x), return_dtype=pl.Float64
    ).alias("BMI")
)

print(df.head())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpq7d9lgua.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

# Creating DataFrames
df = pl.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
#df = pl.read_csv("file.csv",try_parse_dates=True) #commented out since no csv file available in context
# Selection
df.select("a", "b")            # Select columns
df.select(pl.all())                # Select all columns
# Filtering
df.filter(pl.col("a") > 1)            # Basic comparison
df.filter(pl.col("b").is_in(["x", "y"]))    # Is in list
df.filter(pl.col("a").is_between(1, 2))     # Range check
#df.filter(pl.col("a").is_null())          # Check for nulls #commented out since no null values in example
# AND and OR conditions
df.filter((pl.col("a") > 1) & (pl.col("a") < 3))   # AND condition
df.filter((pl.col("a") > 1) | (pl.col("a") < 3))   # OR condition
df.filter(~(pl.col("a") > 1))             # NOT condition
# Expression Arithmetic
df.with_columns(
(pl.col("a") + pl.col("a")).alias("sum"),     # Addition
(pl.col("a") - pl.col("a")).alias("diff"),     # Subtraction
(pl.col("a") * pl.col("a")).alias("product"),   # Multiplication
(pl.col("a") / pl.col("a")).alias("division")   # Division
)
# Column Operations
df.with_columns(pl.lit("constant").alias("new_col")) # Add constant column
df.rename({"a": "new_name"})          # Rename columns
df.drop("b")             # Drop columns
# Sorting
df.sort("new_name", descending=True)          # Sort by column
df.sort(["new_name", "new_col"], descending=[True, False])  # Multi-column sort
# Aggregation and Grouping
df.select(pl.col("new_name").sum())           # Sum values
df.select(pl.col("new_name").mean())           # Calculate mean
df.select(pl.col("new_name").len())          # Count values
df.select(pl.col("new_name").n_unique())         # Count unique values
# GroupBy operations
#df.group_by("category").agg(pl.col("value").mean())  # Group and aggregate #commented out since no category in exmaple
#df.group_by("category").agg(        # Group and aggregate #commented out since no category in example
#pl.col("value").mean().name.suffix("_mean"),#commented out since no category in example
#pl.col("value").min().name.suffix("_min"))#commented out since no category in example
#df.group_by(["cat1", "cat2"]).agg(          # Multiple aggregations #commented out since no category in example
#pl.col("val1").sum(), #commented out since no category in example
#pl.col("val2").mean()#commented out since no category in example
#)
# Reshaping Data
#df.pivot(values="value", index="row_var", on="col_var") # Pivot table#commented out since no value/row_var/col_var in example
#df.collect().pivot(values="value", index="row_var", on="col_var", aggregate_function="mean") # Pivot lazyframe#commented out since no value/row_var/col_var in example
#df.unpivot(index=["id"], on=["val1", "val2"])     # Unpivot/melt#commented out since no id/val1/val2 in example
#df.explode("list_column")                   # Explode lists#commented out since no list_column in example
df.with_row_index("index")                  # Add row indices
# String Operations
#df.select(pl.col("text").str.len_chars())           # String length#commented out since no text column
#df.select(pl.col("text").str.contains("pattern"))       # Contains pattern#commented out since no text column
#df.select(pl.col("text").str.replace("old", "new"))      # Replace text#commented out since no text column
#df.select(pl.col("text").str.to_lowercase())         # Convert case#commented out since no text column
#df.select(pl.col("text").str.strip())             # Remove whitespace#commented out since no text column
#df.select(pl.col("text").str.split(" "))           # Split strings#commented out since no text column
# Window Functions
df.with_columns(
pl.col("new_name").sum().over("new_col")            # Sum by group
)
# Working with Missing Values
#df.drop_nulls()                        # Drop null rows #commented out since no nulls in example
#df.drop_nulls(subset=["column1", "column2"])         # Drop for specific columns#commented out since no columns in example
#df.fill_null(strategy="forward")               # Forward fill#commented out since no nulls in example
#df.with_columns(
#pl.col("value").fill_null(pl.col("value").mean())     # Fill with mean#commented out since no nulls in example
#)
#df.with_columns(
#pl.when(pl.col("value").is_null())
#.then(pl.lit(0))
#.otherwise(pl.col("value"))
#.alias("value")                      # Replace nulls conditionally
#)
# Data Type Operations
df.select(pl.col("new_name").cast(pl.Int64))             # Cast to type
#df.select(pl.col("date").dt.year())              # Extract year#commented out since no date column
#df.select(pl.col("date").dt.month())             # Extract month#commented out since no date column
#df.select(pl.col("date").dt.strftime("%Y-%m-%d"))       # Format date#commented out since no date column
df.schema                           # Get schema
# Advanced Features
# LazyFrame for optimized execution
lazy_df = df.lazy()
result = lazy_df.filter(pl.col("new_name") > 0).collect()
# Joins
#df1.join(df2, on="key", how="inner")             # Inner join#commented out since no df1/df2
#df1.join(df2, on="key", how="left")              # Left join#commented out since no df1/df2
#df1.join(df2, on=["key1", "key2"], how="full")        # Multiple join keys#commented out since no df1/df2
# Sampling
df.sample(n=3)                        # Sample n rows
df.sample(frac=0.1, with_replacement=True)          # Sample fraction
# User-defined functions
df.select(pl.col("new_name").map_elements(lambda x: x * 2,return_dtype=pl.Int64))     # Apply function
# Efficient operations
df.select([
pl.all(),                       # All columns
pl.exclude("new_name")             # Exclude column
])
df.with_columns(pl.exclude("new_name").name.prefix("new_"))   # Batch operations
# Expression contexts
(
df.lazy()
.filter(pl.col("new_name") > 0)
.with_columns(pl.lit(1).alias("constant"))
.collect()
)                              # Method chaining
# Date and Time Operations
#df.with_columns(
#pl.col("date").dt.date()                 # Extract date
#) #commented out since no date column
#df.with_columns(
#pl.col("date").dt.time()                 # Extract time
#) #commented out since no date column
pl.date_range(datetime(2023, 1, 1), datetime(2023, 3, 31), interval="1d", eager=True) # Create a date range Series

def calculate_bmi(age: float, fare: float) -> float:
    """Calculates Body Mass Index (BMI) given age and fare.
    Uses Age as "height" and Fare as "weight" for BMI calculation as requested.
    """
    if age <= 0 or fare <= 0:
        return None
    return fare / (age**2)

def calculate_bmi_expression():
    return pl.struct([pl.col("Age"), pl.col("Fare")]).map_elements(
        lambda s: calculate_bmi(s["Age"], s["Fare"]), return_dtype=pl.Float64
    ).alias("BMI")

# Load the data using Polars
#df = pl.read_csv("data/titanic.csv") #commented out since no csv file available

# Example dummy data representing the schema
data = {
    "PassengerId": [1, 2, 3, 4, 5],
    "Survived": [0, 1, 0, 1, 0],
    "Pclass": [3, 1, 3, 1, 3],
    "Name": ["Braund, Mr. Owen Harris", "Cumings, Mrs. John Bradley (Florence Briggs Thayer)", "Heikkinen, Miss. Laina", "Futrelle, Mrs. Jacques Heath (Lily May Peel)", "Allen, Mr. William Henry"],
    "Sex": ["male", "female", "female", "female", "male"],
    "Age": [22.0, 38.0, 26.0, 35.0, 35.0],
    "SibSp": [1, 1, 0, 1, 0],
    "Parch": [0, 0, 0, 0, 0],
    "Ticket": ["A/5 21171", "PC 17599", "STON/O2. 3101282", "113803", "373450"],
    "Fare": [7.25, 71.2833, 7.925, 53.1, 8.05],
    "Cabin": [None, "C85", None, "C123", None],
    "Embarked": ["S", "C", "S", "S", "S"]
}

df = pl.DataFrame(data)

# Apply the custom function
bmi_df = df.with_columns(calculate_bmi_expression())

# Print the resulting DataFrame with BMI
print(bmi_df.select(["Name", "Age", "Fare", "BMI"]))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpgs2g11nf.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

apply_user_function_with_multiple_columns

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Apply a custom function to calculate BMI (weight/height^2) for each passenger
# Use Age as height and Fare as weight for this example
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of applying a custom function:
# df.with_columns(pl.struct('col_1','col_2').map_elements(lambda x: func(x['col_1'], x['col_2']),return_dtype=pl.Float64).alias('col_3'))

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load data
df = pl.read_csv("data/titanic.csv")

# Define BMI calculation function
def calculate_bmi(age, fare):
    if age is None or fare is None:
        return None
    # Using Age as height and Fare as weight
    return fare / (age ** 2) if age > 0 else None

# Apply custom function to calculate BMI
df = df.with_columns(
    pl.struct(["Age", "Fare"])
    .map_elements(lambda x: calculate_bmi(x["Age"], x["Fare"]))
    .alias("BMI")
)

# Display result
print(df.head())

Output:

shape: (5, 13)
┌─────────────┬──────────┬────────┬────────────────────┬───┬─────────┬───────┬──────────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name               ┆ … ┆ Fare    ┆ Cabin ┆ Embarked ┆ BMI      │
│ ---         ┆ ---      ┆ ---    ┆ ---                ┆   ┆ ---     ┆ ---   ┆ ---      ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str                ┆   ┆ f64     ┆ str   ┆ str      ┆ f64      │
╞═════════════╪══════════╪════════╪════════════════════╪═══╪═════════╪═══════╪══════════╪══════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr. Owen   ┆ … ┆ 7.25    ┆ null  ┆ S        ┆ 0.014979 │
│             ┆          ┆        ┆ Harris             ┆   ┆         ┆       ┆          ┆          │
│ 2           ┆ 1        ┆ 1      ┆ Cumings, Mrs. John ┆ … ┆ 71.2833 ┆ C85   ┆ C        ┆ 0.049365 │
│             ┆          ┆        ┆ Bradley (Fl…       ┆   ┆         ┆       ┆          ┆          │
│ 3           ┆ 1        ┆ 3      ┆ Heikkinen, Miss.   ┆ … ┆ 7.925   ┆ null  ┆ S        ┆ 0.011723 │
│             ┆          ┆        ┆ Laina              ┆   ┆         ┆       ┆          ┆          │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle, Mrs.     ┆ … ┆ 53.1    ┆ C123  ┆ S        ┆ 0.043347 │
│             ┆          ┆        ┆ Jacques Heath (…   ┆   ┆         ┆       ┆          ┆          │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr. William ┆ … ┆ 8.05    ┆ null  ┆ S        ┆ 0.006571 │
│             ┆          ┆        ┆ Henry              ┆   ┆         ┆       ┆          ┆          │
└─────────────┴──────────┴────────┴────────────────────┴───┴─────────┴───────┴──────────┴──────────┘

Error:

sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

def calculate_bmi(row):
    # Use Age as height and Fare as weight
    # BMI = weight / height^2
    # Add null check to avoid division by zero
    if row['Age'] is None or row['Age'] == 0 or row['Fare'] is None:
        return None
    return row['Fare'] / (row['Age'] ** 2)

df = df.with_columns(
    pl.struct(['Age', 'Fare']).map_elements(
        lambda x: calculate_bmi(x), 
        return_dtype=pl.Float64
    ).alias('BMI')
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Calculate BMI using Age as height and Fare as weight
df = df.with_columns(
    (pl.col("Fare") / (pl.col("Age") ** 2)).alias("BMI")
)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("Age") ** 2)).alias("BMI")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.struct('Age', 'Fare').map_elements(
        lambda x: x['Fare'] / (x['Age'] ** 2) if x['Age'] != 0 else None,
        return_dtype=pl.Float64
    ).alias('BMI')
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpv2vffmhc.py", line 6, in 
    df = df.with_columns(
         ^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: TypeError: unsupported operand type(s) for ** or pow(): 'NoneType' and 'int'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("Age") ** 2)).alias("BMI")
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
def calculate_bmi(row):
    height = row['Age']
    weight = row['Fare']
    if height and weight:
        return weight / (height**2)
    return None

df = df.with_columns(
    pl.struct(['Age', 'Fare']).apply(calculate_bmi).alias('BMI')
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp6zhrwrnh.py", line 11, in 
    pl.struct(['Age', 'Fare']).apply(calculate_bmi).alias('BMI')
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'apply'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
# Apply BMI calculation using custom function on Age and Fare
df_with_bmi = df.with_columns(
    pl.struct('Fare', 'Age').map_elements(
        lambda x: x['Fare'] / (x['Age'] ** 2) if x['Age'] != 0 else None, 
        return_dtype=pl.Float64
    ).alias('BMI')
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmphu69h7q8.py", line 4, in 
    df_with_bmi = df.with_columns(
                  ^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: TypeError: unsupported operand type(s) for ** or pow(): 'NoneType' and 'int'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Calculate BMI
df = df.with_columns(
    (pl.col("Fare") / (pl.col("Age") ** 2)).alias("BMI")
)

df

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_bmi(height, weight):
    if height <= 0 or weight <= 0:
        return None
    return weight / (height ** 2)

df = pl.read_csv("data/titanic.csv")

df = df.with_columns([
    pl.struct(['Age', 'Fare'])
    .map_elements(lambda x: calculate_bmi(x['Age'], x['Fare']))
    .alias('BMI')
])

Error:

sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpui0ykutr.py", line 12, in 
    df = df.with_columns([
         ^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: TypeError: '<=' not supported between instances of 'NoneType' and 'int'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_bmi(age, fare):
    if age is None or fare is None:
        return None
    return fare / (age ** 2)

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.struct(['Age', 'Fare']).map_elements(
        lambda x: calculate_bmi(x['Age'], x['Fare']),
        return_dtype=pl.Float64
    ).alias('BMI')
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("Age") * pl.col("Age"))).alias("BMI")
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_bmi(height, weight):
  """Calculates BMI given height and weight."""
  if height <= 0 or weight <= 0:
    return None
  return weight / (height ** 2)

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.struct(["Age", "Fare"])
    .map(lambda s: calculate_bmi(s["Age"], s["Fare"]))
    .alias("BMI")
)

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpt8kqi2s5.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_bmi(age: float, fare: float) -> float:
    """
    Calculates BMI using age as height and fare as weight.
    """
    if isinstance(age, float) and isinstance(fare, float):
        if age > 0:  # Avoid division by zero
            return fare / (age * age)
        else:
            return float('nan')
    else:
        return float('nan')

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.struct('Age', 'Fare')
    .map_elements(lambda x: calculate_bmi(x['Age'], x['Fare']), return_dtype=pl.Float64)
    .alias('BMI')
)

print(df.head())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_ukfxjxm.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

# Creating DataFrames
df = pl.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
#df = pl.read_csv("file.csv",try_parse_dates=True) #commented out since no csv file available in context
# Selection
df.select("a", "b")            # Select columns
df.select(pl.all())                # Select all columns
# Filtering
df.filter(pl.col("a") > 1)            # Basic comparison
df.filter(pl.col("b").is_in(["x", "y"]))    # Is in list
df.filter(pl.col("a").is_between(1, 2))     # Range check
#df.filter(pl.col("a").is_null())          # Check for nulls #commented out since no null values in example
# AND and OR conditions
df.filter((pl.col("a") > 1) & (pl.col("a") < 3))   # AND condition
df.filter((pl.col("a") > 1) | (pl.col("a") < 3))   # OR condition
df.filter(~(pl.col("a") > 1))             # NOT condition
# Expression Arithmetic
df.with_columns(
(pl.col("a") + pl.col("a")).alias("sum"),     # Addition
(pl.col("a") - pl.col("a")).alias("diff"),     # Subtraction
(pl.col("a") * pl.col("a")).alias("product"),   # Multiplication
(pl.col("a") / pl.col("a")).alias("division")   # Division
)
# Column Operations
df.with_columns(pl.lit("constant").alias("new_col")) # Add constant column
df.rename({"a": "new_name"})          # Rename columns
df.drop("b")             # Drop columns
# Sorting
df.sort("new_name", descending=True)          # Sort by column
df.sort(["new_name", "new_col"], descending=[True, False])  # Multi-column sort
# Aggregation and Grouping
df.select(pl.col("new_name").sum())           # Sum values
df.select(pl.col("new_name").mean())           # Calculate mean
df.select(pl.col("new_name").len())          # Count values
df.select(pl.col("new_name").n_unique())         # Count unique values
# GroupBy operations
#df.group_by("category").agg(pl.col("value").mean())  # Group and aggregate #commented out since no category in exmaple
#df.group_by("category").agg(        # Group and aggregate #commented out since no category in example
#pl.col("value").mean().name.suffix("_mean"),#commented out since no category in example
#pl.col("value").min().name.suffix("_min"))#commented out since no category in example
#df.group_by(["cat1", "cat2"]).agg(          # Multiple aggregations #commented out since no category in example
#pl.col("val1").sum(), #commented out since no category in example
#pl.col("val2").mean()#commented out since no category in example
#)
# Reshaping Data
#df.pivot(values="value", index="row_var", on="col_var") # Pivot table#commented out since no value/row_var/col_var in example
#df.collect().pivot(values="value", index="row_var", on="col_var", aggregate_function="mean") # Pivot lazyframe#commented out since no value/row_var/col_var in example
#df.unpivot(index=["id"], on=["val1", "val2"])     # Unpivot/melt#commented out since no id/val1/val2 in example
#df.explode("list_column")                   # Explode lists#commented out since no list_column in example
df.with_row_index("index")                  # Add row indices
# String Operations
#df.select(pl.col("text").str.len_chars())           # String length#commented out since no text column
#df.select(pl.col("text").str.contains("pattern"))       # Contains pattern#commented out since no text column
#df.select(pl.col("text").str.replace("old", "new"))      # Replace text#commented out since no text column
#df.select(pl.col("text").str.to_lowercase())         # Convert case#commented out since no text column
#df.select(pl.col("text").str.strip())             # Remove whitespace#commented out since no text column
#df.select(pl.col("text").str.split(" "))           # Split strings#commented out since no text column
# Window Functions
df.with_columns(
pl.col("new_name").sum().over("new_col")            # Sum by group
)
# Working with Missing Values
#df.drop_nulls()                        # Drop null rows #commented out since no nulls in example
#df.drop_nulls(subset=["column1", "column2"])         # Drop for specific columns#commented out since no columns in example
#df.fill_null(strategy="forward")               # Forward fill#commented out since no nulls in example
#df.with_columns(
#pl.col("value").fill_null(pl.col("value").mean())     # Fill with mean#commented out since no nulls in example
#)
#df.with_columns(
#pl.when(pl.col("value").is_null())
#.then(pl.lit(0))
#.otherwise(pl.col("value"))
#.alias("value")                      # Replace nulls conditionally
#)
# Data Type Operations
df.select(pl.col("new_name").cast(pl.Int64))             # Cast to type
#df.select(pl.col("date").dt.year())              # Extract year#commented out since no date column
#df.select(pl.col("date").dt.month())             # Extract month#commented out since no date column
#df.select(pl.col("date").dt.strftime("%Y-%m-%d"))       # Format date#commented out since no date column
df.schema                           # Get schema
# Advanced Features
# LazyFrame for optimized execution
lazy_df = df.lazy()
result = lazy_df.filter(pl.col("new_name") > 0).collect()
# Joins
#df1.join(df2, on="key", how="inner")             # Inner join#commented out since no df1/df2
#df1.join(df2, on="key", how="left")              # Left join#commented out since no df1/df2
#df1.join(df2, on=["key1", "key2"], how="full")        # Multiple join keys#commented out since no df1/df2
# Sampling
df.sample(n=3)                        # Sample n rows
df.sample(frac=0.1, with_replacement=True)          # Sample fraction
# User-defined functions
df.select(pl.col("new_name").map_elements(lambda x: x * 2,return_dtype=pl.Int64))     # Apply function
# Efficient operations
df.select([
pl.all(),                       # All columns
pl.exclude("new_name")             # Exclude column
])
df.with_columns(pl.exclude("new_name").name.prefix("new_"))   # Batch operations
# Expression contexts
(
df.lazy()
.filter(pl.col("new_name") > 0)
.with_columns(pl.lit(1).alias("constant"))
.collect()
)                              # Method chaining
# Date and Time Operations
#df.with_columns(
#pl.col("date").dt.date()                 # Extract date
#) #commented out since no date column
#df.with_columns(
#pl.col("date").dt.time()                 # Extract time
#) #commented out since no date column
pl.date_range(datetime(2023, 1, 1), datetime(2023, 3, 31), interval="1d", eager=True) # Create a date range Series

def calculate_bmi(age: float, fare: float) -> float:
    """Calculates Body Mass Index (BMI) given age and fare.
    Uses Age as "height" and Fare as "weight" for BMI calculation as requested.
    """
    if age <= 0 or fare <= 0:
        return None
    return fare / (age**2)

def calculate_bmi_expression():
    return pl.struct([pl.col("Age"), pl.col("Fare")]).map_elements(
        lambda s: calculate_bmi(s["Age"], s["Fare"]), return_dtype=pl.Float64
    ).alias("BMI")

# Load the data using Polars
#df = pl.read_csv("data/titanic.csv") #commented out since no csv file available

# Example dummy data representing the schema
data = {
    "PassengerId": [1, 2, 3, 4, 5],
    "Survived": [0, 1, 0, 1, 0],
    "Pclass": [3, 1, 3, 1, 3],
    "Name": ["Braund, Mr. Owen Harris", "Cumings, Mrs. John Bradley (Florence Briggs Thayer)", "Heikkinen, Miss. Laina", "Futrelle, Mrs. Jacques Heath (Lily May Peel)", "Allen, Mr. William Henry"],
    "Sex": ["male", "female", "female", "female", "male"],
    "Age": [22.0, 38.0, 26.0, 35.0, 35.0],
    "SibSp": [1, 1, 0, 1, 0],
    "Parch": [0, 0, 0, 0, 0],
    "Ticket": ["A/5 21171", "PC 17599", "STON/O2. 3101282", "113803", "373450"],
    "Fare": [7.25, 71.2833, 7.925, 53.1, 8.05],
    "Cabin": [None, "C85", None, "C123", None],
    "Embarked": ["S", "C", "S", "S", "S"]
}

df = pl.DataFrame(data)

# Apply the custom function
bmi_df = df.with_columns(calculate_bmi_expression())

# Print the resulting DataFrame with BMI
print(bmi_df.select(["Name", "Age", "Fare", "BMI"]))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpp51yops0.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

def calculate_bmi(row):
    try:
        height = row["Age"]
        weight = row["Fare"]
        bmi = weight / (height ** 2)
        return bmi
    except (TypeError, ValueError):
        return None

df = df.with_columns(pl.struct(["Age", "Fare"]).map_elements(calculate_bmi).alias("BMI"))

Error:

sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.
sys:1: MapWithoutReturnDtypeWarning: Calling `map_elements` without specifying `return_dtype` can lead to unpredictable results. Specify `return_dtype` to silence this warning.

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

def calculate_bmi(age, fare):
    if age is None or fare is None or age <= 0:
        return None
    return fare / (age ** 2)

df = df.with_columns(
    pl.struct('Age', 'Fare')
    .map_elements(lambda x: calculate_bmi(x['Age'], x['Fare']), return_dtype=pl.Float64)
    .alias('BMI')
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (df["Fare"] / (df["Age"] * df["Age"])).alias("BMI")
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

def calculate_bmi(age, fare):
    return fare / (age ** 2)

df = df.with_column(pl col("Age").arr.pow(2).alias("height_squared"))
df = df.with_column(pl.col("Fare") / pl.col("height_squared")).alias("BMI"))

Error:

unmatched ')' (, line 12)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

def calculate_bmi(weight, height):
    return weight / (height ** 2)

df = df.with_columns(pl.struct('Fare', 'Age').map_elements(lambda x: calculate_bmi(x['Fare'], x['Age']), return_dtype=pl.Float64).alias('BMI'))

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

def calc_bmi(age, fare):
    return fare / (age ** 2)

df = df.with_columns(pl.col("Age").apply(lambda x: calc_bmi(x, df["Fare"])).alias("BMI"))

Status: ✗ Failed

binning_values

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Create a new column 'fare_category' that bins Fare into categories:
# 'low' for fares <= 20
# 'medium' for fares > 20 and <= 50
# 'high' for fares > 50
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of value binning using when/then/otherwise:
# df.with_columns(
#     pl.when(pl.col("value") <= 10)
#     .then(pl.lit("low"))
#     .when(pl.col("value") <= 20)
#     .then(pl.lit("medium"))
#     .otherwise(pl.lit("high"))
#     .alias("category")
# )

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.when(pl.col('Fare') <= 20).then('low').when(pl.col('Fare') <= 50).then('medium').otherwise('high').alias('fare_category'))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpprj3dr5d.py", line 6, in 
    df = df.with_column(pl.when(pl.col('Fare') <= 20).then('low').when(pl.col('Fare') <= 50).then('medium').otherwise('high').alias('fare_category'))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
   .then(pl.lit("low"))
   .when(pl.col("Fare") <= 50)
   .then(pl.lit("medium"))
   .otherwise(pl.lit("high"))
   .alias("fare_category")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then("low")
    .when(pl.col("Fare") > 20)
    .when(pl.col("Fare") <= 50)
    .then("medium")
    .otherwise("high")
    .alias("fare_category")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpoa_6t214.py", line 10, in 
    .when(pl.col("Fare") <= 50)
     ^^^^
AttributeError: 'ChainedWhen' object has no attribute 'when'. Did you mean: 'then'?

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")

    df = df.with_columns(
        pl.when(pl.col("Fare") <= 20)
        .then(pl.lit("low"))
        .when((pl.col("Fare") > 20) & (pl.col("Fare") <= 50))
        .then(pl.lit("medium"))
        .otherwise(pl.lit("high"))
        .alias("fare_category")
    )
    return df

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpsn3oys73.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then(pl.lit("low"))
    .when(pl.col("Fare") <= 50)
    .then(pl.lit("medium"))
    .otherwise(pl.lit("high"))
    .alias("fare_category")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpegdj9e_y.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def categorize_fare(fare):
    if fare <= 20:
        return 'low'
    elif 20 < fare <= 50:
        return 'medium'
    else:
        return 'high'

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").map_elements(categorize_fare).alias("fare_category")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpl1whdrtd.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then(pl.lit("low"))
    .when(pl.col("Fare") > 20 & pl.col("Fare") <= 50)
    .then(pl.lit("medium"))
    .otherwise(pl.lit("high"))
    .alias("fare_category")
)

Error:

TypeError(msg)
TypeError: the truth value of an Expr is ambiguous

You probably got here by using a Python standard library function instead of the native expressions API.
Here are some things you might want to try:
- instead of `pl.col('a') and pl.col('b')`, use `pl.col('a') & pl.col('b')`
- instead of `pl.col('a') in [y, z]`, use `pl.col('a').is_in([y, z])`
- instead of `max(pl.col('a'), pl.col('b'))`, use `pl.max_horizontal(pl.col('a'), pl.col('b'))`

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then(pl.lit("low"))
    .when(pl.col("Fare") > 20)
    .when(pl.col("Fare") <= 50)
    .then(pl.lit("medium"))
    .otherwise(pl.lit("high"))
    .alias("fare_category")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmph9awqu4u.py", line 11, in 
    .when(pl.col("Fare") <= 50)
     ^^^^
AttributeError: 'ChainedWhen' object has no attribute 'when'. Did you mean: 'then'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then(pl.lit("low"))
    .when(pl.col("Fare") > 20 & pl.col("Fare") <= 50)
    .then(pl.lit("medium"))
    .otherwise(pl.lit("high"))
    .alias("fare_category")
)

Error:

TypeError(msg)
TypeError: the truth value of an Expr is ambiguous

You probably got here by using a Python standard library function instead of the native expressions API.
Here are some things you might want to try:
- instead of `pl.col('a') and pl.col('b')`, use `pl.col('a') & pl.col('b')`
- instead of `pl.col('a') in [y, z]`, use `pl.col('a').is_in([y, z])`
- instead of `max(pl.col('a'), pl.col('b'))`, use `pl.max_horizontal(pl.col('a'), pl.col('b'))`

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then("low")
    .when((pl.col("Fare") > 20) & (pl.col("Fare") <= 50))
    .then("medium")
    .otherwise("high")
    .alias("fare_category")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwty8gavy.py", line 7, in 
    df = df.with_columns(
         ^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: low

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'with_columns' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then(pl.lit("low"))
    .when((pl.col("Fare") > 20) & (pl.col("Fare") <= 50))
    .then(pl.lit("medium"))
    .otherwise(pl.lit("high"))
    .alias("fare_category")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then(pl.lit("low"))
    .when((pl.col("Fare") > 20) & (pl.col("Fare") <= 50))
    .then(pl.lit("medium"))
    .otherwise(pl.lit("high"))
    .alias("fare_category")
)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = df.with_columns([
    pl.when(pl.col("Fare") <= 20)
    .then("low")
    .when(pl.col("Fare") <= 50)
    .then("medium")
    .otherwise("high")
    .alias("fare_category")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpkaur3e_h.py", line 5, in 
    df = df.with_columns([
         ^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: low

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'with_columns' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then(pl.lit("low"))
    .when(pl.col("Fare") <= 50)
    .then(pl.lit("medium"))
    .otherwise(pl.lit("high"))
    .alias("fare_category")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then(pl.lit("low"))
    .when(pl.col("Fare") <= 50)
    .then(pl.lit("medium"))
    .otherwise(pl.lit("high"))
    .alias("fare_category")
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20).then("low")
    .when((pl.col("Fare") > 20) & (pl.col("Fare") <= 50)).then("medium")
    .otherwise("high")
    .alias("fare_category")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1vofzwrn.py", line 7, in 
    df = df.with_columns(
         ^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: low

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'with_columns' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then(pl.lit("low"))
    .when(pl.col("Fare") <= 50)
    .then(pl.lit("medium"))
    .otherwise(pl.lit("high"))
    .alias("fare_category")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then(pl.lit("low"))
    .when(pl.col("Fare") > 20) & (pl.col("Fare") <= 50)
    .then(pl.lit("medium"))
    .otherwise(pl.lit("high"))
    .alias("fare_category")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpw4fphz3t.py", line 11, in 
    .then(pl.lit("medium"))
     ^^^^
AttributeError: 'Expr' object has no attribute 'then'

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_column(
    pl.when(df["Fare"] <= 20).then("low")
    .when((df["Fare"] > 20) & (df["Fare"] <= 50)).then("medium")
    .otherwise("high")
    .alias("fare_category")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpca8mqz_l.py", line 3, in 
    df = df.with_column(
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20)
    .then(pl.lit("low"))
    .when(pl.col("Fare") <= 50)
    .then(pl.lit("medium"))
    .otherwise(pl.lit("high"))
    .alias("fare_category")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Fare") <= 20).then("low")
    .when(pl.col("Fare") <= 50).then("medium")
    .otherwise("high")
    .alias("fare_category")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmptoyn_qvk.py", line 7, in 
    df = df.with_columns(
         ^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: low

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'with_columns' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

calculate_rmse_by_id

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/train.csv")

# Calculate the Root Mean Square Error (RMSE) between actual values (y) and predictions (pred) for each id:
# 1. Group by unique_id
# 2. Calculate the squared difference between y and pred
# 3. Calculate the mean of the squared differences
# 4. Take the square root of the mean squared differences
# Available columns: unique_id, ds, y, pred

Suffix Prompt

# Here is an example of calculating a power:
# df.group_by("id").agg(
#     (pl.col("actual") - pl.col("predicted").pow(2)
# )

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/train.csv")

rmse = df.group_by("unique_id").agg(
    pl.sqrt(pl.mean((pl.col("y") - pl.col("pred")).pow(2))).alias("rmse")
)

Error:

AttributeError(msg)
AttributeError: module 'polars' has no attribute 'sqrt'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/train.csv")

rmse_by_id = df.group_by("unique_id").agg(
    ((pl.col("y") - pl.col("pred")).pow(2).mean().sqrt()).alias("rmse")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/train.csv")

rmse_df = df.group_by("unique_id").agg(
    ((pl.col("y") - pl.col("pred")).pow(2).mean().sqrt().alias("rmse")
)

Error:

'(' was never closed (, line 7)

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/train.csv")

rmse_by_id = (
    df
    .with_columns((pl.col("y") - pl.col("pred")).pow(2).alias("squared_diff"))
    .group_by("unique_id")
    .agg(
        pl.mean("squared_diff").pow(0.5).alias("rmse")
    )
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/train.csv")

rmse_by_id = df.group_by("unique_id").agg(
    pl.sqrt(pl.mean((pl.col("y") - pl.col("pred")).pow(2))).alias("rmse")
)

Error:

AttributeError(msg)
AttributeError: module 'polars' has no attribute 'sqrt'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/train.csv")

result = (
    df.group_by("unique_id")
    .agg(
        pl.sqrt(pl.mean((pl.col("y") - pl.col("pred"))**2)).alias("rmse")
    )
)

Error:

AttributeError(msg)
AttributeError: module 'polars' has no attribute 'sqrt'

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
rmse_by_id = (
    df.lazy()
    .groupby('unique_id')
    .agg(
        pl.mean((pl.col('y') - pl.col('pred'))**2).alias('mse')
    )
    .with_columns(
        pl.col('mse').sqrt().alias('rmse')
    )
    .drop('mse')
    .collect()
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmph3cnbt2h.py", line 5, in 
    .groupby('unique_id')
     ^^^^^^^
AttributeError: 'LazyFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

rmse = (
    df.group_by("unique_id")
    .agg(
        pl.sqrt(
            (pl.col("y") - pl.col("pred"))
            .pow(2)
            .mean()
        )
    )
    .sort("unique_id")
)

Error:

AttributeError(msg)
AttributeError: module 'polars' has no attribute 'sqrt'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/train.csv")

result = (
    df.group_by("unique_id")
    .agg(
        pl.sqrt(
            ((pl.col("y") - pl.col("pred")) ** 2).mean()
        ).alias("rmse")
    )
)

Error:

AttributeError(msg)
AttributeError: module 'polars' has no attribute 'sqrt'

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_rmse(df: pl.DataFrame) -> pl.DataFrame:
    """
    Calculates the Root Mean Square Error (RMSE) between actual values (y) and predictions (pred) for each id.

    Args:
        df: Polars DataFrame with columns 'unique_id', 'y', and 'pred'.

    Returns:
        Polars DataFrame with columns 'unique_id' and 'rmse'.
    """

    rmse_df = (
        df.group_by("unique_id")
        .agg(
            pl.sqrt(pl.mean((pl.col("y") - pl.col("pred"))**2)).alias("rmse")
        )
    )
    return rmse_df

if __name__ == '__main__':
    df = pl.read_csv("data/train.csv")
    rmse_df = calculate_rmse(df)
    print(rmse_df)

Error:

AttributeError(msg)
AttributeError: module 'polars' has no attribute 'sqrt'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_rmse(df: pl.DataFrame) -> pl.DataFrame:
    """
    Calculates the Root Mean Square Error (RMSE) between actual values (y) and predictions (pred) for each id.

    Args:
        df: Polars DataFrame with columns 'unique_id', 'ds', 'y', 'pred'.

    Returns:
        Polars DataFrame with columns 'unique_id' and 'rmse'.
    """

    rmse_df = (
        df.group_by("unique_id")
        .agg(
            pl.col("y")
            .sub(pl.col("pred"))
            .pow(2)
            .mean()
            .sqrt()
            .alias("rmse")
        )
    )

    return rmse_df

if __name__ == "__main__":
    df = pl.read_csv("data/train.csv")
    rmse_df = calculate_rmse(df)
    print(rmse_df)

Output:

shape: (2, 2)
┌───────────┬──────┐
│ unique_id ┆ rmse │
│ ---       ┆ ---  │
│ str       ┆ f64  │
╞═══════════╪══════╡
│ id2       ┆ 1.0  │
│ id1       ┆ 1.0  │
└───────────┴──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

# First load the data using Polars as follows
df = pl.read_csv("data/train.csv")

# Calculate the Root Mean Square Error (RMSE) between actual values (y) and predictions (pred) for each id:
# 1. Group by unique_id
# 2. Calculate the squared difference between y and pred
# 3. Calculate the mean of the squared differences
# 4. Take the square root of the mean squared differences
rmse_df = (
    df.group_by("unique_id")
    .agg(
        pl.col("y").sub(pl.col("pred")).pow(2).mean().alias("mse")
    )
    .with_columns(pl.col("mse").sqrt().alias("rmse"))
)

print(rmse_df)

Output:

shape: (2, 3)
┌───────────┬─────┬──────┐
│ unique_id ┆ mse ┆ rmse │
│ ---       ┆ --- ┆ ---  │
│ str       ┆ f64 ┆ f64  │
╞═══════════╪═════╪══════╡
│ id1       ┆ 1.0 ┆ 1.0  │
│ id2       ┆ 1.0 ┆ 1.0  │
└───────────┴─────┴──────┘

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

def calculate_rmse(df: pl.DataFrame) -> pl.DataFrame:
    """
    Calculates the Root Mean Square Error (RMSE) between actual values (y) and predictions (pred) for each id.

    Args:
        df: A Polars DataFrame with columns 'unique_id', 'ds', 'y', and 'pred'.

    Returns:
        A Polars DataFrame with 'unique_id' and 'rmse' columns.
    """

    rmse_df = df.group_by("unique_id").agg(
        (
            ((pl.col("y") - pl.col("pred")) ** 2).mean().sqrt()
        ).alias("rmse")
    )

    return rmse_df

if __name__ == '__main__':
    # Create a dummy DataFrame for testing
    data = {
        "unique_id": ["id1", "id1", "id2", "id2"],
        "ds": ["2023-01-01", "2023-01-02", "2023-01-01", "2023-01-02"],
        "y": [10, 12, 15, 18],
        "pred": [9, 13, 16, 17],
    }
    df = pl.DataFrame(data)

    # Calculate RMSE
    rmse_result = calculate_rmse(df)

    # Print the result
    print(rmse_result)

Output:

shape: (2, 2)
┌───────────┬──────┐
│ unique_id ┆ rmse │
│ ---       ┆ ---  │
│ str       ┆ f64  │
╞═══════════╪══════╡
│ id1       ┆ 1.0  │
│ id2       ┆ 1.0  │
└───────────┴──────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_rmse(df: pl.DataFrame) -> pl.DataFrame:
    """
    Calculates the Root Mean Square Error (RMSE) between actual values (y) and predictions (pred) for each id.

    Args:
        df (pl.DataFrame): Input DataFrame with columns 'unique_id', 'ds', 'y', 'pred'.

    Returns:
        pl.DataFrame: DataFrame containing 'unique_id' and 'rmse' columns.
    """

    rmse_df = df.group_by("unique_id").agg(
        [
            ((pl.col("y") - pl.col("pred")) ** 2).mean().sqrt().alias("rmse")
        ]
    )

    return rmse_df

if __name__ == '__main__':
    # Create a dummy DataFrame for testing
    data = {
        'unique_id': ['A', 'A', 'B', 'B', 'C', 'C'],
        'ds': ['2023-01-01', '2023-01-02', '2023-01-01', '2023-01-02', '2023-01-01', '2023-01-02'],
        'y': [10, 12, 15, 18, 20, 22],
        'pred': [9, 13, 16, 17, 19, 23]
    }
    df = pl.DataFrame(data)

    # Calculate RMSE
    rmse_results = calculate_rmse(df)

    # Print the results
    print(rmse_results)

Output:

shape: (3, 2)
┌───────────┬──────┐
│ unique_id ┆ rmse │
│ ---       ┆ ---  │
│ str       ┆ f64  │
╞═══════════╪══════╡
│ C         ┆ 1.0  │
│ A         ┆ 1.0  │
│ B         ┆ 1.0  │
└───────────┴──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

def calculate_rmse(df: pl.DataFrame) -> pl.DataFrame:
    """
    Calculates the Root Mean Square Error (RMSE) between actual values (y) and predictions (pred) for each id.

    Args:
        df (pl.DataFrame): Input DataFrame containing columns 'unique_id', 'y', and 'pred'.

    Returns:
        pl.DataFrame: DataFrame containing 'unique_id' and the calculated 'rmse' for each unique id.
    """
    rmse_df = (
        df.group_by("unique_id")
        .agg(
            [
                ((pl.col("y") - pl.col("pred")) ** 2).mean().sqrt().alias("rmse")
            ]
        )
    )
    return rmse_df

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import numpy as np

df = pl.read_csv("data/train.csv")

rmse_per_id = (
    df
   .groupby("unique_id")
   .agg([
        (pl.col("y") - pl.col("pred")).pow(2).mean().alias("mse"),
    ])
   .with_column(pl.col("mse").pow(0.5).alias("rmse"))
   .select("unique_id", "rmse")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpzm4iflzk.py", line 11, in 
    .groupby("unique_id")
     ^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import numpy as np

df = pl.read_csv("data/train.csv")

rmse_df = df.groupby("unique_id").agg([
    ((pl.col("y") - pl.col("pred")) ** 2).mean().alias("mse"),
]).select(pl.col("mse").apply(np.sqrt).alias("rmse"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpi5tl_ugu.py", line 9, in 
    rmse_df = df.groupby("unique_id").agg([
              ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/train.csv")

rmse = df.groupby("unique_id").agg(pl.col("y").arr.sub(pl.col("pred")).pow(2).mean().pow(0.5).alias("rmse"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp46zk28ir.py", line 8, in 
    rmse = df.groupby("unique_id").agg(pl.col("y").arr.sub(pl.col("pred")).pow(2).mean().pow(0.5).alias("rmse"))
           ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from math import sqrt

df = pl.read_csv("data/train.csv")

rmse_df = (
    df
    .with_columns((pl.col("y") - pl.col("pred")).pow(2).alias("squared_error"))
    .groupby("unique_id")
    .agg(pl.col("squared_error").mean().map_elements(sqrt).alias("rmse"))
)

print(rmse_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpjzt52970.py", line 11, in 
    .groupby("unique_id")
     ^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/train.csv")

rmse = (
    df.groupby("unique_id")
    .agg(
        (pl.col("y") - pl.col("pred")).pow(2).mean().sqrt().alias("rmse")
    )
)

print(rmse)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfxna6tge.py", line 7, in 
    df.groupby("unique_id")
    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/train.csv")

rmse_df = (
    df.with_columns(((pl.col("y") - pl.col("pred")) ** 2).alias("squared_diff"))
    .group_by("unique_id")
    .agg((pl.col("squared_diff").mean().sqrt()).alias("rmse"))
)

print(rmse_df)

Output:

shape: (2, 2)
┌───────────┬──────┐
│ unique_id ┆ rmse │
│ ---       ┆ ---  │
│ str       ┆ f64  │
╞═══════════╪══════╡
│ id2       ┆ 1.0  │
│ id1       ┆ 1.0  │
└───────────┴──────┘

Status: ✓ Success

categorical_encoding

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Create a new column 'sex_code' that encodes Sex as numeric (0 for 'male', 1 for 'female')
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of categorical encoding: df.with_columns((pl.when(pl.col("category") == "value").then(pl.lit(1)).otherwise(pl.lit(0)).alias("encoded"))

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns([
    pl.col("Sex").map_dict({
        "male": 0,
        "female": 1
    }).alias("sex_code")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp276l9_r9.py", line 7, in 
    pl.col("Sex").map_dict({
    ^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'map_dict'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.when(pl.col("Sex") == "female").then(pl.lit(1)).otherwise(pl.lit(0)).alias("sex_code"))
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Sex") == "male")
    .then(pl.lit(0))
    .otherwise(pl.lit(1))
    .alias("sex_code")
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def transform_data(df: pl.DataFrame) -> pl.DataFrame:
    """
    Transforms the input DataFrame by creating a new column 'sex_code'
    that encodes Sex as numeric (0 for 'male', 1 for 'female').

    Args:
        df: Input Polars DataFrame.

    Returns:
        Transformed Polars DataFrame with the 'sex_code' column.
    """
    df = df.with_columns(
        pl.when(pl.col("Sex") == "male")
        .then(0)
        .otherwise(1)
        .alias("sex_code")
    )
    return df

if __name__ == '__main__':
    df = pl.read_csv("data/titanic.csv")
    transformed_df = transform_data(df)
    print(transformed_df.head())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpe3e51xoj.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Sex") == "male")
    .then(pl.lit(0))
    .otherwise(pl.lit(1))
    .alias("sex_code")
)

print(df.head())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp4gnm679a.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Sex") == "male")
    .then(pl.lit(0))
    .otherwise(pl.lit(1))
    .alias("sex_code")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp7vfd6z80.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(df["Sex"] == "male").then(0).otherwise(1).alias("sex_code")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (
        pl.when(pl.col("Sex") == "male").then(pl.lit(0)).otherwise(pl.lit(1)).alias("sex_code")
    )
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Sex") == "male")
    .then(pl.lit(0))
    .when(pl.col("Sex") == "female")
    .then(pl.lit(1))
    .otherwise(pl.lit(None))
    .alias("sex_code")
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load data
df = pl.read_csv("data/titanic.csv")

# Create sex_code column (0 for male, 1 for female)
df = df.with_column(
    pl.when(pl.col("Sex") == "male").then(0).otherwise(1).alias("sex_code")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9pj1mzdh.py", line 9, in 
    df = df.with_column(
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Create a new column 'sex_code' that encodes Sex as numeric (0 for 'male', 1 for 'female')
df = df.with_columns(
    (pl.when(pl.col("Sex") == "female").then(pl.lit(1)).otherwise(pl.lit(0)).alias("sex_code"))
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Sex") == "female")
    .then(pl.lit(1))
    .otherwise(pl.lit(0))
    .alias("sex_code")
)

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.when(pl.col("Sex") == "male").then(0).otherwise(1).alias("sex_code"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpjhb8zy42.py", line 7, in 
    df = df.with_column(pl.when(pl.col("Sex") == "male").then(0).otherwise(1).alias("sex_code"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns([
    (pl.when(pl.col("Sex") == "male").then(pl.lit(0)).otherwise(pl.lit(1)).alias("sex_code"))
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.when(pl.col("Sex") == "male").then(pl.lit(0)).otherwise(pl.lit(1)).alias("sex_code"))

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.when(pl.col("Sex") == "male").then(0).otherwise(1).alias("sex_code"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns((pl.when(pl.col("Sex") == "female").then(pl.lit(1)).otherwise(pl.lit(0)).alias("sex_code")))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Sex") == "male")
    .then(0)
    .otherwise(1)
    .alias("sex_code")
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_column((df['Sex'] == 'female').cast(pl.Int8).alias('sex_code'))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfm99izs0.py", line 3, in 
    df = df.with_column((df['Sex'] == 'female').cast(pl.Int8).alias('sex_code'))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    (pl.when(pl.col("Sex") == "male").then(pl.lit(0)).otherwise(pl.lit(1)).alias("sex_code"))
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.when(pl.col("Sex") == "male")
    .then(pl.lit(0))
    .otherwise(pl.lit(1))
    .alias("sex_code")
)

Status: ✓ Success

coalesce_null_values

Original Prompt

# First create a DataFrame with multiple columns containing null values:
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

# Fill null values by checking value1 first, then value2, then value3
# Create a new column called 'combined_value'

Suffix Prompt

# Here is an example of using coalesce: df.with_columns(pl.coalesce(["col1", "col2", "col3"]).alias("combined"))

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import numpy as np

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df["combined_value"] = df.select(pl.coalesce("value1", "value2", "value3"))

Error:

TypeError(msg)
TypeError: DataFrame object does not support `Series` assignment by index

Use `DataFrame.with_columns`.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(pl.coalesce("value1", "value2", "value3").alias("combined_value"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code for the task:

import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.when(pl.col("value1").is_not_null())
   .then(pl.col("value1"))
   .when(pl.col("value2").is_not_null())
   .then(pl.col("value2"))
   .otherwise(pl.col("value3"))
   .alias("combined_value")
)

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.coalesce([
        pl.col("value1"),
        pl.col("value2"),
        pl.col("value3")
    ]).alias("combined_value")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df_with_combined = df.with_columns(
    pl.coalesce(["value1", "value2", "value3"]).alias("combined_value")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None], 
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df.with_columns(
    pl.coalesce([
        pl.col("value1"),
        pl.col("value2"), 
        pl.col("value3")
    ]).alias("combined_value")
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.coalesce([pl.col("value1"), pl.col("value2"), pl.col("value3")]).alias("combined_value")
)

print(df)

Output:

shape: (4, 5)
┌─────┬────────┬────────┬────────┬────────────────┐
│ id  ┆ value1 ┆ value2 ┆ value3 ┆ combined_value │
│ --- ┆ ---    ┆ ---    ┆ ---    ┆ ---            │
│ i64 ┆ i64    ┆ i64    ┆ i64    ┆ i64            │
╞═════╪════════╪════════╪════════╪════════════════╡
│ 1   ┆ 10     ┆ null   ┆ 100    ┆ 10             │
│ 2   ┆ null   ┆ 20     ┆ 200    ┆ 20             │
│ 3   ┆ 30     ┆ null   ┆ null   ┆ 30             │
│ 4   ┆ null   ┆ 40     ┆ null   ┆ 40             │
└─────┴────────┴────────┴────────┴────────────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df_with_combined = df.with_columns(
    pl.coalesce(["value1", "value2", "value3"]).alias("combined_value")
)
print(df_with_combined)

Output:

shape: (4, 5)
┌─────┬────────┬────────┬────────┬────────────────┐
│ id  ┆ value1 ┆ value2 ┆ value3 ┆ combined_value │
│ --- ┆ ---    ┆ ---    ┆ ---    ┆ ---            │
│ i64 ┆ i64    ┆ i64    ┆ i64    ┆ i64            │
╞═════╪════════╪════════╪════════╪════════════════╡
│ 1   ┆ 10     ┆ null   ┆ 100    ┆ 10             │
│ 2   ┆ null   ┆ 20     ┆ 200    ┆ 20             │
│ 3   ┆ 30     ┆ null   ┆ null   ┆ 30             │
│ 4   ┆ null   ┆ 40     ┆ null   ┆ 40             │
└─────┴────────┴────────┴────────┴────────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.coalesce([
        pl.col("value1"),
        pl.col("value2"),
        pl.col("value3")
    ]).alias("combined_value")
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.when(df["value1"].is_null())
    .then(pl.when(df["value2"].is_null()).then(df["value3"]).otherwise(df["value2"]))
    .otherwise(df["value1"])
    .alias("combined_value")
)

print(df)

Output:

shape: (4, 5)
┌─────┬────────┬────────┬────────┬────────────────┐
│ id  ┆ value1 ┆ value2 ┆ value3 ┆ combined_value │
│ --- ┆ ---    ┆ ---    ┆ ---    ┆ ---            │
│ i64 ┆ i64    ┆ i64    ┆ i64    ┆ i64            │
╞═════╪════════╪════════╪════════╪════════════════╡
│ 1   ┆ 10     ┆ null   ┆ 100    ┆ 10             │
│ 2   ┆ null   ┆ 20     ┆ 200    ┆ 20             │
│ 3   ┆ 30     ┆ null   ┆ null   ┆ 30             │
│ 4   ┆ null   ┆ 40     ┆ null   ┆ 40             │
└─────┴────────┴────────┴────────┴────────────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.coalesce(["value1", "value2", "value3"]).alias("combined_value")
)

print(df)

Output:

shape: (4, 5)
┌─────┬────────┬────────┬────────┬────────────────┐
│ id  ┆ value1 ┆ value2 ┆ value3 ┆ combined_value │
│ --- ┆ ---    ┆ ---    ┆ ---    ┆ ---            │
│ i64 ┆ i64    ┆ i64    ┆ i64    ┆ i64            │
╞═════╪════════╪════════╪════════╪════════════════╡
│ 1   ┆ 10     ┆ null   ┆ 100    ┆ 10             │
│ 2   ┆ null   ┆ 20     ┆ 200    ┆ 20             │
│ 3   ┆ 30     ┆ null   ┆ null   ┆ 30             │
│ 4   ┆ null   ┆ 40     ┆ null   ┆ 40             │
└─────┴────────┴────────┴────────┴────────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.coalesce([pl.col("value1"), pl.col("value2"), pl.col("value3")]).alias("combined_value")
)

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    (pl.coalesce(df["value1"], df["value2"], df["value3"])).alias("combined_value")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpf45ega_z.py", line 4, in 
    (pl.coalesce(df["value1"], df["value2"], df["value3"])).alias("combined_value")
                 ~~^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 1362, in __getitem__
    return get_df_item_by_key(self, key)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/getitem.py", line 163, in get_df_item_by_key
    return df.get_column(key)
           ^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 8523, in get_column
    return wrap_s(self._df.get_column(name))
                  ^^^^^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: "value1" not found

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.coalesce([pl.col("value1"), pl.col("value2"), pl.col("value3")]).alias("combined_value")
)

print(df)

Output:

shape: (4, 5)
┌─────┬────────┬────────┬────────┬────────────────┐
│ id  ┆ value1 ┆ value2 ┆ value3 ┆ combined_value │
│ --- ┆ ---    ┆ ---    ┆ ---    ┆ ---            │
│ i64 ┆ i64    ┆ i64    ┆ i64    ┆ i64            │
╞═════╪════════╪════════╪════════╪════════════════╡
│ 1   ┆ 10     ┆ null   ┆ 100    ┆ 10             │
│ 2   ┆ null   ┆ 20     ┆ 200    ┆ 20             │
│ 3   ┆ 30     ┆ null   ┆ null   ┆ 30             │
│ 4   ┆ null   ┆ 40     ┆ null   ┆ 40             │
└─────┴────────┴────────┴────────┴────────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.when(pl.col("value1").is_null())
    .then(
        pl.when(pl.col("value2").is_null())
        .then(pl.col("value3"))
        .otherwise(pl.col("value2"))
    )
    .otherwise(pl.col("value1"))
    .alias("combined_value")
)

print(df)

Output:

shape: (4, 5)
┌─────┬────────┬────────┬────────┬────────────────┐
│ id  ┆ value1 ┆ value2 ┆ value3 ┆ combined_value │
│ --- ┆ ---    ┆ ---    ┆ ---    ┆ ---            │
│ i64 ┆ i64    ┆ i64    ┆ i64    ┆ i64            │
╞═════╪════════╪════════╪════════╪════════════════╡
│ 1   ┆ 10     ┆ null   ┆ 100    ┆ 10             │
│ 2   ┆ null   ┆ 20     ┆ 200    ┆ 20             │
│ 3   ┆ 30     ┆ null   ┆ null   ┆ 30             │
│ 4   ┆ null   ┆ 40     ┆ null   ┆ 40             │
└─────┴────────┴────────┴────────┴────────────────┘

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.coalesce(["value1", "value2", "value3"]).alias("combined_value")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(pl.coalesce(["value1", "value2", "value3"]).alias("combined_value"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.coalesce(["value1", "value2", "value3"]).alias("combined_value")
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.coalesce(
        [pl.col("value1"), pl.col("value2"), pl.col("value3")]
    ).alias("combined_value")
)

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpn7pr5111.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(pl.coalesce(["value1", "value2", "value3"]).alias("combined_value"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpkzap3fe_.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3, 4],
    "value1": [10, None, 30, None],
    "value2": [None, 20, None, 40],
    "value3": [100, 200, None, None]
})

df = df.with_columns(
    pl.coalesce(
        [pl.col("value1"), pl.col("value2"), pl.col("value3")]
    ).alias("combined_value")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpgm0ovbwt.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

concatenate_columns

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Concatenate Name and Sex columns with a space between them
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of concatenating columns: df.with_columns(pl.concat_str([pl.col("first"), pl.col("last")], separator=" ").alias("full_name"))

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns((pl.col("Name") + " " + pl.col("Sex")).alias("Name_Sex"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.concat_str([pl.col("Name"), pl.col("Sex")], separator=" ").alias("Name_Sex"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Name") + " " + pl.col("Sex")).alias("Name_Sex")
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Name") + " " + pl.col("Sex").alias("Name_Sex"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.concat_str([pl.col("Name"), pl.col("Sex")], separator=" ").alias("Name_Sex"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns((pl.col("Name") + " " + pl.col("Sex")).alias("Name_Sex"))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Name").str.concat(" ").str.concat(pl.col("Sex")).alias("Name_Sex"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpyjwj3dxf.py", line 3, in 
    df = df.with_columns(pl.col("Name").str.concat(" ").str.concat(pl.col("Sex")).alias("Name_Sex"))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/expr/string.py", line 2910, in concat
    return self.join(delimiter, ignore_nulls=ignore_nulls)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/expr/string.py", line 2853, in join
    return wrap_expr(self._pyexpr.str_join(delimiter, ignore_nulls=ignore_nulls))
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: argument 'delimiter': 'Expr' object cannot be converted to 'PyString'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.concat_str([pl.col("Name"), pl.col("Sex")], separator=" ").alias("Name_Sex"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.concat_str([pl.col("Name"), pl.col("Sex")], separator=" ").alias("Name_Sex")
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    df = df.with_columns(
        pl.concat_str([pl.col("Name"), pl.lit(" "), pl.col("Sex")]).alias("Name_Sex")
    )
    return df

if __name__ == "__main__":
    df = solve()
    print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpop4va5iz.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.concat_str([pl.col("Name"), pl.col("Sex")], separator=" ").alias("Name_Sex"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp237gtiaz.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Assume titanic.csv is in a data folder
try:
    df = pl.read_csv("data/titanic.csv")
except FileNotFoundError:
    print("titanic.csv not found in data folder. Please ensure the file exists.")
    exit()


result = df.with_columns(
    pl.concat_str([pl.col("Name"), pl.col("Sex")], separator=" ").alias("Name_Sex")
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpiaebbd0a.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Concatenate Name and Sex columns with a space between them
df = df.with_column(
    pl.concat_str([
        pl.col("Name"),
        pl.lit(" "),
        pl.col("Sex")
    ]).alias("Name_Sex")
)

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpd1pz943u.py", line 8, in 
    df = df.with_column(
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Concatenate Name and Sex columns with a space between them
df = df.with_columns(pl.concat_str([pl.col("Name"), pl.col("Sex")], separator=" ").alias("NameSex"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Concatenate Name and Sex columns with a space between them
df = df.with_columns(
    (pl.col("Name") + " " + pl.col("Sex")).alias("Name_Sex")
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns((pl.col("Name") + " " + pl.col("Sex")).alias("Name_Sex"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.concat_str([pl.col("Name"), pl.col("Sex")], separator=" ").alias("Name_Sex"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.with_columns(
    (pl.col("Name") + " " + pl.col("Sex")).alias("NameSex")
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df["Name_Sex"] = df["Name"] + " " + df["Sex"]

Error:

TypeError(msg)
TypeError: DataFrame object does not support `Series` assignment by index

Use `DataFrame.with_columns`.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.concat_str([pl.col("Name"), pl.col("Sex")], separator=" ").alias("Name_and_Sex"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns((pl.col("Name") + " " + pl.col("Sex")).alias("Name_Sex"))

Status: ✓ Success

correlation_float_columns

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Calculate the correlation matrix between all float columns
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of calculating correlations:
# df.select(pl.col(pl.Float64)).corr()

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")
float_cols = df.select([col for col in df.columns if df[col].dtype == pl.Float64]).to_pandas()
corr_matrix = float_cols.corr(method='pearson')
print(corr_matrix)

Output:

Age      Fare
Age   1.000000  0.096067
Fare  0.096067  1.000000

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
correlation_matrix = df.select(pl.col(pl.Float64)).corr()
print(correlation_matrix)

Output:

shape: (2, 2)
┌─────┬──────┐
│ Age ┆ Fare │
│ --- ┆ ---  │
│ f64 ┆ f64  │
╞═════╪══════╡
│ NaN ┆ NaN  │
│ NaN ┆ 1.0  │
└─────┴──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

correlation_matrix = df.select(pl.col(pl.Float64)).corr()
print(correlation_matrix)

Output:

shape: (2, 2)
┌─────┬──────┐
│ Age ┆ Fare │
│ --- ┆ ---  │
│ f64 ┆ f64  │
╞═════╪══════╡
│ NaN ┆ NaN  │
│ NaN ┆ 1.0  │
└─────┴──────┘

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
correlation_matrix = df.select(pl.col(pl.Float64)).corr()
print(correlation_matrix)

Output:

shape: (2, 2)
┌─────┬──────┐
│ Age ┆ Fare │
│ --- ┆ ---  │
│ f64 ┆ f64  │
╞═════╪══════╡
│ NaN ┆ NaN  │
│ NaN ┆ 1.0  │
└─────┴──────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
# Calculate the correlation matrix between all float columns
correlation_matrix = df.select(pl.col(pl.Float64)).corr()

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
correlation_matrix = df.select(pl.all().is_float()).corr()
print(correlation_matrix)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdr7kpst7.py", line 6, in 
    correlation_matrix = df.select(pl.all().is_float()).corr()
                                   ^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'is_float'

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

float_columns = [col for col in df.columns if df[col].dtype in (pl.Float32, pl.Float64)]
correlation_matrix = df.select(float_columns).corr()

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Calculate the correlation matrix between all float columns
df.select(pl.col(pl.Float64)).corr()

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
float_columns = [col for col in df.columns if df[col].dtype in (pl.Float32, pl.Float64)]
correlation_matrix = df.select(float_columns).corr()

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")

numeric_cols = df.select(
    pl.col(['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare'])
)

corr_matrix = numeric_cols.corr()
print(corr_matrix)

Output:

shape: (7, 7)
┌─────────────┬───────────┬───────────┬─────┬───────────┬───────────┬──────────┐
│ PassengerId ┆ Survived  ┆ Pclass    ┆ Age ┆ SibSp     ┆ Parch     ┆ Fare     │
│ ---         ┆ ---       ┆ ---       ┆ --- ┆ ---       ┆ ---       ┆ ---      │
│ f64         ┆ f64       ┆ f64       ┆ f64 ┆ f64       ┆ f64       ┆ f64      │
╞═════════════╪═══════════╪═══════════╪═════╪═══════════╪═══════════╪══════════╡
│ 1.0         ┆ -0.005007 ┆ -0.035144 ┆ NaN ┆ -0.057527 ┆ -0.001652 ┆ 0.012658 │
│ -0.005007   ┆ 1.0       ┆ -0.338481 ┆ NaN ┆ -0.035322 ┆ 0.081629  ┆ 0.257307 │
│ -0.035144   ┆ -0.338481 ┆ 1.0       ┆ NaN ┆ 0.083081  ┆ 0.018443  ┆ -0.5495  │
│ NaN         ┆ NaN       ┆ NaN       ┆ NaN ┆ NaN       ┆ NaN       ┆ NaN      │
│ -0.057527   ┆ -0.035322 ┆ 0.083081  ┆ NaN ┆ 1.0       ┆ 0.414838  ┆ 0.159651 │
│ -0.001652   ┆ 0.081629  ┆ 0.018443  ┆ NaN ┆ 0.414838  ┆ 1.0       ┆ 0.216225 │
│ 0.012658    ┆ 0.257307  ┆ -0.5495   ┆ NaN ┆ 0.159651  ┆ 0.216225  ┆ 1.0      │
└─────────────┴───────────┴───────────┴─────┴───────────┴───────────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

numeric_cols = ['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare']
correlation_matrix = df.select(numeric_cols).corr()

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Get float/integer columns 
numeric_cols = df.select([
    col for col in df.columns 
    if df[col].dtype in [pl.Float64, pl.Int64]
])

# Calculate correlation matrix
corr_matrix = numeric_cols.corr()

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Select only float columns for correlation
float_cols = df.select(
    pl.col(col_name) for col_name in df.columns
    if df[col_name].dtype in [pl.Float32, pl.Float64] or 
       (df[col_name].dtype in [pl.Int32, pl.Int64] and col_name not in ["Ticket"])
)

# Calculate correlation matrix
correlation_matrix = float_cols.corr()
print(correlation_matrix)

Output:

shape: (7, 7)
┌─────────────┬───────────┬───────────┬─────┬───────────┬───────────┬──────────┐
│ PassengerId ┆ Survived  ┆ Pclass    ┆ Age ┆ SibSp     ┆ Parch     ┆ Fare     │
│ ---         ┆ ---       ┆ ---       ┆ --- ┆ ---       ┆ ---       ┆ ---      │
│ f64         ┆ f64       ┆ f64       ┆ f64 ┆ f64       ┆ f64       ┆ f64      │
╞═════════════╪═══════════╪═══════════╪═════╪═══════════╪═══════════╪══════════╡
│ 1.0         ┆ -0.005007 ┆ -0.035144 ┆ NaN ┆ -0.057527 ┆ -0.001652 ┆ 0.012658 │
│ -0.005007   ┆ 1.0       ┆ -0.338481 ┆ NaN ┆ -0.035322 ┆ 0.081629  ┆ 0.257307 │
│ -0.035144   ┆ -0.338481 ┆ 1.0       ┆ NaN ┆ 0.083081  ┆ 0.018443  ┆ -0.5495  │
│ NaN         ┆ NaN       ┆ NaN       ┆ NaN ┆ NaN       ┆ NaN       ┆ NaN      │
│ -0.057527   ┆ -0.035322 ┆ 0.083081  ┆ NaN ┆ 1.0       ┆ 0.414838  ┆ 0.159651 │
│ -0.001652   ┆ 0.081629  ┆ 0.018443  ┆ NaN ┆ 0.414838  ┆ 1.0       ┆ 0.216225 │
│ 0.012658    ┆ 0.257307  ┆ -0.5495   ┆ NaN ┆ 0.159651  ┆ 0.216225  ┆ 1.0      │
└─────────────┴───────────┴───────────┴─────┴───────────┴───────────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Get all float columns
float_cols = df.select(pl.col(pl.FLOAT_DTYPES))

# If there are no float columns, convert numeric columns to float
if float_cols.width == 0:
    numeric_cols = ["PassengerId", "Survived", "Pclass", "Age", "SibSp", "Parch", "Fare"]
    float_cols = df.select([pl.col(col).cast(pl.Float64) for col in numeric_cols if col in df.columns])

# Calculate correlation matrix
correlation_matrix = float_cols.corr()
print(correlation_matrix)

Output:

shape: (2, 2)
┌─────┬──────┐
│ Age ┆ Fare │
│ --- ┆ ---  │
│ f64 ┆ f64  │
╞═════╪══════╡
│ NaN ┆ NaN  │
│ NaN ┆ 1.0  │
└─────┴──────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp7ys4biqg.py:9: DeprecationWarning: `FLOAT_DTYPES` is deprecated. Define your own data type groups or use the `polars.selectors` module for selecting columns of a certain data type.
  float_cols = df.select(pl.col(pl.FLOAT_DTYPES))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# First load the data using Polars as follows
df = pl.read_csv("data/titanic.csv")

# Select only the float/numeric columns for correlation
numeric_cols = df.select([
    col for col in df.columns 
    if df[col].dtype in [pl.Float64, pl.Float32, pl.Int64, pl.Int32, pl.Int16, pl.Int8]
])

# Calculate the correlation matrix
correlation_matrix = numeric_cols.corr()

# Display the correlation matrix
print(correlation_matrix)

Output:

shape: (7, 7)
┌─────────────┬───────────┬───────────┬─────┬───────────┬───────────┬──────────┐
│ PassengerId ┆ Survived  ┆ Pclass    ┆ Age ┆ SibSp     ┆ Parch     ┆ Fare     │
│ ---         ┆ ---       ┆ ---       ┆ --- ┆ ---       ┆ ---       ┆ ---      │
│ f64         ┆ f64       ┆ f64       ┆ f64 ┆ f64       ┆ f64       ┆ f64      │
╞═════════════╪═══════════╪═══════════╪═════╪═══════════╪═══════════╪══════════╡
│ 1.0         ┆ -0.005007 ┆ -0.035144 ┆ NaN ┆ -0.057527 ┆ -0.001652 ┆ 0.012658 │
│ -0.005007   ┆ 1.0       ┆ -0.338481 ┆ NaN ┆ -0.035322 ┆ 0.081629  ┆ 0.257307 │
│ -0.035144   ┆ -0.338481 ┆ 1.0       ┆ NaN ┆ 0.083081  ┆ 0.018443  ┆ -0.5495  │
│ NaN         ┆ NaN       ┆ NaN       ┆ NaN ┆ NaN       ┆ NaN       ┆ NaN      │
│ -0.057527   ┆ -0.035322 ┆ 0.083081  ┆ NaN ┆ 1.0       ┆ 0.414838  ┆ 0.159651 │
│ -0.001652   ┆ 0.081629  ┆ 0.018443  ┆ NaN ┆ 0.414838  ┆ 1.0       ┆ 0.216225 │
│ 0.012658    ┆ 0.257307  ┆ -0.5495   ┆ NaN ┆ 0.159651  ┆ 0.216225  ┆ 1.0      │
└─────────────┴───────────┴───────────┴─────┴───────────┴───────────┴──────────┘

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

float_cols = df.select(
    pl.col(c).cast(pl.Float64) for c in df.columns if df[c].dtype in [pl.Float64, pl.Int64]
)

correlation_matrix = float_cols.corr()

print(correlation_matrix)

Output:

shape: (7, 7)
┌─────────────┬───────────┬───────────┬─────┬───────────┬───────────┬──────────┐
│ PassengerId ┆ Survived  ┆ Pclass    ┆ Age ┆ SibSp     ┆ Parch     ┆ Fare     │
│ ---         ┆ ---       ┆ ---       ┆ --- ┆ ---       ┆ ---       ┆ ---      │
│ f64         ┆ f64       ┆ f64       ┆ f64 ┆ f64       ┆ f64       ┆ f64      │
╞═════════════╪═══════════╪═══════════╪═════╪═══════════╪═══════════╪══════════╡
│ 1.0         ┆ -0.005007 ┆ -0.035144 ┆ NaN ┆ -0.057527 ┆ -0.001652 ┆ 0.012658 │
│ -0.005007   ┆ 1.0       ┆ -0.338481 ┆ NaN ┆ -0.035322 ┆ 0.081629  ┆ 0.257307 │
│ -0.035144   ┆ -0.338481 ┆ 1.0       ┆ NaN ┆ 0.083081  ┆ 0.018443  ┆ -0.5495  │
│ NaN         ┆ NaN       ┆ NaN       ┆ NaN ┆ NaN       ┆ NaN       ┆ NaN      │
│ -0.057527   ┆ -0.035322 ┆ 0.083081  ┆ NaN ┆ 1.0       ┆ 0.414838  ┆ 0.159651 │
│ -0.001652   ┆ 0.081629  ┆ 0.018443  ┆ NaN ┆ 0.414838  ┆ 1.0       ┆ 0.216225 │
│ 0.012658    ┆ 0.257307  ┆ -0.5495   ┆ NaN ┆ 0.159651  ┆ 0.216225  ┆ 1.0      │
└─────────────┴───────────┴───────────┴─────┴───────────┴───────────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

correlation_matrix = df.select(pl.col(pl.Float64)).corr()

print(correlation_matrix)

Output:

shape: (2, 2)
┌─────┬──────┐
│ Age ┆ Fare │
│ --- ┆ ---  │
│ f64 ┆ f64  │
╞═════╪══════╡
│ NaN ┆ NaN  │
│ NaN ┆ 1.0  │
└─────┴──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Calculate the correlation matrix between all float columns
float_columns = df.select(pl.col(pl.Any).filter(pl.col(pl.Any).dtype == pl.Float64)).columns
correlation_matrix = df.select(float_columns).corr()

Error:

TypeError(msg) from None
TypeError: cannot parse input of type '_AnyMeta' into Polars data type (given: typing.Any)

Status: ✗ Failed

create_bar_chart

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Create a bar chart using the plot method showing:
# 1. Passenger class (Pclass) on the x-axis
# 2. Average fare on the y-axis
# 3. Color bars by passenger class
# 4. Add a title "Average Fare by Passenger Class"
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of creating a bar chart:
#  df.plot.bar(x="id",y="value")

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

(
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean())
    .sort("Pclass")
    .plot(
        x="Pclass",
        y="Fare",
        kind="bar",
        title="Average Fare by Passenger Class",
        color="Pclass",
    )
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpd_q279w8.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.group_by("Pclass").agg(pl.mean("Fare")).sort("Pclass").plot.bar(x="Pclass", y="Fare", color="Pclass", title="Average Fare by Passenger Class")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp7okxxqk7.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")

(
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean())
    .sort("Pclass")
    .with_columns(pl.col("Pclass").cast(pl.Utf8))
    .plot(
        x="Pclass",
        y="Fare",
        kind="bar",
        title="Average Fare by Passenger Class",
        color="Pclass",
    )
)
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpemp_szgw.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")

fig, ax = plt.subplots()
ax = df.groupby("Pclass").agg(pl.col("Fare").mean()).plot(kind="bar", x="Pclass", y="Fare", ax=ax, color=plt.cm.tab20(range(3)))
ax.set_title("Average Fare by Passenger Class")
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmptxkhi46b.py", line 5, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")

df.groupby("Pclass").agg(pl.col("Fare").mean()).collect().to_pandas().plot.bar(x="Pclass", y="Fare")
plt.title("Average Fare by Passenger Class")
plt.xlabel("Passenger Class")
plt.ylabel("Average Fare")
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpgg9mjx2g.py", line 5, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")

df.groupby("Pclass").agg(pl.col("Fare").mean()).plot(kind="bar", x="Pclass", y="Fare", color="Pclass")
plt.title("Average Fare by Passenger Class")
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpf2841bxr.py", line 5, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")

# Calculate average fare by passenger class
average_fare_by_class = df.groupby("Pclass").agg(pl.mean("Fare")).to_pandas()

# Plot the bar chart
plt.bar(
    average_fare_by_class['Pclass'],
    average_fare_by_class['Fare'],
    color=['blue', 'green', 'red']  # Adjust colors as needed
)
plt.xlabel('Passenger Class')
plt.ylabel('Average Fare')
plt.title('Average Fare by Passenger Class')
plt.xticks(ticks=average_fare_by_class['Pclass'])
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp925tselt.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")
avg_fare_by_class = df.groupby("Pclass").agg(pl.col("Fare").mean().alias("Average_Fare")).sort("Pclass")

avg_fare_by_class.to_pandas().plot.bar(x="Pclass", y="Average_Fare", color=["skyblue", "orange", "green"], legend=False)
plt.title("Average Fare by Passenger Class")
plt.ylabel("Average Fare")
plt.xlabel("Passenger Class")
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpp0nfy8_z.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")

# Compute average fare by passenger class
avg_fare_by_class = df.group_by("Pclass").agg(pl.col("Fare").mean().alias("avg_fare")).collect()

# Plotting
plt.bar(avg_fare_by_class["Pclass"].to_list(), avg_fare_by_class["avg_fare"].to_list(), color=['blue', 'orange', 'green'])
plt.xlabel('Passenger Class')
plt.ylabel('Average Fare')
plt.title('Average Fare by Passenger Class')
plt.xticks(avg_fare_by_class["Pclass"].to_list())
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmplpgargh8.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

# Load the data
df = pl.read_csv("data/titanic.csv")

# Group by Pclass and calculate average fare
avg_fare_by_class = df.group_by('Pclass').agg(pl.col('Fare').mean().alias('AvgFare')).sort('Pclass')

# Convert to pandas for plotting
pdf = avg_fare_by_class.to_pandas()

# Define colors for each class
colors = ['#3366CC', '#FF9900', '#DC3912']

# Create the bar chart
plt.figure(figsize=(10, 6))
bars = plt.bar(pdf['Pclass'].astype(str), pdf['AvgFare'], color=colors[:len(pdf)])

# Add title and labels
plt.title('Average Fare by Passenger Class', fontsize=14)
plt.xlabel('Passenger Class', fontsize=12)
plt.ylabel('Average Fare ($)', fontsize=12)

# Add a grid for better readability
plt.grid(axis='y', linestyle='--', alpha=0.7)

# Show the plot
plt.tight_layout()
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmphdk0m95_.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

# Load data
df = pl.read_csv("data/titanic.csv")

# Compute average fare per class
avg_fare_by_class = df.group_by("Pclass").agg(pl.col("Fare").mean().alias("AvgFare")).sort("Pclass")

# Create a bar chart
fig, ax = plt.subplots(figsize=(10, 6))
bars = ax.bar(
    avg_fare_by_class["Pclass"].to_list(), 
    avg_fare_by_class["AvgFare"].to_list(),
    color=['#1f77b4', '#ff7f0e', '#2ca02c']  # Colors for each class
)

# Add labels and title
ax.set_xlabel('Passenger Class')
ax.set_ylabel('Average Fare ($)')
ax.set_title('Average Fare by Passenger Class')
ax.set_xticks([1, 2, 3])
ax.set_xticklabels(['1st Class', '2nd Class', '3rd Class'])

plt.tight_layout()
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpud67xqly.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

# Load the data
df = pl.read_csv("data/titanic.csv")

# Group by passenger class and calculate average fare
avg_fare_by_class = df.group_by("Pclass").agg(pl.col("Fare").mean()).sort("Pclass")

# Create the plot
plt.figure(figsize=(10, 6))
bars = plt.bar(
    avg_fare_by_class["Pclass"].cast(str),
    avg_fare_by_class["Fare"],
    color=['blue', 'green', 'red']
)

# Add labels and title
plt.xlabel('Passenger Class')
plt.ylabel('Average Fare')
plt.title('Average Fare by Passenger Class')

# Display the plot
plt.tight_layout()
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpyjq918cy.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.groupby('Pclass').agg(pl.col('Fare').mean()).sort('Pclass').plot(
    x='Pclass',
    y='Fare',
    kind='bar',
    color='Pclass',
    title='Average Fare by Passenger Class'
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwfxq1zew.py", line 3, in 
    df.groupby('Pclass').agg(pl.col('Fare').mean()).sort('Pclass').plot(
    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import plotly.express as px

df = pl.read_csv("data/titanic.csv")
avg_fare = df.groupby("Pclass").agg(pl.col("Fare").mean()).sort("Pclass")

fig = px.bar(
    avg_fare,
    x="Pclass",
    y="Fare",
    color="Pclass",
    title="Average Fare by Passenger Class"
)

fig.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpds4e4m_k.py", line 7, in 
    avg_fare = df.groupby("Pclass").agg(pl.col("Fare").mean()).sort("Pclass")
               ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

(df.groupby('Pclass')
 .agg(pl.col('Fare').mean().alias('Average_Fare'))
 .sort('Pclass')
 .plot.bar(
     x='Pclass',
     y='Average_Fare',
     color='Pclass',
     title='Average Fare by Passenger Class'
 ))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpeeu3y4mx.py", line 7, in 
    (df.groupby('Pclass')
     ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")
grouped = df.group_by("Pclass").agg(pl.col("Fare").mean().alias("AverageFare"))
plt.figure()
grouped.to_pandas().plot.bar(x="Pclass", y="AverageFare", color=["blue", "green", "red"], legend=False)
plt.title("Average Fare by Passenger Class")
plt.xlabel("Passenger Class")
plt.ylabel("Average Fare")
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_7i0p5x3.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.group_by("Pclass").agg(pl.col("Fare").mean()).sort("Pclass").plot.bar(
    x="Pclass",
    y="Fare",
    title="Average Fare by Passenger Class",
    color="Pclass"
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_9_qv51y.py", line 6, in 
    df.group_by("Pclass").agg(pl.col("Fare").mean()).sort("Pclass").plot.bar(
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/plotting.py", line 97, in bar
    self._chart.mark_bar(tooltip=True)
TypeError: _EncodingMixin.encode() got an unexpected keyword argument 'title'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Group by Pclass and calculate average fare
avg_fare = df.group_by("Pclass").agg(pl.col("Fare").mean().alias("AverageFare"))

# Create bar chart
avg_fare.plot.bar(
    x="Pclass",
    y="AverageFare",
    color="Pclass",
    title="Average Fare by Passenger Class"
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpiwkoh6xo.py", line 10, in 
    avg_fare.plot.bar(
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/plotting.py", line 97, in bar
    self._chart.mark_bar(tooltip=True)
TypeError: _EncodingMixin.encode() got an unexpected keyword argument 'title'

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

avg_fare_by_pclass = df.group_by("Pclass").agg(
    pl.col("Fare").mean().alias("Average Fare")
)

avg_fare_by_pclass.plot(
    x="Pclass",
    y="Average Fare",
    color="Pclass",
    title="Average Fare by Passenger Class",
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmptkg3hoe3.py", line 11, in 
    avg_fare_by_pclass.plot(
TypeError: 'DataFramePlot' object is not callable

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.groupby("Pclass")["Fare"].mean().plot.bar(
    x="Pclass", y="Fare", color="Pclass", title="Average Fare by Passenger Class"
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpeyj7arlb.py", line 7, in 
    df.groupby("Pclass")["Fare"].mean().plot.bar(
    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.group_by("Pclass").agg(pl.col("Fare").mean()).plot(
    x="Pclass", y="Fare", kind="bar", color="Pclass", title="Average Fare by Passenger Class"
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpacx55olc.py", line 7, in 
    df.group_by("Pclass").agg(pl.col("Fare").mean()).plot(
TypeError: 'DataFramePlot' object is not callable

Status: ✗ Failed

create_column_from_list

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv").head(5)

# Create a new column 'row_labels' from a list of values 1 to 5 repeated
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of creating a column from a list: df.with_columns(pl.Series("new_col", [1,2,3]))

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.Series("row_labels", [1, 2, 3, 4, 5]))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwn8oj_ev.py", line 3, in 
    df = df.with_columns(pl.Series("row_labels", [1, 2, 3, 4, 5]))
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ShapeError: unable to add a column of length 5 to a DataFrame of height 891

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.Series("row_labels", [1, 2, 3, 4, 5]))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpj035_6j7.py", line 3, in 
    df = df.with_columns(pl.Series("row_labels", [1, 2, 3, 4, 5]))
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ShapeError: unable to add a column of length 5 to a DataFrame of height 891

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv").head(5)
row_labels = [1, 2, 3, 4, 5]
df = df.with_columns(pl.Series("row_labels", row_labels))

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv").head(5)
df = df.with_columns(pl.Series("row_labels", list(range(1, 6))))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv").head(5)

df = df.with_columns(pl.Series("row_labels", list(range(1,6))))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv").head(5)

df = df.with_columns(
    pl.Series(name="row_labels", values=list(range(1,6)))
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv").head(5)

# Create row_labels column from values 1 to 5
df = df.with_column(pl.Series("row_labels", list(range(1, 6))))

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp3spuxt38.py", line 8, in 
    df = df.with_column(pl.Series("row_labels", list(range(1, 6))))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv").head(5)

df = df.with_columns(pl.Series("row_labels", list(range(1, 6))))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv").head(5)

# Create a new column 'row_labels' with values from 1 to 5
df = df.with_columns(
    pl.Series(name="row_labels", values=list(range(1, 6)))
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv").head(5)
df = df.with_columns(pl.col("*"), pl.lit([1,2,3,4,5]).alias("row_labels"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv").head(5)
df = df.with_columns(pl.Series("row_labels", [1, 2, 3, 4, 5]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv").head(5)
df = df.with_columns([pl.arange(1, 6).alias("row_labels")])

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv").head(5)
df = df.with_columns(pl.Series("row_labels", [1, 2, 3, 4, 5]))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv").head(5)
df = df.with_columns(pl.Series("row_labels", [1, 2, 3, 4, 5]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv").head(5)
df = df.with_columns(pl.Series("row_labels", [1, 2, 3, 4, 5]))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv").head(5)
row_labels = list(range(1, 6))
df = df.with_columns(pl.Series(name="row_labels", values=row_labels))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv").head(5)

row_labels = list(range(1, 6))
df = df.with_columns(pl.Series("row_labels", row_labels))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv").head(5)

row_labels = [1, 2, 3, 4, 5]
df = df.with_columns(pl.Series(name="row_labels", values=row_labels))

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv").head(5)

row_labels = list(range(1, 6))
df = df.with_columns(pl.Series(name="row_labels", values=row_labels))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmptkk5kvkk.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv").head(5)

row_labels = list(range(1, 6))
df = df.with_columns(pl.Series("row_labels", row_labels))

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpkgoaj6aw.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv").head(5)
df = df.with_columns(pl.Series(name="row_labels", values=list(range(1, 6))))
print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpx85ofsgz.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

create_column_from_numpy

Original Prompt

# First load the data using Polars as follows
import polars as pl
import numpy as np
df = pl.read_csv("data/titanic.csv")

# Create a new column 'random_scores' using numpy to generate random numbers between 0 and 1
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of creating a column from numpy: df.with_columns(pl.Series("new_col", np.array([1.0, 2.0, 3.0])))

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.Series(
        name="random_scores",
        values=np.random.uniform(0, 1, len(df))
)

Error:

'(' was never closed (, line 7)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.Series("random_scores", np.random.rand(len(df))))

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.Series("random_scores", np.random.rand(df.height))
)

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.Series('random_scores', np.random.rand(df.height)))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpi573fxtv.py", line 3, in 
    df = df.with_columns(pl.Series('random_scores', np.random.rand(df.height)))
                                                    ^^
NameError: name 'np' is not defined

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.Series("random_scores", np.random.rand(df.height)))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpcdck3ods.py", line 3, in 
    df = df.with_columns(pl.Series("random_scores", np.random.rand(df.height)))
                                                    ^^
NameError: name 'np' is not defined

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.Series(np.random.rand(df.height)).alias("random_scores"))

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")

# Generate random scores and add as a new column
np.random.seed(42)  # for reproducibility
random_scores = np.random.random(len(df))
df = df.with_column(pl.lit(random_scores).alias("random_scores"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0cb_zk46.py", line 11, in 
    df = df.with_column(pl.lit(random_scores).alias("random_scores"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")

# Generate random scores with the same length as the dataframe
random_values = np.random.random(len(df))

# Add the random scores as a new column
df = df.with_columns(pl.Series("random_scores", random_values))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")

# Create a new column 'random_scores' with random numbers between 0 and 1
df = df.with_columns(
    pl.lit(np.random.random(len(df))).alias("random_scores")
)

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.Series(name="random_scores", values=np.random.rand(df.height)))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.Series("random_scores", np.random.rand(df.height)))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.Series(np.random.rand(df.height)).alias("random_scores"))

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.Series(name="random_scores", values=np.random.random(len(df)))
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")
n_rows = df.height
random_values = np.random.random(n_rows)
df = df.with_columns(pl.Series("random_scores", random_values))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.lit(np.random.random(len(df))).alias("random_scores")
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import numpy as np
df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.col("random_scores").cast(pl.Float64, np.random.rand(len(df))))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpppw5z0me.py", line 7, in 
    df = df.with_column(pl.col("random_scores").cast(pl.Float64, np.random.rand(len(df))))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.Series("random_scores", np.random.rand(len(df))))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.Series("random_scores", np.random.rand(len(df))))

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.Series(name="random_scores", values=np.random.rand(df.shape[0]))
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0j5aj1rw.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.Series("random_scores", np.random.rand(df.height)))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpumb5q2_x.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.Series(name="random_scores", values=np.random.rand(len(df)))
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp5hoh6rpw.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

create_scatter_chart

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Create a bar chart showing:
# 1. Passenger class (Pclass) on the x-axis
# 2. Average fare on the y-axis
# 3. Color bars by passenger class
# 4. Add a title "Average Fare by Passenger Class"
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of creating a bar chart:
#  df.plot.bar(x="id",y="value")

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import plotly.express as px

df = pl.read_csv("data/titanic.csv")

average_fare = df.group_by("Pclass").agg(pl.mean("Fare"))

fig = px.bar(
    average_fare.to_pandas(),
    x="Pclass",
    y="Fare",
    color="Pclass",
    title="Average Fare by Passenger Class",
    labels={"Fare": "Average Fare", "Pclass": "Passenger Class"},
)

fig.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_6f884an.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")

avg_fare_by_pclass = df.group_by("Pclass").agg(pl.mean("Fare").alias("Average Fare")).sort("Pclass")

plt.figure(figsize=(8, 6))
plt.bar(avg_fare_by_pclass["Pclass"].to_numpy().astype(str), avg_fare_by_pclass["Average Fare"].to_numpy(), color=['red', 'green', 'blue'])
plt.xlabel("Passenger Class (Pclass)")
plt.ylabel("Average Fare")
plt.title("Average Fare by Passenger Class")
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0xrehbg5.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import plotly.express as px

df = pl.read_csv("data/titanic.csv")

df_grouped = df.group_by("Pclass").agg(pl.col("Fare").mean())

fig = px.bar(
    df_grouped.to_pandas(),
    x="Pclass",
    y="Fare",
    color="Pclass",
    title="Average Fare by Passenger Class",
)

fig.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpjbbsozex.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")
avg_fare_by_pclass = df.groupby("Pclass").agg({"Fare": "mean"})

avg_fare_by_pclass.select(pl.col("Pclass"), pl.col("Fare").alias("Average Fare")).to_pandas().plot(x="Pclass", y="Average Fare", kind="bar", color=plt.cmtab20(range(3)))
plt.title("Average Fare by Passenger Class")
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8r1zxpra.py", line 5, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")
df.groupby("Pclass").agg(pl.col("Fare").mean()).rename({"Fare": "Average Fare"}).plot.bar(x="Pclass",y="Average Fare", color_by="Pclass")
plt.title("Average Fare by Passenger Class")
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp4665gqlv.py", line 5, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code to create a bar chart using Polars and Matplotlib:

import polars as pl
import matplotlib.pyplot as plt

# Load the data
df = pl.read_csv("data/titanic.csv")

# Group by Pclass and calculate average Fare
avg_fare_by_class = df.groupby("Pclass").agg(pl.col("Fare").mean())

# Convert to Pandas DataFrame for plotting
pd_df = avg_fare_by_class.to_pandas()

# Create a bar chart
plt.figure(figsize=(10, 6))
plt.bar(pd_df["Pclass"], pd_df["Fare"], color=plt.cm.tab20(range(len(pd_df))))
plt.xlabel("Passenger Class")
plt.ylabel("Average Fare")
plt.title("Average Fare by Passenger Class")
plt.show()

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")

avg_fare_by_class = df.groupby("Pclass")["Fare"].mean().to_pandas()

plt.figure(figsize=(8, 6))
plt.bar(avg_fare_by_class.index, avg_fare_by_class.values, color=['skyblue', 'lightgreen', 'lightcoral'])
plt.xlabel("Passenger Class")
plt.ylabel("Average Fare")
plt.title("Average Fare by Passenger Class")
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmphr8ikm0w.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.group_by("Pclass")["Fare"].mean().plot.bar(
    x="Pclass", y="Fare", color="Pclass", title="Average Fare by Passenger Class"
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0f3ean6l.py", line 7, in 
    df.group_by("Pclass")["Fare"].mean().plot.bar(
    ~~~~~~~~~~~~~~~~~~~~~^^^^^^^^
TypeError: 'GroupBy' object is not subscriptable

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

# Load the data
df = pl.read_csv("data/titanic.csv")

# Calculate the average fare for each passenger class
avg_fare_by_class = df.group_by("Pclass").agg(pl.col("Fare").mean()).sort("Pclass")

# Create the bar chart
plt.figure(figsize=(8, 6))
plt.bar(avg_fare_by_class["Pclass"], avg_fare_by_class["Fare"], color=['skyblue', 'lightgreen', 'lightcoral'])
plt.xlabel("Passenger Class")
plt.ylabel("Average Fare")
plt.title("Average Fare by Passenger Class")
plt.xticks(avg_fare_by_class["Pclass"])  # Ensure x-axis ticks are passenger classes
plt.grid(axis='y', linestyle='--')
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp4w5nahrp.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import seaborn as sns
import matplotlib.pyplot as plt

# Calculate average fare by class
avg_fare = df.groupby('Pclass').agg(
    pl.col('Fare').mean().alias('avg_fare')
)

# Create bar plot
plt.figure(figsize=(10,6))
sns.barplot(data=avg_fare.to_pandas(), x='Pclass', y='avg_fare', palette='Set2')
plt.title('Average Fare by Passenger Class')
plt.xlabel('Passenger Class')
plt.ylabel('Average Fare ($)')
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8_62bpgc.py", line 4, in 
    import seaborn as sns
ModuleNotFoundError: No module named 'seaborn'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import plotly.express as px

# Group by Pclass and calculate mean fare
fare_by_class = df.groupby('Pclass').agg(pl.col('Fare').mean()).sort('Pclass')

# Create bar chart
fig = px.bar(
    fare_by_class,
    x='Pclass',
    y='Fare',
    color='Pclass',
    title='Average Fare by Passenger Class'
)

# Update layout
fig.update_layout(
    xaxis_title='Passenger Class',
    yaxis_title='Average Fare ($)'
)

fig.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1kq_03v4.py", line 7, in 
    fare_by_class = df.groupby('Pclass').agg(pl.col('Fare').mean()).sort('Pclass')
                    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import plotly.express as px

# Load data
df = pl.read_csv("data/titanic.csv")

# Calculate average fare by class
avg_fare = df.group_by("Pclass").agg(
    pl.col("Fare").mean().alias("Average_Fare")
).collect()

# Create bar chart
fig = px.bar(avg_fare, 
             x="Pclass",
             y="Average_Fare",
             color="Pclass",
             title="Average Fare by Passenger Class")

fig.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpiw1mmcxe.py", line 12, in 
    ).collect()
      ^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'collect'

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")
grouped = df.group_by("Pclass").agg(pl.col("Fare").mean().alias("AverageFare"))

plt.bar(
    grouped["Pclass"].to_list(),
    grouped["AverageFare"].to_list(),
    color=['blue', 'green', 'red']
)
plt.xlabel("Passenger Class")
plt.ylabel("Average Fare")
plt.title("Average Fare by Passenger Class")
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdalghigj.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")

# Group by Pclass and calculate average fare
avg_fare = df.group_by("Pclass").agg(pl.col("Fare").mean().alias("AverageFare"))

# Convert to pandas for plotting
avg_fare_pd = avg_fare.to_pandas()

# Create the bar chart
plt.bar(avg_fare_pd["Pclass"], avg_fare_pd["AverageFare"], color=['blue', 'green', 'red'])
plt.xlabel("Passenger Class (Pclass)")
plt.ylabel("Average Fare")
plt.title("Average Fare by Passenger Class")
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpx8a0ujzi.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")

# Group by Pclass and calculate average fare
avg_fare = df.group_by("Pclass").agg(pl.col("Fare").mean().alias("AverageFare"))

# Convert to pandas for plotting
avg_fare_pd = avg_fare.to_pandas()

# Create bar chart
plt.bar(avg_fare_pd["Pclass"], avg_fare_pd["AverageFare"], color=['blue', 'green', 'red'])
plt.xlabel("Passenger Class")
plt.ylabel("Average Fare")
plt.title("Average Fare by Passenger Class")
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpr54gp096.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt
import seaborn as sns

df = pl.read_csv("data/titanic.csv")

# Group data by Pclass and calculate mean fare
mean_fare_by_class = df.groupby("Pclass").agg(pl.col("Fare").mean().alias("mean_fare"))

# Convert to pandas DataFrame for plotting
mean_fare_by_class_pd = mean_fare_by_class.to_pandas()

# Create bar chart
sns.barplot(data=mean_fare_by_class_pd, x="Pclass", y="mean_fare", palette="viridis")

# Add title
plt.title("Average Fare by Passenger Class")

# Show plot
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpsntftet6.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

df = pl.read_csv("data/titanic.csv")
avg_fare_by_class = df.groupby("Pclass").agg(pl.col("Fare").mean().alias("Average_Fare")).sort("Pclass")

plt.bar(avg_fare_by_class["Pclass"], avg_fare_by_class["Average_Fare"], color=['blue', 'orange', 'green'])
plt.xlabel("Passenger Class")
plt.ylabel("Average Fare")
plt.title("Average Fare by Passenger Class")
plt.xticks(avg_fare_by_class["Pclass"])
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpze1li1cb.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

# Load data
df = pl.read_csv("data/titanic.csv")

# Aggregate the average fare by passenger class
avg_fare_by_class = (
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean().alias("average_fare"))
    .sort("Pclass")
)

# Plotting
plt.figure(figsize=(8, 6))
plt.bar(
    avg_fare_by_class["Pclass"],
    avg_fare_by_class["average_fare"],
    color=["blue", "orange", "green"]
)
plt.xlabel('Passenger Class')
plt.ylabel('Average Fare')
plt.title('Average Fare by Passenger Class')
plt.xticks(avg_fare_by_class["Pclass"])
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0e8nic09.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt
import seaborn as sns

# Load the data
df = pl.read_csv("data/titanic.csv")

# Group by Pclass and calculate average fare
avg_fare_by_class = df.group_by('Pclass').agg(
    pl.col('Fare').mean().alias('Average Fare')
).sort('Pclass')

# Convert to pandas for plotting
pd_df = avg_fare_by_class.to_pandas()

# Create color palette - we'll use different colors for each class
colors = sns.color_palette("viridis", len(pd_df))

# Create the plot
plt.figure(figsize=(10, 6))
bars = plt.bar(pd_df['Pclass'], pd_df['Average Fare'], color=colors)

# Customize the plot
plt.title('Average Fare by Passenger Class', fontsize=16)
plt.xlabel('Passenger Class', fontsize=12)
plt.ylabel('Average Fare ($)', fontsize=12)
plt.xticks(pd_df['Pclass'])
plt.grid(axis='y', linestyle='--', alpha=0.7)

# Add value labels on top of each bar
for bar in bars:
    height = bar.get_height()
    plt.text(bar.get_x() + bar.get_width()/2., height + 2,
             f'${height:.2f}', ha='center', va='bottom')

plt.tight_layout()
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8v7__k7v.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt
import seaborn as sns

# Load the data
df = pl.read_csv("data/titanic.csv")

# Calculate average fare by passenger class
avg_fare_by_class = df.group_by('Pclass').agg(pl.col('Fare').mean().alias('Average_Fare')).sort('Pclass')

# Convert to pandas for plotting
avg_fare_pandas = avg_fare_by_class.to_pandas()

# Create a color map
colors = ['#3366cc', '#dc3912', '#ff9900']

# Create the bar chart
plt.figure(figsize=(10, 6))
bars = plt.bar(avg_fare_pandas['Pclass'], avg_fare_pandas['Average_Fare'], color=colors[:len(avg_fare_pandas)])

# Add labels and title
plt.xlabel('Passenger Class')
plt.ylabel('Average Fare ($)')
plt.title('Average Fare by Passenger Class')
plt.xticks(avg_fare_pandas['Pclass'])

# Show plot
plt.tight_layout()
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpsydl6d_j.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import matplotlib.pyplot as plt

# Load the data using Polars
df = pl.read_csv("data/titanic.csv")

# Calculate the average fare by passenger class
avg_fare_by_class = df.group_by("Pclass").agg(
    pl.col("Fare").mean().alias("AvgFare")
).sort("Pclass")

# Convert to pandas for easier plotting with matplotlib
pd_df = avg_fare_by_class.to_pandas()

# Create a color map for the bars
colors = ['#3274A1', '#E1812C', '#3A923A']

# Create the bar chart
plt.figure(figsize=(10, 6))
bars = plt.bar(pd_df['Pclass'], pd_df['AvgFare'], color=colors)

# Add labels and title
plt.xlabel('Passenger Class')
plt.ylabel('Average Fare ($)')
plt.title('Average Fare by Passenger Class')
plt.xticks(pd_df['Pclass'])

# Add a grid for better readability
plt.grid(axis='y', linestyle='--', alpha=0.7)

# Show the plot
plt.tight_layout()
plt.show()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmptcj6x9gq.py", line 4, in 
    import matplotlib.pyplot as plt
ModuleNotFoundError: No module named 'matplotlib'

Status: ✗ Failed

cumulative_sum

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Calculate the cumulative sum of the Fare column
# This will show the running total of fares as you go down the rows
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of calculating a cumulative sum:
# df.with_columns(pl.col("value").cum_sum().alias("cumulative_sum"))

api/gemini-2.0-flash

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").cumsum().alias("Fare_cumsum"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpl6iscpmj.py", line 5, in 
    df = df.with_columns(pl.col("Fare").cumsum().alias("Fare_cumsum"))
                         ^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'cumsum'. Did you mean: 'cum_sum'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.with_columns(pl.col("Fare").cum_sum().alias("cumulative_fare"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.with_columns(pl.col("Fare").cumsum().alias("cumulative_fare"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpyf50r7qi.py", line 5, in 
    df.with_columns(pl.col("Fare").cumsum().alias("cumulative_fare"))
                    ^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'cumsum'. Did you mean: 'cum_sum'?

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
dfWithIdentifier = df.with_column(pl.col("Fare").cumsum(). alias("CumulativeFare"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpb6s74fo6.py", line 7, in 
    dfWithIdentifier = df.with_column(pl.col("Fare").cumsum(). alias("CumulativeFare"))
                       ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df_cumulative_sum = df.with_columns(pl.col("Fare").cum_sum().alias("cumulative_sum"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.with_columns(pl.col("Fare").cumsum().alias("Cumulative_Fare"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp6vjzh22i.py", line 8, in 
    result = df.with_columns(pl.col("Fare").cumsum().alias("Cumulative_Fare"))
                             ^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'cumsum'. Did you mean: 'cum_sum'?

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.with_columns(pl.col("Fare").cum_sum().alias("Cumulative_Fare"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_with_cumsum = df.with_columns(pl.col("Fare").cum_sum().alias("Cumulative_Fare"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.with_columns(
    pl.col("Fare").cum_sum().alias("Cumulative_Fare")
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Calculate the cumulative sum of the Fare column
df_with_cumsum = df.with_columns(
    pl.col("Fare").cum_sum().alias("CumulativeFare")
)

print(df_with_cumsum)

Output:

shape: (891, 13)
┌─────────────┬──────────┬────────┬───────────────┬───┬─────────┬───────┬──────────┬───────────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name          ┆ … ┆ Fare    ┆ Cabin ┆ Embarked ┆ CumulativeFar │
│ ---         ┆ ---      ┆ ---    ┆ ---           ┆   ┆ ---     ┆ ---   ┆ ---      ┆ e             │
│ i64         ┆ i64      ┆ i64    ┆ str           ┆   ┆ f64     ┆ str   ┆ str      ┆ ---           │
│             ┆          ┆        ┆               ┆   ┆         ┆       ┆          ┆ f64           │
╞═════════════╪══════════╪════════╪═══════════════╪═══╪═════════╪═══════╪══════════╪═══════════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr.   ┆ … ┆ 7.25    ┆ null  ┆ S        ┆ 7.25          │
│             ┆          ┆        ┆ Owen Harris   ┆   ┆         ┆       ┆          ┆               │
│ 2           ┆ 1        ┆ 1      ┆ Cumings, Mrs. ┆ … ┆ 71.2833 ┆ C85   ┆ C        ┆ 78.5333       │
│             ┆          ┆        ┆ John Bradley  ┆   ┆         ┆       ┆          ┆               │
│             ┆          ┆        ┆ (Fl…          ┆   ┆         ┆       ┆          ┆               │
│ 3           ┆ 1        ┆ 3      ┆ Heikkinen,    ┆ … ┆ 7.925   ┆ null  ┆ S        ┆ 86.4583       │
│             ┆          ┆        ┆ Miss. Laina   ┆   ┆         ┆       ┆          ┆               │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle,     ┆ … ┆ 53.1    ┆ C123  ┆ S        ┆ 139.5583      │
│             ┆          ┆        ┆ Mrs. Jacques  ┆   ┆         ┆       ┆          ┆               │
│             ┆          ┆        ┆ Heath (…      ┆   ┆         ┆       ┆          ┆               │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr.    ┆ … ┆ 8.05    ┆ null  ┆ S        ┆ 147.6083      │
│             ┆          ┆        ┆ William Henry ┆   ┆         ┆       ┆          ┆               │
│ …           ┆ …        ┆ …      ┆ …             ┆ … ┆ …       ┆ …     ┆ …        ┆ …             │
│ 887         ┆ 0        ┆ 2      ┆ Montvila,     ┆ … ┆ 13.0    ┆ null  ┆ S        ┆ 28602.7493    │
│             ┆          ┆        ┆ Rev. Juozas   ┆   ┆         ┆       ┆          ┆               │
│ 888         ┆ 1        ┆ 1      ┆ Graham, Miss. ┆ … ┆ 30.0    ┆ B42   ┆ S        ┆ 28632.7493    │
│             ┆          ┆        ┆ Margaret      ┆   ┆         ┆       ┆          ┆               │
│             ┆          ┆        ┆ Edith         ┆   ┆         ┆       ┆          ┆               │
│ 889         ┆ 0        ┆ 3      ┆ Johnston,     ┆ … ┆ 23.45   ┆ null  ┆ S        ┆ 28656.1993    │
│             ┆          ┆        ┆ Miss.         ┆   ┆         ┆       ┆          ┆               │
│             ┆          ┆        ┆ Catherine     ┆   ┆         ┆       ┆          ┆               │
│             ┆          ┆        ┆ Hele…         ┆   ┆         ┆       ┆          ┆               │
│ 890         ┆ 1        ┆ 1      ┆ Behr, Mr.     ┆ … ┆ 30.0    ┆ C148  ┆ C        ┆ 28686.1993    │
│             ┆          ┆        ┆ Karl Howell   ┆   ┆         ┆       ┆          ┆               │
│ 891         ┆ 0        ┆ 3      ┆ Dooley, Mr.   ┆ … ┆ 7.75    ┆ null  ┆ Q        ┆ 28693.9493    │
│             ┆          ┆        ┆ Patrick       ┆   ┆         ┆       ┆          ┆               │
└─────────────┴──────────┴────────┴───────────────┴───┴─────────┴───────┴──────────┴───────────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Calculate the cumulative sum of the Fare column
df_with_cumsum = df.with_columns(pl.col("Fare").cum_sum().alias("Fare_CumulativeSum"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# First load the data using Polars as follows
df = pl.read_csv("data/titanic.csv")

# Calculate the cumulative sum of the Fare column
df_with_cumsum = df.with_columns(
    pl.col("Fare").cum_sum().alias("CumulativeFare")
)

# Display the result (optional)
print(df_with_cumsum.select(["PassengerId", "Fare", "CumulativeFare"]))

Output:

shape: (891, 3)
┌─────────────┬─────────┬────────────────┐
│ PassengerId ┆ Fare    ┆ CumulativeFare │
│ ---         ┆ ---     ┆ ---            │
│ i64         ┆ f64     ┆ f64            │
╞═════════════╪═════════╪════════════════╡
│ 1           ┆ 7.25    ┆ 7.25           │
│ 2           ┆ 71.2833 ┆ 78.5333        │
│ 3           ┆ 7.925   ┆ 86.4583        │
│ 4           ┆ 53.1    ┆ 139.5583       │
│ 5           ┆ 8.05    ┆ 147.6083       │
│ …           ┆ …       ┆ …              │
│ 887         ┆ 13.0    ┆ 28602.7493     │
│ 888         ┆ 30.0    ┆ 28632.7493     │
│ 889         ┆ 23.45   ┆ 28656.1993     │
│ 890         ┆ 30.0    ┆ 28686.1993     │
│ 891         ┆ 7.75    ┆ 28693.9493     │
└─────────────┴─────────┴────────────────┘

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

cumulative_fare = df.select(pl.col("Fare").cumsum().alias("CumulativeFare"))
print(cumulative_fare)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpu4_u6us5.py", line 7, in 
    cumulative_fare = df.select(pl.col("Fare").cumsum().alias("CumulativeFare"))
                                ^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'cumsum'. Did you mean: 'cum_sum'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Fare").cum_sum().alias("cumulative_fare"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").cumsum().alias("cumulative_fare")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpjh5qmnbu.py", line 8, in 
    pl.col("Fare").cumsum().alias("cumulative_fare")
    ^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'cumsum'. Did you mean: 'cum_sum'?

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Fare").cumsum().alias("Fare_cumsum"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpsqvyvh55.py", line 3, in 
    df = df.with_columns(pl.col("Fare").cumsum().alias("Fare_cumsum"))
                         ^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'cumsum'. Did you mean: 'cum_sum'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_with_cumulative_fare = df.with_columns(pl.col("Fare").cum_sum().alias("cumulative_fare"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Calculate the cumulative sum of the Fare column
df = df.with_columns(
    pl.col("Fare").cumsum().alias("Fare_cumsum")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9iibaf0a.py", line 9, in 
    pl.col("Fare").cumsum().alias("Fare_cumsum")
    ^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'cumsum'. Did you mean: 'cum_sum'?

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

explode_list_column

Original Prompt

# First create a DataFrame with a list column:
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

# Explode the scores list column to create a row for each score
# Sort by id and score

Suffix Prompt

# Here is an example of exploding lists: df.explode("scores").sort(["id", "scores"])

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

exploded_df = df.explode("scores").sort(["id", "scores"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})
df.explode("scores").sort(["id", "scores"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

df = df.explode("scores").sort(["id", "scores"])

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})
exploded_df = df.explode("scores").sort(["id", "scores"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})
df = df.explode("scores").sort(["id", "scores"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

df = df.explode("scores").sort(["id", "scores"])

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

df_exploded = df.explode("scores").sort(["id", "scores"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

result = df.explode("scores").sort(["id", "scores"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

result = df.explode("scores").sort(["id", "scores"])

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

exploded_df = df.explode("scores").sort(["id", "scores"])
print(exploded_df)

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

df = df.explode("scores")
df = df.sort(["id", "scores"])

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

exploded_df = df.explode("scores").sort(["id", "scores"])
print(exploded_df)

Output:

shape: (6, 2)
┌─────┬────────┐
│ id  ┆ scores │
│ --- ┆ ---    │
│ i64 ┆ i64    │
╞═════╪════════╡
│ 1   ┆ 10     │
│ 1   ┆ 20     │
│ 2   ┆ 30     │
│ 3   ┆ 40     │
│ 3   ┆ 50     │
│ 3   ┆ 60     │
└─────┴────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

df.explode("scores").sort(["id", "scores"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

df = df.explode("scores").sort(["id", "scores"])

print(df)

Output:

shape: (6, 2)
┌─────┬────────┐
│ id  ┆ scores │
│ --- ┆ ---    │
│ i64 ┆ i64    │
╞═════╪════════╡
│ 1   ┆ 10     │
│ 1   ┆ 20     │
│ 2   ┆ 30     │
│ 3   ┆ 40     │
│ 3   ┆ 50     │
│ 3   ┆ 60     │
└─────┴────────┘

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

exploded_df = df.explode("scores").sort(["id", "scores"])
print(exploded_df)

Output:

shape: (6, 2)
┌─────┬────────┐
│ id  ┆ scores │
│ --- ┆ ---    │
│ i64 ┆ i64    │
╞═════╪════════╡
│ 1   ┆ 10     │
│ 1   ┆ 20     │
│ 2   ┆ 30     │
│ 3   ┆ 40     │
│ 3   ┆ 50     │
│ 3   ┆ 60     │
└─────┴────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

result = df.explode("scores").sort(["id", "scores"])
print(result)

Output:

shape: (6, 2)
┌─────┬────────┐
│ id  ┆ scores │
│ --- ┆ ---    │
│ i64 ┆ i64    │
╞═════╪════════╡
│ 1   ┆ 10     │
│ 1   ┆ 20     │
│ 2   ┆ 30     │
│ 3   ┆ 40     │
│ 3   ┆ 50     │
│ 3   ┆ 60     │
└─────┴────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

exploded_df = df.explode("scores").sort(["id", "scores"])

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

exploded_df = df.explode("scores").sort(["id", "scores"])
print(exploded_df)

Output:

shape: (6, 2)
┌─────┬────────┐
│ id  ┆ scores │
│ --- ┆ ---    │
│ i64 ┆ i64    │
╞═════╪════════╡
│ 1   ┆ 10     │
│ 1   ┆ 20     │
│ 2   ┆ 30     │
│ 3   ┆ 40     │
│ 3   ┆ 50     │
│ 3   ┆ 60     │
└─────┴────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

result = df.explode("scores").sort(["id", "scores"])
print(result)

Output:

shape: (6, 2)
┌─────┬────────┐
│ id  ┆ scores │
│ --- ┆ ---    │
│ i64 ┆ i64    │
╞═════╪════════╡
│ 1   ┆ 10     │
│ 1   ┆ 20     │
│ 2   ┆ 30     │
│ 3   ┆ 40     │
│ 3   ┆ 50     │
│ 3   ┆ 60     │
└─────┴────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "scores": [[10, 20], [30], [40, 50, 60]]
})

result = df.explode("scores").sort(["id", "scores"])

Status: ✓ Success

exponential_weighted_mean

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Calculate an exponentially weighted mean of the Fare column with a span of 4
# The span parameter determines the decay rate of the weights
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of calculating an exponentially weighted mean:
# df.with_columns(pl.col("value").ewm_mean(span=4).alias("ewm"))

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
ewm = df.select(
    pl.col("Fare").ewm_mean(span=4, adjust=True)
).to_series()

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_with_ewm = df.with_columns(
    pl.col("Fare").ewm_mean(span=4).alias("Fare_EWM")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.with_columns(
    pl.col("Fare").ewm_mean(span=4).alias("Fare_EWM")
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

ewm_fare = df.select(
    pl.col("Fare").ewm(span=4).mean().alias("ewm_fare")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwxr53t2i.py", line 8, in 
    pl.col("Fare").ewm(span=4).mean().alias("ewm_fare")
    ^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'ewm'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Fare").ewm_mean(span=4).alias("Fare_ewm"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Calculate the exponentially weighted mean of the Fare column with a span of 4
df = df.with_columns(pl.col("Fare").ewm(span=4).mean().alias("fare_ewm"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpseismaka.py", line 9, in 
    df = df.with_columns(pl.col("Fare").ewm(span=4).mean().alias("fare_ewm"))
                         ^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'ewm'

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df['ewm_fare'] = df['Fare'].ewm(span=4).mean()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmppe5q9t9u.py", line 6, in 
    df['ewm_fare'] = df['Fare'].ewm(span=4).mean()
                     ^^^^^^^^^^^^^^
AttributeError: 'Series' object has no attribute 'ewm'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df_ewm = df.with_columns(pl.col("Fare").ewm_mean(span=4).alias("ewm_Fare"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").ewm(span=4).mean().alias("ewm_Fare"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpl_zkriq4.py", line 7, in 
    df = df.with_columns(pl.col("Fare").ewm(span=4).mean().alias("ewm_Fare"))
                         ^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'ewm'

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    pl.col("Fare").ewm_mean(span=4).alias("EWM_Fare")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Fare").ewm_mean(span=4).alias("Fare_ewm"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
ewm_fare = df.select(
    pl.col("Fare").ewm_mean(span=4).alias("Fare_ewm")
)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.select(
    pl.col("Fare").ewm_mean(span=4)
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.with_columns(pl.col("Fare").ewm_mean(span=4).alias("ewm_fare"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.select(
    pl.col("Fare").ewm_mean(span=4)
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Calculate an exponentially weighted mean of the Fare column with a span of 4
result = df.select(
    pl.col("Fare").ewm_mean(span=4).alias("Fare_EWM")
)

# If you want to add the result back to the original dataframe
df_with_ewm = df.with_column(
    pl.col("Fare").ewm_mean(span=4).alias("Fare_EWM")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpqo59dpo9.py", line 13, in 
    df_with_ewm = df.with_column(
                  ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Calculate an exponentially weighted mean of the Fare column with a span of 4
df_with_ewm = df.with_columns(
    pl.col("Fare").ewm_mean(span=4).alias("Fare_EWM")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").ewm_mean(span=4).alias("Fare_EWM")
)

Status: ✓ Success

filter_basic

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter a Polars DataFrame to show only passengers from first class (Pclass == 1)
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars syntax: df.filter(pl.col("id")=="a")

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
first_class = df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
first_class = df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

first_class = df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
first_class_passengers = df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter a Polars DataFrame to show only passengers from first class (Pclass == 1)
first_class_passengers = df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
first_class_passengers = df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
first_class_passengers = df.filter(pl.col('Pclass') == 1)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
first_class_passengers = df.filter(pl.col("Pclass")==1)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = pl.read_csv("data/titanic.csv")
first_class_passengers = df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

first_class_passengers = df.filter(pl.col("Pclass") == 1)

print(first_class_passengers)

Output:

shape: (216, 12)
┌─────────────┬──────────┬────────┬──────────────┬───┬──────────┬─────────┬─────────────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name         ┆ … ┆ Ticket   ┆ Fare    ┆ Cabin       ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---          ┆   ┆ ---      ┆ ---     ┆ ---         ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str          ┆   ┆ str      ┆ f64     ┆ str         ┆ str      │
╞═════════════╪══════════╪════════╪══════════════╪═══╪══════════╪═════════╪═════════════╪══════════╡
│ 2           ┆ 1        ┆ 1      ┆ Cumings,     ┆ … ┆ PC 17599 ┆ 71.2833 ┆ C85         ┆ C        │
│             ┆          ┆        ┆ Mrs. John    ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Bradley (Fl… ┆   ┆          ┆         ┆             ┆          │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle,    ┆ … ┆ 113803   ┆ 53.1    ┆ C123        ┆ S        │
│             ┆          ┆        ┆ Mrs. Jacques ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Heath (…     ┆   ┆          ┆         ┆             ┆          │
│ 7           ┆ 0        ┆ 1      ┆ McCarthy,    ┆ … ┆ 17463    ┆ 51.8625 ┆ E46         ┆ S        │
│             ┆          ┆        ┆ Mr. Timothy  ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ J            ┆   ┆          ┆         ┆             ┆          │
│ 12          ┆ 1        ┆ 1      ┆ Bonnell,     ┆ … ┆ 113783   ┆ 26.55   ┆ C103        ┆ S        │
│             ┆          ┆        ┆ Miss.        ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Elizabeth    ┆   ┆          ┆         ┆             ┆          │
│ 24          ┆ 1        ┆ 1      ┆ Sloper, Mr.  ┆ … ┆ 113788   ┆ 35.5    ┆ A6          ┆ S        │
│             ┆          ┆        ┆ William      ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Thompson     ┆   ┆          ┆         ┆             ┆          │
│ …           ┆ …        ┆ …      ┆ …            ┆ … ┆ …        ┆ …       ┆ …           ┆ …        │
│ 872         ┆ 1        ┆ 1      ┆ Beckwith,    ┆ … ┆ 11751    ┆ 52.5542 ┆ D35         ┆ S        │
│             ┆          ┆        ┆ Mrs. Richard ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Leonard…     ┆   ┆          ┆         ┆             ┆          │
│ 873         ┆ 0        ┆ 1      ┆ Carlsson,    ┆ … ┆ 695      ┆ 5.0     ┆ B51 B53 B55 ┆ S        │
│             ┆          ┆        ┆ Mr. Frans    ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Olof         ┆   ┆          ┆         ┆             ┆          │
│ 880         ┆ 1        ┆ 1      ┆ Potter, Mrs. ┆ … ┆ 11767    ┆ 83.1583 ┆ C50         ┆ C        │
│             ┆          ┆        ┆ Thomas Jr    ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ (Lily A…     ┆   ┆          ┆         ┆             ┆          │
│ 888         ┆ 1        ┆ 1      ┆ Graham,      ┆ … ┆ 112053   ┆ 30.0    ┆ B42         ┆ S        │
│             ┆          ┆        ┆ Miss.        ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Margaret     ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Edith        ┆   ┆          ┆         ┆             ┆          │
│ 890         ┆ 1        ┆ 1      ┆ Behr, Mr.    ┆ … ┆ 111369   ┆ 30.0    ┆ C148        ┆ C        │
│             ┆          ┆        ┆ Karl Howell  ┆   ┆          ┆         ┆             ┆          │
└─────────────┴──────────┴────────┴──────────────┴───┴──────────┴─────────┴─────────────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def filter_first_class(df: pl.DataFrame) -> pl.DataFrame:
    """
    Filters a Polars DataFrame to show only passengers from first class (Pclass == 1).

    Args:
        df: The input Polars DataFrame.

    Returns:
        A new Polars DataFrame containing only the first-class passengers.
    """
    return df.filter(pl.col("Pclass") == 1)

if __name__ == '__main__':
    df = pl.read_csv("data/titanic.csv")
    first_class_df = filter_first_class(df)
    print(first_class_df)

Output:

shape: (216, 12)
┌─────────────┬──────────┬────────┬──────────────┬───┬──────────┬─────────┬─────────────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name         ┆ … ┆ Ticket   ┆ Fare    ┆ Cabin       ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---          ┆   ┆ ---      ┆ ---     ┆ ---         ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str          ┆   ┆ str      ┆ f64     ┆ str         ┆ str      │
╞═════════════╪══════════╪════════╪══════════════╪═══╪══════════╪═════════╪═════════════╪══════════╡
│ 2           ┆ 1        ┆ 1      ┆ Cumings,     ┆ … ┆ PC 17599 ┆ 71.2833 ┆ C85         ┆ C        │
│             ┆          ┆        ┆ Mrs. John    ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Bradley (Fl… ┆   ┆          ┆         ┆             ┆          │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle,    ┆ … ┆ 113803   ┆ 53.1    ┆ C123        ┆ S        │
│             ┆          ┆        ┆ Mrs. Jacques ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Heath (…     ┆   ┆          ┆         ┆             ┆          │
│ 7           ┆ 0        ┆ 1      ┆ McCarthy,    ┆ … ┆ 17463    ┆ 51.8625 ┆ E46         ┆ S        │
│             ┆          ┆        ┆ Mr. Timothy  ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ J            ┆   ┆          ┆         ┆             ┆          │
│ 12          ┆ 1        ┆ 1      ┆ Bonnell,     ┆ … ┆ 113783   ┆ 26.55   ┆ C103        ┆ S        │
│             ┆          ┆        ┆ Miss.        ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Elizabeth    ┆   ┆          ┆         ┆             ┆          │
│ 24          ┆ 1        ┆ 1      ┆ Sloper, Mr.  ┆ … ┆ 113788   ┆ 35.5    ┆ A6          ┆ S        │
│             ┆          ┆        ┆ William      ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Thompson     ┆   ┆          ┆         ┆             ┆          │
│ …           ┆ …        ┆ …      ┆ …            ┆ … ┆ …        ┆ …       ┆ …           ┆ …        │
│ 872         ┆ 1        ┆ 1      ┆ Beckwith,    ┆ … ┆ 11751    ┆ 52.5542 ┆ D35         ┆ S        │
│             ┆          ┆        ┆ Mrs. Richard ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Leonard…     ┆   ┆          ┆         ┆             ┆          │
│ 873         ┆ 0        ┆ 1      ┆ Carlsson,    ┆ … ┆ 695      ┆ 5.0     ┆ B51 B53 B55 ┆ S        │
│             ┆          ┆        ┆ Mr. Frans    ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Olof         ┆   ┆          ┆         ┆             ┆          │
│ 880         ┆ 1        ┆ 1      ┆ Potter, Mrs. ┆ … ┆ 11767    ┆ 83.1583 ┆ C50         ┆ C        │
│             ┆          ┆        ┆ Thomas Jr    ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ (Lily A…     ┆   ┆          ┆         ┆             ┆          │
│ 888         ┆ 1        ┆ 1      ┆ Graham,      ┆ … ┆ 112053   ┆ 30.0    ┆ B42         ┆ S        │
│             ┆          ┆        ┆ Miss.        ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Margaret     ┆   ┆          ┆         ┆             ┆          │
│             ┆          ┆        ┆ Edith        ┆   ┆          ┆         ┆             ┆          │
│ 890         ┆ 1        ┆ 1      ┆ Behr, Mr.    ┆ … ┆ 111369   ┆ 30.0    ┆ C148        ┆ C        │
│             ┆          ┆        ┆ Karl Howell  ┆   ┆          ┆         ┆             ┆          │
└─────────────┴──────────┴────────┴──────────────┴───┴──────────┴─────────┴─────────────┴──────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def filter_first_class(df: pl.DataFrame) -> pl.DataFrame:
    """
    Filters a Polars DataFrame to show only passengers from first class (Pclass == 1).

    Args:
        df: The input Polars DataFrame.

    Returns:
        A Polars DataFrame containing only passengers from first class.
    """
    return df.filter(pl.col("Pclass") == 1)

if __name__ == '__main__':
    # Example Usage (assuming titanic.csv exists in a data directory)
    try:
        df = pl.read_csv("data/titanic.csv")
        first_class_df = filter_first_class(df)
        print(first_class_df)
    except FileNotFoundError:
        print("Error: titanic.csv not found in the data directory.  Please ensure the file exists.")

Output:

shape: (2, 12)
┌─────────────┬──────────┬────────┬───────┬───┬──────────┬───────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name  ┆ … ┆ Ticket   ┆ Fare  ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---   ┆   ┆ ---      ┆ ---   ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str   ┆   ┆ str      ┆ f64   ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪═══════╪═══╪══════════╪═══════╪═══════╪══════════╡
│ 2           ┆ 1        ┆ 1      ┆ test2 ┆ … ┆ PC 17599 ┆ 71.28 ┆ C85   ┆ C        │
│ 4           ┆ 1        ┆ 1      ┆ test4 ┆ … ┆ 113803   ┆ 53.1  ┆ C123  ┆ S        │
└─────────────┴──────────┴────────┴───────┴───┴──────────┴───────┴───────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

first_class_passengers = df.filter(pl.col("Pclass") == 1)

print(first_class_passengers)

Output:

shape: (2, 12)
┌─────────────┬──────────┬────────┬───────┬───┬──────────┬───────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name  ┆ … ┆ Ticket   ┆ Fare  ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---   ┆   ┆ ---      ┆ ---   ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str   ┆   ┆ str      ┆ f64   ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪═══════╪═══╪══════════╪═══════╪═══════╪══════════╡
│ 2           ┆ 1        ┆ 1      ┆ test2 ┆ … ┆ PC 17599 ┆ 71.28 ┆ C85   ┆ C        │
│ 4           ┆ 1        ┆ 1      ┆ test4 ┆ … ┆ 113803   ┆ 53.1  ┆ C123  ┆ S        │
└─────────────┴──────────┴────────┴───────┴───┴──────────┴───────┴───────┴──────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.filter(pl.col("Pclass") == 1)
print(result)

Output:

shape: (2, 12)
┌─────────────┬──────────┬────────┬───────┬───┬──────────┬───────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name  ┆ … ┆ Ticket   ┆ Fare  ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---   ┆   ┆ ---      ┆ ---   ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str   ┆   ┆ str      ┆ f64   ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪═══════╪═══╪══════════╪═══════╪═══════╪══════════╡
│ 2           ┆ 1        ┆ 1      ┆ test2 ┆ … ┆ PC 17599 ┆ 71.28 ┆ C85   ┆ C        │
│ 4           ┆ 1        ┆ 1      ┆ test4 ┆ … ┆ 113803   ┆ 53.1  ┆ C123  ┆ S        │
└─────────────┴──────────┴────────┴───────┴───┴──────────┴───────┴───────┴──────────┘

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

first_class_passengers = df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter to show only first class passengers
first_class_passengers = df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

first_class_passengers = df.filter(pl.col("Pclass") == 1)

Status: ✓ Success

filter_multiple_conditions

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter the Polars DataFrame to show passengers who are over 30 years old and in second class
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars syntax: df.filter(pl.col("id")=="a",pl.col("value")>10)

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter for passengers over 30 years old and in second class
filtered_df = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))
print(filtered_df)

Output:

shape: (77, 12)
┌─────────────┬──────────┬────────┬──────────────────┬───┬────────────┬─────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name             ┆ … ┆ Ticket     ┆ Fare    ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---              ┆   ┆ ---        ┆ ---     ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str              ┆   ┆ str        ┆ f64     ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════════════════╪═══╪════════════╪═════════╪═══════╪══════════╡
│ 16          ┆ 1        ┆ 2      ┆ Hewlett, Mrs.    ┆ … ┆ 248706     ┆ 16.0    ┆ null  ┆ S        │
│             ┆          ┆        ┆ (Mary D          ┆   ┆            ┆         ┆       ┆          │
│             ┆          ┆        ┆ Kingcome…        ┆   ┆            ┆         ┆       ┆          │
│ 21          ┆ 0        ┆ 2      ┆ Fynney, Mr.      ┆ … ┆ 239865     ┆ 26.0    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Joseph J         ┆   ┆            ┆         ┆       ┆          │
│ 22          ┆ 1        ┆ 2      ┆ Beesley, Mr.     ┆ … ┆ 248698     ┆ 13.0    ┆ D56   ┆ S        │
│             ┆          ┆        ┆ Lawrence         ┆   ┆            ┆         ┆       ┆          │
│ 34          ┆ 0        ┆ 2      ┆ Wheadon, Mr.     ┆ … ┆ C.A. 24579 ┆ 10.5    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Edward H         ┆   ┆            ┆         ┆       ┆          │
│ 71          ┆ 0        ┆ 2      ┆ Jenkin, Mr.      ┆ … ┆ C.A. 33111 ┆ 10.5    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Stephen Curnow   ┆   ┆            ┆         ┆       ┆          │
│ …           ┆ …        ┆ …      ┆ …                ┆ … ┆ …          ┆ …       ┆ …     ┆ …        │
│ 809         ┆ 0        ┆ 2      ┆ Meyer, Mr.       ┆ … ┆ 248723     ┆ 13.0    ┆ null  ┆ S        │
│             ┆          ┆        ┆ August           ┆   ┆            ┆         ┆       ┆          │
│ 813         ┆ 0        ┆ 2      ┆ Slemen, Mr.      ┆ … ┆ 28206      ┆ 10.5    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Richard James    ┆   ┆            ┆         ┆       ┆          │
│ 818         ┆ 0        ┆ 2      ┆ Mallet, Mr.      ┆ … ┆ S.C./PARIS ┆ 37.0042 ┆ null  ┆ C        │
│             ┆          ┆        ┆ Albert           ┆   ┆ 2079       ┆         ┆       ┆          │
│ 855         ┆ 0        ┆ 2      ┆ Carter, Mrs.     ┆ … ┆ 244252     ┆ 26.0    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Ernest Courtenay ┆   ┆            ┆         ┆       ┆          │
│             ┆          ┆        ┆ …                ┆   ┆            ┆         ┆       ┆          │
│ 866         ┆ 1        ┆ 2      ┆ Bystrom, Mrs.    ┆ … ┆ 236852     ┆ 13.0    ┆ null  ┆ S        │
│             ┆          ┆        ┆ (Karolina)       ┆   ┆            ┆         ┆       ┆          │
└─────────────┴──────────┴────────┴──────────────────┴───┴────────────┴─────────┴───────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

print(filtered_df)

Output:

shape: (0, 12)
┌─────────────┬──────────┬────────┬──────┬───┬────────┬──────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name ┆ … ┆ Ticket ┆ Fare ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---  ┆   ┆ ---    ┆ ---  ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str  ┆   ┆ str    ┆ f64  ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════╪═══╪════════╪══════╪═══════╪══════════╡
└─────────────┴──────────┴────────┴──────┴───┴────────┴──────┴───────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter(
    (pl.col("Age") > 30) & (pl.col("Pclass") == 2)
)

print(filtered_df)

Output:

shape: (0, 12)
┌─────────────┬──────────┬────────┬──────┬───┬────────┬──────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name ┆ … ┆ Ticket ┆ Fare ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---  ┆   ┆ ---    ┆ ---  ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str  ┆   ┆ str    ┆ f64  ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════╪═══╪════════╪══════╪═══════╪══════════╡
└─────────────┴──────────┴────────┴──────┴───┴────────┴──────┴───────┴──────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))
print(result)

Output:

shape: (0, 12)
┌─────────────┬──────────┬────────┬──────┬───┬────────┬──────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name ┆ … ┆ Ticket ┆ Fare ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---  ┆   ┆ ---    ┆ ---  ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str  ┆   ┆ str    ┆ f64  ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════╪═══╪════════╪══════╪═══════╪══════════╡
└─────────────┴──────────┴────────┴──────┴───┴────────┴──────┴───────┴──────────┘

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))
filtered_df

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter(pl.col("Age") > 30, pl.col("Pclass") == 2)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter the DataFrame
filtered_df = df.filter((pl.col("Age") > 30) & (pl.col("Pclass") == 2))

Status: ✓ Success

filter_or_condition

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter the Polars DataFrame to show passengers who are either over 70 years old OR in first class
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars syntax: df.filter(pl.col("id")=="a")

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter for passengers over 70 years old OR in first class
filtered_df = df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter passengers who are either over 70 years old OR in first class
result = df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def filter_dataframe(df: pl.DataFrame) -> pl.DataFrame:
    """
    Filters a Polars DataFrame to show passengers who are either over 70 years old OR in first class.

    Args:
        df: The input Polars DataFrame.

    Returns:
        A new Polars DataFrame containing the filtered passengers.
    """
    return df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

if __name__ == '__main__':
    # Load the data using Polars
    df = pl.read_csv("data/titanic.csv")

    # Filter the DataFrame
    filtered_df = filter_dataframe(df)

    # Print the filtered DataFrame (optional)
    print(filtered_df)

Output:

shape: (2, 12)
┌─────────────┬──────────┬────────┬───────┬───┬──────────┬───────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name  ┆ … ┆ Ticket   ┆ Fare  ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---   ┆   ┆ ---      ┆ ---   ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str   ┆   ┆ str      ┆ f64   ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪═══════╪═══╪══════════╪═══════╪═══════╪══════════╡
│ 2           ┆ 1        ┆ 1      ┆ test2 ┆ … ┆ PC 17599 ┆ 71.28 ┆ C85   ┆ C        │
│ 4           ┆ 1        ┆ 1      ┆ test4 ┆ … ┆ 113803   ┆ 53.1  ┆ C123  ┆ S        │
└─────────────┴──────────┴────────┴───────┴───┴──────────┴───────┴───────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_filtered = df.filter(
    (pl.col("Age") > 70) | (pl.col("Pclass") == 1)
)

print(df_filtered)

Output:

shape: (2, 12)
┌─────────────┬──────────┬────────┬───────┬───┬──────────┬───────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name  ┆ … ┆ Ticket   ┆ Fare  ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---   ┆   ┆ ---      ┆ ---   ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str   ┆   ┆ str      ┆ f64   ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪═══════╪═══╪══════════╪═══════╪═══════╪══════════╡
│ 2           ┆ 1        ┆ 1      ┆ test2 ┆ … ┆ PC 17599 ┆ 71.28 ┆ C85   ┆ C        │
│ 4           ┆ 1        ┆ 1      ┆ test4 ┆ … ┆ 113803   ┆ 53.1  ┆ C123  ┆ S        │
└─────────────┴──────────┴────────┴───────┴───┴──────────┴───────┴───────┴──────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter(
    (pl.col("Age") > 70) | (pl.col("Pclass") == 1)
)

print(filtered_df)

Output:

shape: (2, 12)
┌─────────────┬──────────┬────────┬───────┬───┬──────────┬───────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name  ┆ … ┆ Ticket   ┆ Fare  ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---   ┆   ┆ ---      ┆ ---   ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str   ┆   ┆ str      ┆ f64   ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪═══════╪═══╪══════════╪═══════╪═══════╪══════════╡
│ 2           ┆ 1        ┆ 1      ┆ test2 ┆ … ┆ PC 17599 ┆ 71.28 ┆ C85   ┆ C        │
│ 4           ┆ 1        ┆ 1      ┆ test4 ┆ … ┆ 113803   ┆ 53.1  ┆ C123  ┆ S        │
└─────────────┴──────────┴────────┴───────┴───┴──────────┴───────┴───────┴──────────┘

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.filter(
    (pl.col("Age") > 70) | (pl.col("Pclass") == 1)
)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.filter((pl.col("Age") > 70) | (pl.col("Pclass") == 1))

Status: ✓ Success

filter_range

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter the Polars DataFrame to show passengers between ages 10-20 (inclusive)
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars syntax: df.filter(pl.col("id").is_between(0,3))

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df_filtered = df.filter(
    (pl.col("Age") >= 10) & (pl.col("Age") <= 20)
)

print(df_filtered)

Output:

shape: (0, 12)
┌─────────────┬──────────┬────────┬──────┬───┬────────┬──────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name ┆ … ┆ Ticket ┆ Fare ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---  ┆   ┆ ---    ┆ ---  ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str  ┆   ┆ str    ┆ f64  ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════╪═══╪════════╪══════╪═══════╪══════════╡
└─────────────┴──────────┴────────┴──────┴───┴────────┴──────┴───────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter(pl.col("Age").is_between(10, 20))
print(filtered_df)

Output:

shape: (0, 12)
┌─────────────┬──────────┬────────┬──────┬───┬────────┬──────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name ┆ … ┆ Ticket ┆ Fare ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---  ┆   ┆ ---    ┆ ---  ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str  ┆   ┆ str    ┆ f64  ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════╪═══╪════════╪══════╪═══════╪══════════╡
└─────────────┴──────────┴────────┴──────┴───┴────────┴──────┴───────┴──────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.filter((pl.col("Age") >= 10) & (pl.col("Age") <= 20))
print(result)

Output:

shape: (0, 12)
┌─────────────┬──────────┬────────┬──────┬───┬────────┬──────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name ┆ … ┆ Ticket ┆ Fare ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---  ┆   ┆ ---    ┆ ---  ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str  ┆   ┆ str    ┆ f64  ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════╪═══╪════════╪══════╪═══════╪══════════╡
└─────────────┴──────────┴────────┴──────┴───┴────────┴──────┴───────┴──────────┘

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter((pl.col("Age") >= 10) & (pl.col("Age") <= 20))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.filter(pl.col("Age").is_between(10, 20, inclusive="both"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpkd4rzypa.py", line 5, in 
    df.filter(pl.col("Age").is_between(10, 20, inclusive="both"))
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Expr.is_between() got an unexpected keyword argument 'inclusive'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.filter(pl.col("Age").is_between(10, 20, inclusive="both"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfy8ofa6k.py", line 5, in 
    df.filter(pl.col("Age").is_between(10, 20, inclusive="both"))
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Expr.is_between() got an unexpected keyword argument 'inclusive'

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((df["Age"] >= 10) & (df["Age"] <= 20))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter(pl.col("Age").is_between(10, 20))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.filter(pl.col("Age").is_between(10, 20))

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("Age") >= 10) & (pl.col("Age") <= 20))
print(filtered_df)

Output:

shape: (117, 12)
┌─────────────┬──────────┬────────┬─────────────┬───┬───────────┬─────────┬─────────────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name        ┆ … ┆ Ticket    ┆ Fare    ┆ Cabin       ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---         ┆   ┆ ---       ┆ ---     ┆ ---         ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str         ┆   ┆ str       ┆ f64     ┆ str         ┆ str      │
╞═════════════╪══════════╪════════╪═════════════╪═══╪═══════════╪═════════╪═════════════╪══════════╡
│ 10          ┆ 1        ┆ 2      ┆ Nasser,     ┆ … ┆ 237736    ┆ 30.0708 ┆ null        ┆ C        │
│             ┆          ┆        ┆ Mrs.        ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ Nicholas    ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ (Adele A…   ┆   ┆           ┆         ┆             ┆          │
│ 13          ┆ 0        ┆ 3      ┆ Saundercock ┆ … ┆ A/5. 2151 ┆ 8.05    ┆ null        ┆ S        │
│             ┆          ┆        ┆ , Mr.       ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ William     ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ Henry       ┆   ┆           ┆         ┆             ┆          │
│ 15          ┆ 0        ┆ 3      ┆ Vestrom,    ┆ … ┆ 350406    ┆ 7.8542  ┆ null        ┆ S        │
│             ┆          ┆        ┆ Miss. Hulda ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ Amanda Ad…  ┆   ┆           ┆         ┆             ┆          │
│ 23          ┆ 1        ┆ 3      ┆ McGowan,    ┆ … ┆ 330923    ┆ 8.0292  ┆ null        ┆ Q        │
│             ┆          ┆        ┆ Miss. Anna  ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ "Annie"     ┆   ┆           ┆         ┆             ┆          │
│ 28          ┆ 0        ┆ 1      ┆ Fortune,    ┆ … ┆ 19950     ┆ 263.0   ┆ C23 C25 C27 ┆ S        │
│             ┆          ┆        ┆ Mr. Charles ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ Alexander   ┆   ┆           ┆         ┆             ┆          │
│ …           ┆ …        ┆ …      ┆ …           ┆ … ┆ …         ┆ …       ┆ …           ┆ …        │
│ 856         ┆ 1        ┆ 3      ┆ Aks, Mrs.   ┆ … ┆ 392091    ┆ 9.35    ┆ null        ┆ S        │
│             ┆          ┆        ┆ Sam (Leah   ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ Rosen)      ┆   ┆           ┆         ┆             ┆          │
│ 876         ┆ 1        ┆ 3      ┆ Najib,      ┆ … ┆ 2667      ┆ 7.225   ┆ null        ┆ C        │
│             ┆          ┆        ┆ Miss. Adele ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ Kiamie      ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ "Jan…       ┆   ┆           ┆         ┆             ┆          │
│ 877         ┆ 0        ┆ 3      ┆ Gustafsson, ┆ … ┆ 7534      ┆ 9.8458  ┆ null        ┆ S        │
│             ┆          ┆        ┆ Mr. Alfred  ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ Ossian      ┆   ┆           ┆         ┆             ┆          │
│ 878         ┆ 0        ┆ 3      ┆ Petroff,    ┆ … ┆ 349212    ┆ 7.8958  ┆ null        ┆ S        │
│             ┆          ┆        ┆ Mr. Nedelio ┆   ┆           ┆         ┆             ┆          │
│ 888         ┆ 1        ┆ 1      ┆ Graham,     ┆ … ┆ 112053    ┆ 30.0    ┆ B42         ┆ S        │
│             ┆          ┆        ┆ Miss.       ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ Margaret    ┆   ┆           ┆         ┆             ┆          │
│             ┆          ┆        ┆ Edith       ┆   ┆           ┆         ┆             ┆          │
└─────────────┴──────────┴────────┴─────────────┴───┴───────────┴─────────┴─────────────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter passengers between ages 10-20 (inclusive)
filtered_df = df.filter(pl.col("Age").is_between(10, 20))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter the DataFrame to show passengers between ages 10-20 (inclusive)
filtered_df = df.filter(pl.col("Age").is_between(10, 20))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_filtered = df.filter((df["Age"] >= 10) & (df["Age"] <= 20))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter(pl.col("Age").is_between(10, 20))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.filter(pl.col("Age").is_between(10, 20))

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Age") >= 10 & pl.col("Age") <= 20)

Error:

TypeError(msg)
TypeError: the truth value of an Expr is ambiguous

You probably got here by using a Python standard library function instead of the native expressions API.
Here are some things you might want to try:
- instead of `pl.col('a') and pl.col('b')`, use `pl.col('a') & pl.col('b')`
- instead of `pl.col('a') in [y, z]`, use `pl.col('a').is_in([y, z])`
- instead of `max(pl.col('a'), pl.col('b'))`, use `pl.max_horizontal(pl.col('a'), pl.col('b'))`

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Age").is-between(10,20))

Error:

invalid syntax (, line 6)

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code:


import polars as pl

df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Age").is_between(10, 20))

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter((pl.col("Age") >= 10) & (pl.col("Age") <= 20))
filtered_df

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter(pl.col("Age").is_between(10, 20))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.filter(pl.col("Age").is_between(10, 20, closed="both"))

Status: ✓ Success

filter_with_is_in

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter the DataFrame to show only passengers from this list of classes: 2, 3
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of using is_in: df.filter(pl.col("category").is_in(["A", "B"]))

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df_filtered = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

classes = [2, 3]
filtered_df = df.filter(pl.col("Pclass").is_in(classes))

print(filtered_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmphap7_hbf.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_filtered = df.filter(pl.col("Pclass").is_in([2, 3]))

print(df_filtered)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdys5ni_2.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.filter(pl.col("Pclass").is_in([2, 3]))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdoglzl1y.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.filter(pl.col("Pclass").is_in([2, 3]))

Status: ✓ Success

filter_with_negation

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter the DataFrame to show all passengers who are NOT in first class (Pclass != 1)
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of negating a filter: df.filter(pl.col("category").eq("value").not_())

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.filter(pl.col("Pclass") != 1)
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8vlfsudy.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.filter(pl.col("Pclass") != 1)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpf2nbd8lf.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter(pl.col("Pclass") != 1)
print(filtered_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpvf_3x_os.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

not_first_class = df.filter(pl.col("Pclass") != 1)
print(not_first_class)

Output:

shape: (675, 12)
┌─────────────┬──────────┬────────┬───────────────────┬───┬────────────┬────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name              ┆ … ┆ Ticket     ┆ Fare   ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---               ┆   ┆ ---        ┆ ---    ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str               ┆   ┆ str        ┆ f64    ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪═══════════════════╪═══╪════════════╪════════╪═══════╪══════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr. Owen  ┆ … ┆ A/5 21171  ┆ 7.25   ┆ null  ┆ S        │
│             ┆          ┆        ┆ Harris            ┆   ┆            ┆        ┆       ┆          │
│ 3           ┆ 1        ┆ 3      ┆ Heikkinen, Miss.  ┆ … ┆ STON/O2.   ┆ 7.925  ┆ null  ┆ S        │
│             ┆          ┆        ┆ Laina             ┆   ┆ 3101282    ┆        ┆       ┆          │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr.        ┆ … ┆ 373450     ┆ 8.05   ┆ null  ┆ S        │
│             ┆          ┆        ┆ William Henry     ┆   ┆            ┆        ┆       ┆          │
│ 6           ┆ 0        ┆ 3      ┆ Moran, Mr. James  ┆ … ┆ 330877     ┆ 8.4583 ┆ null  ┆ Q        │
│ 8           ┆ 0        ┆ 3      ┆ Palsson, Master.  ┆ … ┆ 349909     ┆ 21.075 ┆ null  ┆ S        │
│             ┆          ┆        ┆ Gosta Leonard     ┆   ┆            ┆        ┆       ┆          │
│ …           ┆ …        ┆ …      ┆ …                 ┆ … ┆ …          ┆ …      ┆ …     ┆ …        │
│ 885         ┆ 0        ┆ 3      ┆ Sutehall, Mr.     ┆ … ┆ SOTON/OQ   ┆ 7.05   ┆ null  ┆ S        │
│             ┆          ┆        ┆ Henry Jr          ┆   ┆ 392076     ┆        ┆       ┆          │
│ 886         ┆ 0        ┆ 3      ┆ Rice, Mrs.        ┆ … ┆ 382652     ┆ 29.125 ┆ null  ┆ Q        │
│             ┆          ┆        ┆ William (Margaret ┆   ┆            ┆        ┆       ┆          │
│             ┆          ┆        ┆ N…                ┆   ┆            ┆        ┆       ┆          │
│ 887         ┆ 0        ┆ 2      ┆ Montvila, Rev.    ┆ … ┆ 211536     ┆ 13.0   ┆ null  ┆ S        │
│             ┆          ┆        ┆ Juozas            ┆   ┆            ┆        ┆       ┆          │
│ 889         ┆ 0        ┆ 3      ┆ Johnston, Miss.   ┆ … ┆ W./C. 6607 ┆ 23.45  ┆ null  ┆ S        │
│             ┆          ┆        ┆ Catherine Hele…   ┆   ┆            ┆        ┆       ┆          │
│ 891         ┆ 0        ┆ 3      ┆ Dooley, Mr.       ┆ … ┆ 370376     ┆ 7.75   ┆ null  ┆ Q        │
│             ┆          ┆        ┆ Patrick           ┆   ┆            ┆        ┆       ┆          │
└─────────────┴──────────┴────────┴───────────────────┴───┴────────────┴────────┴───────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter to exclude first class passengers
not_first_class = df.filter(pl.col("Pclass").eq(1).not_())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter passengers who are not in first class
not_first_class = df.filter(pl.col("Pclass") != 1)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter(pl.col("Pclass") != 1)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_filtered = df.filter(pl.col("Pclass").eq(1).not_())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.filter(pl.col("Pclass") != 1)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
not_first_class = df.filter(pl.col("Pclass")!= 1)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
not_first_class = df.filter(pl.col("Pclass").eq(1).not_())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code for the given task:

import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter(pl.col("Pclass")!= 1)

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_filtered = df.filter(pl.col("Pclass") != 1)
print(df_filtered)

Output:

shape: (675, 12)
┌─────────────┬──────────┬────────┬───────────────────┬───┬────────────┬────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name              ┆ … ┆ Ticket     ┆ Fare   ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---               ┆   ┆ ---        ┆ ---    ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str               ┆   ┆ str        ┆ f64    ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪═══════════════════╪═══╪════════════╪════════╪═══════╪══════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr. Owen  ┆ … ┆ A/5 21171  ┆ 7.25   ┆ null  ┆ S        │
│             ┆          ┆        ┆ Harris            ┆   ┆            ┆        ┆       ┆          │
│ 3           ┆ 1        ┆ 3      ┆ Heikkinen, Miss.  ┆ … ┆ STON/O2.   ┆ 7.925  ┆ null  ┆ S        │
│             ┆          ┆        ┆ Laina             ┆   ┆ 3101282    ┆        ┆       ┆          │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr.        ┆ … ┆ 373450     ┆ 8.05   ┆ null  ┆ S        │
│             ┆          ┆        ┆ William Henry     ┆   ┆            ┆        ┆       ┆          │
│ 6           ┆ 0        ┆ 3      ┆ Moran, Mr. James  ┆ … ┆ 330877     ┆ 8.4583 ┆ null  ┆ Q        │
│ 8           ┆ 0        ┆ 3      ┆ Palsson, Master.  ┆ … ┆ 349909     ┆ 21.075 ┆ null  ┆ S        │
│             ┆          ┆        ┆ Gosta Leonard     ┆   ┆            ┆        ┆       ┆          │
│ …           ┆ …        ┆ …      ┆ …                 ┆ … ┆ …          ┆ …      ┆ …     ┆ …        │
│ 885         ┆ 0        ┆ 3      ┆ Sutehall, Mr.     ┆ … ┆ SOTON/OQ   ┆ 7.05   ┆ null  ┆ S        │
│             ┆          ┆        ┆ Henry Jr          ┆   ┆ 392076     ┆        ┆       ┆          │
│ 886         ┆ 0        ┆ 3      ┆ Rice, Mrs.        ┆ … ┆ 382652     ┆ 29.125 ┆ null  ┆ Q        │
│             ┆          ┆        ┆ William (Margaret ┆   ┆            ┆        ┆       ┆          │
│             ┆          ┆        ┆ N…                ┆   ┆            ┆        ┆       ┆          │
│ 887         ┆ 0        ┆ 2      ┆ Montvila, Rev.    ┆ … ┆ 211536     ┆ 13.0   ┆ null  ┆ S        │
│             ┆          ┆        ┆ Juozas            ┆   ┆            ┆        ┆       ┆          │
│ 889         ┆ 0        ┆ 3      ┆ Johnston, Miss.   ┆ … ┆ W./C. 6607 ┆ 23.45  ┆ null  ┆ S        │
│             ┆          ┆        ┆ Catherine Hele…   ┆   ┆            ┆        ┆       ┆          │
│ 891         ┆ 0        ┆ 3      ┆ Dooley, Mr.       ┆ … ┆ 370376     ┆ 7.75   ┆ null  ┆ Q        │
│             ┆          ┆        ┆ Patrick           ┆   ┆            ┆        ┆       ┆          │
└─────────────┴──────────┴────────┴───────────────────┴───┴────────────┴────────┴───────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.filter(pl.col("Pclass").ne(1))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.filter(pl.col("Pclass") != 1)

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Pclass") != 1)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter(pl.col("Pclass").neq(1))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpmhieqp3m.py", line 3, in 
    filtered_df = df.filter(pl.col("Pclass").neq(1))
                            ^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'neq'. Did you mean: 'eq'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Pclass") != 1)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Pclass") != 1)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Pclass").eq(1).not_())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.filter(pl.col("Pclass") != 1)

Status: ✓ Success

get_string_length

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Get the length of the Name column
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of getting string length: df.select(pl.col("value").str.len_chars())

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

length_of_name = len(df["Name"])

print(length_of_name)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpqm9q8bd3.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

name_length = df.select(pl.col("Name").str.len_chars())
print(name_length)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpsi2go6uh.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

print(df.select(pl.col("Name").str.len_chars()))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpghg80xqu.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select(pl.col("Name").str.lengths()).to_series().to_list()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpiieq59kg.py", line 3, in 
    df.select(pl.col("Name").str.lengths()).to_series().to_list()
              ^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'ExprStringNameSpace' object has no attribute 'lengths'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select(pl.col("Name").str.len_chars())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.col("Name").str.len_chars())

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Get the length of the Name column
df = df.with_column(pl.col("Name").str.len_chars().alias("NameLength"))

# Display the result
print(df.select(["Name", "NameLength"]).head())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpiqn1b6bw.py", line 7, in 
    df = df.with_column(pl.col("Name").str.len_chars().alias("NameLength"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Get the length of the Name column
name_lengths = df.select(pl.col("Name").str.len_chars().alias("NameLength"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Get the length of the Name column
df.with_columns(
    pl.col("Name").str.len_chars().alias("Name_Length")
)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
name_length = df["Name"].len()

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.col("Name").str.len_chars())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.col("Name").str.len_chars())

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
name_length = df["Name"].str.lengths()
print(name_length)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpb4nduftr.py", line 3, in 
    name_length = df["Name"].str.lengths()
                  ^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'StringNameSpace' object has no attribute 'lengths'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
name_lengths = df.select(pl.col("Name").str.len_chars())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.col("Name").str.len_chars())

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
name_length = df.select(pl.col("Name").str.lengths()).collect()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpxzdps1b9.py", line 6, in 
    name_length = df.select(pl.col("Name").str.lengths()).collect()
                            ^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'ExprStringNameSpace' object has no attribute 'lengths'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
name_length = df.select(pl.col("Name").str.len_chars())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
print(df.select(pl.col("Name").str.len_chars()).collect())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpmbod3vgk.py", line 6, in 
    print(df.select(pl.col("Name").str.len_chars()).collect())
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'collect'

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

name_length = df["Name"].str.lengths().to_list()
print(len(name_length))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9xv6x0kj.py", line 6, in 
    name_length = df["Name"].str.lengths().to_list()
                  ^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'StringNameSpace' object has no attribute 'lengths'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

name_lengths = df.select(pl.col("Name").str.len_chars())
print(name_lengths)

Output:

shape: (891, 1)
┌──────┐
│ Name │
│ ---  │
│ u32  │
╞══════╡
│ 23   │
│ 51   │
│ 22   │
│ 44   │
│ 24   │
│ …    │
│ 21   │
│ 28   │
│ 40   │
│ 21   │
│ 19   │
└──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
print(df.select(pl.col("Name").str.len_chars()).collect())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpko35ngm8.py", line 5, in 
    print(df.select(pl.col("Name").str.len_chars()).collect())
          ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'collect'

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

groupby_agg_single_column

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Group the data by Pclass and calculate the min,mean,max Age for each class
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars group-by syntax: df.group_by("id").agg(pl.col("value").mean().name.suffix("_mean"))

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.groupby("Pclass").agg([
    pl.col("Age").min().alias("min_age"),
    pl.col("Age").mean().alias("mean_age"),
    pl.col("Age").max().alias("max_age")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwnctg5n2.py", line 3, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.group_by("Pclass").agg([
    pl.col("Age").min().alias("Age_min"),
    pl.col("Age").mean().alias("Age_mean"),
    pl.col("Age").max().alias("Age_max")
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.col("Age").min().alias("min_age"),
    pl.col("Age").mean().alias("mean_age"),
    pl.col("Age").max().alias("max_age")
)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass").agg([
    pl.col("Age").min().alias("Min_Age"),
    pl.col("Age").mean().alias("Mean_Age"),
    pl.col("Age").max().alias("Max_Age")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpf7ztm2te.py", line 7, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.group_by("Pclass").agg([
    pl.col("Age").min().alias("Age_min"),
    pl.col("Age").mean().alias("Age_mean"),
    pl.col("Age").max().alias("Age_max")
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.group_by("Pclass").agg([
    pl.col("Age").min().alias("min_age"),
    pl.col("Age").mean().alias("mean_age"), 
    pl.col("Age").max().alias("max_age")
])

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.groupby("Pclass").agg([
    pl.col("Age").min().alias("min_Age"),
    pl.col("Age").mean().alias("mean_Age"),
    pl.col("Age").max().alias("max_Age")
])
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp989xw2_9.py", line 7, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").min().name("min_Age"),
    pl.col("Age").mean().name("mean_Age"),
    pl.col("Age").max().name("max_Age")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpficznlyt.py", line 8, in 
    pl.col("Age").min().name("min_Age"),
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: 'ExprNameNameSpace' object is not callable

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code for the task:

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").min().alias("min_Age"),
    pl.col("Age").mean().alias("mean_Age"),
    pl.col("Age").max().alias("max_Age")
])

print(result)

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    [
        pl.col("Age").min(),
        pl.col("Age").mean(),
        pl.col("Age").max(),
    ]
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpi68m0k7j.py", line 7, in 
    result = df.group_by("Pclass").agg(
             ^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/group_by.py", line 232, in agg
    .collect(no_optimization=True)
     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.DuplicateError: column with name 'Age' has more than one occurrence

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'group_by' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").min().name.suffix("_min"),
    pl.col("Age").mean().name.suffix("_mean"),
    pl.col("Age").max().name.suffix("_max")
])

print(result)

Output:

shape: (3, 4)
┌────────┬─────────┬───────────┬─────────┐
│ Pclass ┆ Age_min ┆ Age_mean  ┆ Age_max │
│ ---    ┆ ---     ┆ ---       ┆ ---     │
│ i64    ┆ f64     ┆ f64       ┆ f64     │
╞════════╪═════════╪═══════════╪═════════╡
│ 2      ┆ 0.67    ┆ 29.87763  ┆ 70.0    │
│ 1      ┆ 0.92    ┆ 38.233441 ┆ 80.0    │
│ 3      ┆ 0.42    ┆ 25.14062  ┆ 74.0    │
└────────┴─────────┴───────────┴─────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Group by Pclass and calculate min, mean, and max Age
result = (
    df.group_by("Pclass")
    .agg(
        pl.col("Age").min().name.suffix("_min"),
        pl.col("Age").mean().name.suffix("_mean"),
        pl.col("Age").max().name.suffix("_max"),
    )
)

print(result)

Output:

shape: (3, 4)
┌────────┬─────────┬───────────┬─────────┐
│ Pclass ┆ Age_min ┆ Age_mean  ┆ Age_max │
│ ---    ┆ ---     ┆ ---       ┆ ---     │
│ i64    ┆ f64     ┆ f64       ┆ f64     │
╞════════╪═════════╪═══════════╪═════════╡
│ 2      ┆ 0.67    ┆ 29.87763  ┆ 70.0    │
│ 3      ┆ 0.42    ┆ 25.14062  ┆ 74.0    │
│ 1      ┆ 0.92    ┆ 38.233441 ┆ 80.0    │
└────────┴─────────┴───────────┴─────────┘

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg(
    [
        pl.col("Age").min().alias("min_age"),
        pl.col("Age").mean().alias("mean_age"),
        pl.col("Age").max().alias("max_age")
    ]
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.col("Age").min().alias("Age_min"),
    pl.col("Age").mean().alias("Age_mean"),
    pl.col("Age").max().alias("Age_max")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.col("Age").min().alias("min_age"),
    pl.col("Age").mean().alias("mean_age"),
    pl.col("Age").max().alias("max_age")
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.col("Age").min().alias("Min_Age"),
    pl.col("Age").mean().alias("Mean_Age"),
    pl.col("Age").max().alias("Max_Age")
)

print(result)

Output:

shape: (3, 4)
┌────────┬─────────┬───────────┬─────────┐
│ Pclass ┆ Min_Age ┆ Mean_Age  ┆ Max_Age │
│ ---    ┆ ---     ┆ ---       ┆ ---     │
│ i64    ┆ f64     ┆ f64       ┆ f64     │
╞════════╪═════════╪═══════════╪═════════╡
│ 2      ┆ 0.67    ┆ 29.87763  ┆ 70.0    │
│ 1      ┆ 0.92    ┆ 38.233441 ┆ 80.0    │
│ 3      ┆ 0.42    ┆ 25.14062  ┆ 74.0    │
└────────┴─────────┴───────────┴─────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").min().alias("Age_min"),
    pl.col("Age").mean().alias("Age_mean"),
    pl.col("Age").max().alias("Age_max")
])

print(result)

Output:

shape: (3, 4)
┌────────┬─────────┬───────────┬─────────┐
│ Pclass ┆ Age_min ┆ Age_mean  ┆ Age_max │
│ ---    ┆ ---     ┆ ---       ┆ ---     │
│ i64    ┆ f64     ┆ f64       ┆ f64     │
╞════════╪═════════╪═══════════╪═════════╡
│ 2      ┆ 0.67    ┆ 29.87763  ┆ 70.0    │
│ 1      ┆ 0.92    ┆ 38.233441 ┆ 80.0    │
│ 3      ┆ 0.42    ┆ 25.14062  ┆ 74.0    │
└────────┴─────────┴───────────┴─────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.col("Age").min().alias("min_age"),
    pl.col("Age").mean().alias("mean_age"),
    pl.col("Age").max().alias("max_age")
)

print(result)

Output:

shape: (3, 4)
┌────────┬─────────┬───────────┬─────────┐
│ Pclass ┆ min_age ┆ mean_age  ┆ max_age │
│ ---    ┆ ---     ┆ ---       ┆ ---     │
│ i64    ┆ f64     ┆ f64       ┆ f64     │
╞════════╪═════════╪═══════════╪═════════╡
│ 3      ┆ 0.42    ┆ 25.14062  ┆ 74.0    │
│ 1      ┆ 0.92    ┆ 38.233441 ┆ 80.0    │
│ 2      ┆ 0.67    ┆ 29.87763  ┆ 70.0    │
└────────┴─────────┴───────────┴─────────┘

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")

    result = df.group_by("Pclass").agg([
        pl.col("Age").min().alias("min_age"),
        pl.col("Age").mean().alias("mean_age"),
        pl.col("Age").max().alias("max_age")
    ])

    return result

if __name__ == "__main__":
    # Create a dummy titanic.csv for testing purposes.
    data = """PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,"Braund, Mr. Owen Harris",male,22.0,1,0,A/5 21171,7.25,,S
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38.0,1,0,PC 17599,71.2833,C85,C
3,1,3,"Heikkinen, Miss. Laina",female,26.0,0,0,STON/O2. 3101282,7.925,,S
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35.0,1,0,113803,53.1,C123,S
5,0,3,"Allen, Mr. William Henry",male,35.0,0,0,373450,8.05,,S
6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,Q
7,0,1,"McCarthy, Mr. Timothy J",male,54.0,0,0,17463,51.8625,E46,S
8,0,3,"Palsson, Master. Gosta Leonard",male,2.0,3,1,349909,21.075,,S
9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27.0,0,2,347742,11.1333,,S
10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14.0,1,0,237736,30.0708,,C"""

    with open("data/titanic.csv", "w") as f:
        f.write(data)
    
    print(solve())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9r9x9c1x.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    [
        pl.col("Age").min().alias("Age_min"),
        pl.col("Age").mean().alias("Age_mean"),
        pl.col("Age").max().alias("Age_max"),
    ]
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpegzhdyon.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    [
        pl.col("Age").min().alias("min_age"),
        pl.col("Age").mean().alias("mean_age"),
        pl.col("Age").max().alias("max_age"),
    ]
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1ihugcno.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

groupby_maintain_order

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Group by Pclass and calculate the average Fare, but maintain the original order of Pclass values
# The output should have the same order of Pclass as they first appear in the original data
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of maintaining order in groupby: df.group_by("group", maintain_order=True).agg(pl.col("value").mean())

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Get the order of Pclass as they first appear
pclass_order = df.select("Pclass").unique().sort(by="Pclass").to_series().to_list()

# Group by Pclass and calculate average Fare
result = df.group_by("Pclass").agg(
    pl.col("Fare").mean().alias("Average_Fare")
).sort(by="Pclass")

# Display the result
print(result)

Output:

shape: (3, 2)
┌────────┬──────────────┐
│ Pclass ┆ Average_Fare │
│ ---    ┆ ---          │
│ i64    ┆ f64          │
╞════════╪══════════════╡
│ 1      ┆ 84.154687    │
│ 2      ┆ 20.662183    │
│ 3      ┆ 13.67555     │
└────────┴──────────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

avg_fare_by_class = df.group_by("Pclass", maintain_order=True).agg(
    pl.col("Fare").mean().alias("Average_Fare")
)

print(avg_fare_by_class)

Output:

shape: (3, 2)
┌────────┬──────────────┐
│ Pclass ┆ Average_Fare │
│ ---    ┆ ---          │
│ i64    ┆ f64          │
╞════════╪══════════════╡
│ 3      ┆ 13.67555     │
│ 1      ┆ 84.154687    │
│ 2      ┆ 20.662183    │
└────────┴──────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Get the unique Pclass values in their original order of appearance
pclass_order = df.select("Pclass").unique(maintain_order=True)

# Group by Pclass and calculate average Fare
result = df.group_by("Pclass").agg(
    pl.col("Fare").mean().alias("Average_Fare")
)

# Join with the pclass_order to maintain original order
final_result = pclass_order.join(result, on="Pclass", how="left")

print(final_result)

Output:

shape: (3, 2)
┌────────┬──────────────┐
│ Pclass ┆ Average_Fare │
│ ---    ┆ ---          │
│ i64    ┆ f64          │
╞════════╪══════════════╡
│ 3      ┆ 13.67555     │
│ 1      ┆ 84.154687    │
│ 2      ┆ 20.662183    │
└────────┴──────────────┘

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass", maintain_order=True)
    .agg(pl.col("Fare").mean())
    .sort("Pclass")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass", maintain_order=True).agg(pl.col("Fare").mean())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass", maintain_order=True)
    .agg(pl.col("Fare").mean())
)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (df
    .select(['Pclass', 'Fare'])
    .group_by('Pclass', maintain_order=True)
    .agg(pl.mean('Fare'))
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass", maintain_order=True).agg(
    pl.col("Fare").mean().alias("Average_Fare")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.select(["Pclass", "Fare"])
    .group_by("Pclass")
    .agg(pl.col("Fare").mean())
    .sort(by="Pclass", maintain_order=True)
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Group by 'Pclass' while preserving its order and calculate average 'Fare'
result = (
    df
    .with_columns(pl.col("Pclass").rank("min").over("Pclass").alias("Pclass_order"))
    .groupby("Pclass_order", maintain_order=True)
    .agg(
        pl.col("Pclass").first().alias("Pclass"),
        pl.col("Fare").mean().alias("Avg_Fare"),
    )
    .select(["Pclass", "Avg_Fare"])
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfgw_yhj6.py", line 12, in 
    .groupby("Pclass_order", maintain_order=True)
     ^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.groupby("Pclass", maintain_order=True).agg(pl.col("Fare").mean())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_2vfp2j6.py", line 3, in 
    result = df.groupby("Pclass", maintain_order=True).agg(pl.col("Fare").mean())
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = (
    df
    .with_columns(pl.col("Fare").mean().over("Pclass").alias("avg_Fare"))
    .select("Pclass", "avg_Fare")
    .unique()
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass", maintain_order=True).agg([pl.col("Fare").mean()])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp83b3x_or.py", line 7, in 
    result = df.groupby("Pclass", maintain_order=True).agg([pl.col("Fare").mean()])
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass", maintain_order=True).agg(pl.col("Fare").mean())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass", maintain_order=True).agg(pl.col("Fare").mean())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp2pj7742j.py", line 8, in 
    result = df.groupby("Pclass", maintain_order=True).agg(pl.col("Fare").mean())
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_average_fare_by_pclass_ordered(df: pl.DataFrame) -> pl.DataFrame:
    """
    Groups by Pclass and calculates the average Fare, maintaining the original order of Pclass values.

    Args:
        df: The input DataFrame.

    Returns:
        A DataFrame with the average Fare for each Pclass, ordered as they appear in the original data.
    """
    return (
        df.group_by("Pclass", maintain_order=True)
        .agg(
            pl.col("Fare").mean().alias("average_fare")
        )
    )


if __name__ == '__main__':
    # Example Usage (assuming titanic.csv exists in a 'data' folder)
    try:
        df = pl.read_csv("data/titanic.csv")
        result_df = calculate_average_fare_by_pclass_ordered(df)
        print(result_df)
    except FileNotFoundError:
        print("Error: titanic.csv not found in the data folder.  Create a dummy DataFrame instead.")
        # Create a dummy DataFrame for demonstration purposes
        data = {'PassengerId': [1, 2, 3, 4, 5],
                'Survived': [0, 1, 1, 0, 1],
                'Pclass': [3, 1, 3, 1, 2],
                'Name': ['A', 'B', 'C', 'D', 'E'],
                'Sex': ['male', 'female', 'male', 'female', 'male'],
                'Age': [22, 38, 26, 35, 35],
                'SibSp': [1, 1, 0, 1, 0],
                'Parch': [0, 0, 0, 0, 0],
                'Ticket': ['A', 'B', 'C', 'D', 'E'],
                'Fare': [7.25, 71.28, 7.92, 53.10, 26.00],
                'Cabin': [None, 'C85', None, 'C123', None],
                'Embarked': ['S', 'C', 'S', 'S', 'Q']}

        df = pl.DataFrame(data)
        result_df = calculate_average_fare_by_pclass_ordered(df)
        print(result_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpqkzpfxce.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    result = df.group_by("Pclass", maintain_order=True).agg(
        pl.col("Fare").mean()
    )
    return result

if __name__ == "__main__":
    # Create a dummy titanic.csv file for demonstration
    data = """PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,"Braund, Mr. Owen Harris",male,22.0,1,0,A/5 21171,7.25,,"S"
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38.0,1,0,PC 17599,71.2833,C85,"C"
3,1,3,"Heikkinen, Miss. Laina",female,26.0,0,0,STON/O2. 3101282,7.925,,"S"
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35.0,1,0,113803,53.1,C123,"S"
5,0,3,"Allen, Mr. William Henry",male,35.0,0,0,373450,8.05,,"S"
6,0,3,"Moran, Mr. James",male,,0,0,330877,8.4583,,"Q"
7,0,1,"McCarthy, Mr. Timothy J",male,54.0,0,0,17463,51.8625,E46,"S"
8,0,3,"Palsson, Master. Gosta Leonard",male,2.0,3,1,349909,21.075,,"S"
9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27.0,0,2,347742,11.1333,,"S"
10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14.0,1,0,237736,30.0708,,"C"
"""
    with open("data/titanic.csv", "w") as f:
        f.write(data)

    print(solve())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdzyeyxpw.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass", maintain_order=True).agg(
    pl.col("Fare").mean()
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpbf9unojy.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.col("Fare").mean()
).sort_by("Pclass")

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp5jeg4ej2.py", line 9, in 
    ).sort_by("Pclass")
      ^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'sort_by'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass", maintain_order=True).agg(pl.col("Fare").mean())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean())
    .sort("Pclass")
)

print(result)

Output:

shape: (3, 2)
┌────────┬───────────┐
│ Pclass ┆ Fare      │
│ ---    ┆ ---       │
│ i64    ┆ f64       │
╞════════╪═══════════╡
│ 1      ┆ 84.154687 │
│ 2      ┆ 20.662183 │
│ 3      ┆ 13.67555  │
└────────┴───────────┘

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

groupby_multiple_aggs

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Group by Pclass and calculate:
# - The number of passengers (as 'count')
# - The average age (as 'avg_age')
# - The minimum fare (as 'min_fare')
# - The maximum fare (as 'max_fare')
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars syntax  df.group_by("id").agg(pl.col("value").len())

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
])

print(result)

Output:

shape: (3, 5)
┌────────┬───────┬───────────┬──────────┬──────────┐
│ Pclass ┆ count ┆ avg_age   ┆ min_fare ┆ max_fare │
│ ---    ┆ ---   ┆ ---       ┆ ---      ┆ ---      │
│ i64    ┆ u32   ┆ f64       ┆ f64      ┆ f64      │
╞════════╪═══════╪═══════════╪══════════╪══════════╡
│ 3      ┆ 491   ┆ 25.14062  ┆ 0.0      ┆ 69.55    │
│ 2      ┆ 184   ┆ 29.87763  ┆ 0.0      ┆ 73.5     │
│ 1      ┆ 216   ┆ 38.233441 ┆ 0.0      ┆ 512.3292 │
└────────┴───────┴───────────┴──────────┴──────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1rdndqft.py:8: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("count"),

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
)

print(result)

Output:

shape: (3, 5)
┌────────┬───────┬───────────┬──────────┬──────────┐
│ Pclass ┆ count ┆ avg_age   ┆ min_fare ┆ max_fare │
│ ---    ┆ ---   ┆ ---       ┆ ---      ┆ ---      │
│ i64    ┆ u32   ┆ f64       ┆ f64      ┆ f64      │
╞════════╪═══════╪═══════════╪══════════╪══════════╡
│ 1      ┆ 216   ┆ 38.233441 ┆ 0.0      ┆ 512.3292 │
│ 2      ┆ 184   ┆ 29.87763  ┆ 0.0      ┆ 73.5     │
│ 3      ┆ 491   ┆ 25.14062  ┆ 0.0      ┆ 69.55    │
└────────┴───────┴───────────┴──────────┴──────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpevsz4sv6.py:8: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("count"),

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
)

print(result)

Output:

shape: (3, 5)
┌────────┬───────┬───────────┬──────────┬──────────┐
│ Pclass ┆ count ┆ avg_age   ┆ min_fare ┆ max_fare │
│ ---    ┆ ---   ┆ ---       ┆ ---      ┆ ---      │
│ i64    ┆ u32   ┆ f64       ┆ f64      ┆ f64      │
╞════════╪═══════╪═══════════╪══════════╪══════════╡
│ 1      ┆ 216   ┆ 38.233441 ┆ 0.0      ┆ 512.3292 │
│ 2      ┆ 184   ┆ 29.87763  ┆ 0.0      ┆ 73.5     │
│ 3      ┆ 491   ┆ 25.14062  ┆ 0.0      ┆ 69.55    │
└────────┴───────┴───────────┴──────────┴──────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpogzix_jj.py:8: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("count"),

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
])

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_ebf17_i.py:7: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("count"),

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0s0woztn.py:7: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("count"),

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp95d8bcmf.py:7: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("count"),

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass").agg([
    pl.count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpldkvrh5r.py", line 7, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
])

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpbv9tl8bb.py:8: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("count"),

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.count().alias("count"),
    pl.col("Age").mean().alias("avg_age"), 
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
])

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmphyuacrmv.py:8: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("count"),

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    [
        pl.count().alias("count"),
        pl.col("Age").mean().alias("avg_age"),
        pl.col("Fare").min().alias("min_fare"),
        pl.col("Fare").max().alias("max_fare"),
    ]
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0nyuvybr.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1szd3b_u.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpj1q_3507.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.groupby("Pclass").agg([
    pl.count("PassengerId").alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfv5k0d9a.py", line 3, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
grouped_df = df.groupby("Pclass").agg([
    pl.count("PassengerId").alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp7lefaizp.py", line 3, in 
    grouped_df = df.groupby("Pclass").agg([
                 ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg([
        pl.col("PassengerId").count().alias("count"),
        pl.col("Age").mean().alias("avg_age"),
        pl.col("Fare").min().alias("min_fare"),
        pl.col("Fare").max().alias("max_fare")
    ])
)

print(result)

Output:

shape: (3, 5)
┌────────┬───────┬───────────┬──────────┬──────────┐
│ Pclass ┆ count ┆ avg_age   ┆ min_fare ┆ max_fare │
│ ---    ┆ ---   ┆ ---       ┆ ---      ┆ ---      │
│ i64    ┆ u32   ┆ f64       ┆ f64      ┆ f64      │
╞════════╪═══════╪═══════════╪══════════╪══════════╡
│ 1      ┆ 216   ┆ 38.233441 ┆ 0.0      ┆ 512.3292 │
│ 2      ┆ 184   ┆ 29.87763  ┆ 0.0      ┆ 73.5     │
│ 3      ┆ 491   ┆ 25.14062  ┆ 0.0      ┆ 69.55    │
└────────┴───────┴───────────┴──────────┴──────────┘

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass").agg([
    pl.col("*").count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp687c761o.py", line 8, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg([
    pl.col("*").count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpjqm9wxqr.py", line 6, in 
    result = df.group_by("Pclass").agg([
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/group_by.py", line 232, in agg
    .collect(no_optimization=True)
     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.DuplicateError: column with name 'count' has more than one occurrence

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'group_by' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass").agg([
    pl.col("PassengerId").count().alias("count"),
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").min().alias("min_fare"),
    pl.col("Fare").max().alias("max_fare")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwi023i7l.py", line 8, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result_df = (
    df.group_by("Pclass")
    .agg(
        [
            pl.count().alias("count"),
            pl.col("Age").mean().alias("avg_age"),
            pl.col("Fare").min().alias("min_fare"),
            pl.col("Fare").max().alias("max_fare"),
        ]
    )
)

print(result_df)

Output:

shape: (3, 5)
┌────────┬───────┬───────────┬──────────┬──────────┐
│ Pclass ┆ count ┆ avg_age   ┆ min_fare ┆ max_fare │
│ ---    ┆ ---   ┆ ---       ┆ ---      ┆ ---      │
│ i64    ┆ u32   ┆ f64       ┆ f64      ┆ f64      │
╞════════╪═══════╪═══════════╪══════════╪══════════╡
│ 3      ┆ 491   ┆ 25.14062  ┆ 0.0      ┆ 69.55    │
│ 2      ┆ 184   ┆ 29.87763  ┆ 0.0      ┆ 73.5     │
│ 1      ┆ 216   ┆ 38.233441 ┆ 0.0      ┆ 512.3292 │
└────────┴───────┴───────────┴──────────┴──────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpobq5nral.py:11: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("count"),

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(
        [
            pl.col("PassengerId").count().alias("count"),
            pl.col("Age").mean().alias("avg_age"),
            pl.col("Fare").min().alias("min_fare"),
            pl.col("Fare").max().alias("max_fare"),
        ]
    )
)

print(result)

Output:

shape: (3, 5)
┌────────┬───────┬───────────┬──────────┬──────────┐
│ Pclass ┆ count ┆ avg_age   ┆ min_fare ┆ max_fare │
│ ---    ┆ ---   ┆ ---       ┆ ---      ┆ ---      │
│ i64    ┆ u32   ┆ f64       ┆ f64      ┆ f64      │
╞════════╪═══════╪═══════════╪══════════╪══════════╡
│ 1      ┆ 216   ┆ 38.233441 ┆ 0.0      ┆ 512.3292 │
│ 3      ┆ 491   ┆ 25.14062  ┆ 0.0      ┆ 69.55    │
│ 2      ┆ 184   ┆ 29.87763  ┆ 0.0      ┆ 73.5     │
└────────┴───────┴───────────┴──────────┴──────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(
        pl.col("PassengerId").count().alias("count"),
        pl.col("Age").mean().alias("avg_age"),
        pl.col("Fare").min().alias("min_fare"),
        pl.col("Fare").max().alias("max_fare"),
    )
)

Status: ✓ Success

groupby_multiple_columns

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Group by both Pclass and Sex, then count the number of passengers in each group
# Sort the results by Pclass and Sex
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars group-by syntax: df.group_by("id").agg(pl.col("value").mean())

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = (df
    .groupby(['Pclass', 'Sex'])
    .agg(pl.count().alias('passenger_count'))
    .sort(['Pclass', 'Sex'])
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpl89oq0s4.py", line 7, in 
    .groupby(['Pclass', 'Sex'])
     ^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
grouped_df = (df
    .group_by(["Pclass", "Sex"])
    .agg(
        pl.count().alias("passenger_count")
    )
    .sort(["Pclass", "Sex"])
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmparlvndh6.py:6: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("passenger_count")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (df.group_by(["Pclass", "Sex"])
           .agg(pl.col("PassengerId").count().alias("passenger_count"))
           .sort(["Pclass", "Sex"]))

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by(["Pclass", "Sex"])
    .agg(pl.count())
    .sort(["Pclass", "Sex"])
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp__p8ihdc.py:9: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  .agg(pl.count())

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by(["Pclass", "Sex"])
    .agg(pl.count())
    .sort(["Pclass", "Sex"])
)

print(result)

Output:

shape: (6, 3)
┌────────┬────────┬───────┐
│ Pclass ┆ Sex    ┆ count │
│ ---    ┆ ---    ┆ ---   │
│ i64    ┆ str    ┆ u32   │
╞════════╪════════╪═══════╡
│ 1      ┆ female ┆ 94    │
│ 1      ┆ male   ┆ 122   │
│ 2      ┆ female ┆ 76    │
│ 2      ┆ male   ┆ 108   │
│ 3      ┆ female ┆ 144   │
│ 3      ┆ male   ┆ 347   │
└────────┴────────┴───────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpx1x4614j.py:9: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  .agg(pl.count())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
# First load the data using Polars as follows
df = pl.read_csv("data/titanic.csv")

# Group by both Pclass and Sex, then count the number of passengers in each group
# Sort the results by Pclass and Sex
result = (
    df.group_by(["Pclass", "Sex"])
    .agg(pl.col("PassengerId").count().alias("count"))
    .sort(["Pclass", "Sex"])
)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by(["Pclass", "Sex"]).agg(pl.count().alias("PassengerCount")).sort(["Pclass", "Sex"])

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpjuijpztm.py:6: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  result = df.group_by(["Pclass", "Sex"]).agg(pl.count().alias("PassengerCount")).sort(["Pclass", "Sex"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by(["Pclass", "Sex"]).agg(pl.count().alias("PassengerCount")).sort(["Pclass", "Sex"])

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpbkb0odat.py:6: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  result = df.group_by(["Pclass", "Sex"]).agg(pl.count().alias("PassengerCount")).sort(["Pclass", "Sex"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by(["Pclass", "Sex"])
    .agg(pl.count().alias("PassengerCount"))
    .sort(["Pclass", "Sex"])
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_yhzfcgn.py:9: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  .agg(pl.count().alias("PassengerCount"))

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by(["Pclass", "Sex"]).agg(
    pl.count().alias("PassengerCount")
).sort(["Pclass", "Sex"])

print(result)

Output:

shape: (6, 3)
┌────────┬────────┬────────────────┐
│ Pclass ┆ Sex    ┆ PassengerCount │
│ ---    ┆ ---    ┆ ---            │
│ i64    ┆ str    ┆ u32            │
╞════════╪════════╪════════════════╡
│ 1      ┆ female ┆ 94             │
│ 1      ┆ male   ┆ 122            │
│ 2      ┆ female ┆ 76             │
│ 2      ┆ male   ┆ 108            │
│ 3      ┆ female ┆ 144            │
│ 3      ┆ male   ┆ 347            │
└────────┴────────┴────────────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp003gb9om.py:8: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("PassengerCount")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by(["Pclass", "Sex"]).agg(
    pl.count().alias("PassengerCount")
).sort(["Pclass", "Sex"])

print(result)

Output:

shape: (6, 3)
┌────────┬────────┬────────────────┐
│ Pclass ┆ Sex    ┆ PassengerCount │
│ ---    ┆ ---    ┆ ---            │
│ i64    ┆ str    ┆ u32            │
╞════════╪════════╪════════════════╡
│ 1      ┆ female ┆ 94             │
│ 1      ┆ male   ┆ 122            │
│ 2      ┆ female ┆ 76             │
│ 2      ┆ male   ┆ 108            │
│ 3      ┆ female ┆ 144            │
│ 3      ┆ male   ┆ 347            │
└────────┴────────┴────────────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmptbv7n335.py:7: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("PassengerCount")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by(["Pclass", "Sex"])
    .agg(
        pl.count().alias("PassengerCount")
    )
    .sort(["Pclass", "Sex"])
)

print(result)

Output:

shape: (6, 3)
┌────────┬────────┬────────────────┐
│ Pclass ┆ Sex    ┆ PassengerCount │
│ ---    ┆ ---    ┆ ---            │
│ i64    ┆ str    ┆ u32            │
╞════════╪════════╪════════════════╡
│ 1      ┆ female ┆ 94             │
│ 1      ┆ male   ┆ 122            │
│ 2      ┆ female ┆ 76             │
│ 2      ┆ male   ┆ 108            │
│ 3      ┆ female ┆ 144            │
│ 3      ┆ male   ┆ 347            │
└────────┴────────┴────────────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp7pk0oegn.py:10: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("PassengerCount")

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.groupby(["Pclass", "Sex"])
    .agg(pl.count("PassengerId").alias("PassengerCount"))
    .sort(["Pclass", "Sex"])
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpjsi7go6e.py", line 8, in 
    df.groupby(["Pclass", "Sex"])
    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = (df
          .group_by(["Pclass", "Sex"])
          .agg(pl.col("PassengerId").count().alias("PassengerCount"))
          .sort(["Pclass", "Sex"]))

print(result)

Output:

shape: (6, 3)
┌────────┬────────┬────────────────┐
│ Pclass ┆ Sex    ┆ PassengerCount │
│ ---    ┆ ---    ┆ ---            │
│ i64    ┆ str    ┆ u32            │
╞════════╪════════╪════════════════╡
│ 1      ┆ female ┆ 94             │
│ 1      ┆ male   ┆ 122            │
│ 2      ┆ female ┆ 76             │
│ 2      ┆ male   ┆ 108            │
│ 3      ┆ female ┆ 144            │
│ 3      ┆ male   ┆ 347            │
└────────┴────────┴────────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (df
          .group_by(["Pclass", "Sex"])
          .agg(
              pl.col("PassengerId").count().alias("passenger_count")
          )
          .sort(["Pclass", "Sex"]))

print(result)

Output:

shape: (6, 3)
┌────────┬────────┬─────────────────┐
│ Pclass ┆ Sex    ┆ passenger_count │
│ ---    ┆ ---    ┆ ---             │
│ i64    ┆ str    ┆ u32             │
╞════════╪════════╪═════════════════╡
│ 1      ┆ female ┆ 94              │
│ 1      ┆ male   ┆ 122             │
│ 2      ┆ female ┆ 76              │
│ 2      ┆ male   ┆ 108             │
│ 3      ┆ female ┆ 144             │
│ 3      ┆ male   ┆ 347             │
└────────┴────────┴─────────────────┘

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df
   .groupby(["Pclass", "Sex"])
   .agg(pl.count("PassengerId").alias("Count"))
   .sort("Pclass", "Sex")
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9r4ktlk_.py", line 10, in 
    .groupby(["Pclass", "Sex"])
     ^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.group_by(["Pclass", "Sex"]).agg(pl.count("PassengerId")).sort("Pclass", "Sex")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code for the query:

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.groupby(["Pclass", "Sex"])
   .agg(pl.col("*").count())
   .sort("Pclass", "Sex")
)

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by(["Pclass", "Sex"]).agg(
    pl.count().alias("count")
).sort(["Pclass", "Sex"])

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpzbmipo4x.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by(["Pclass", "Sex"]).agg(
    pl.count().alias("count")
).sort(["Pclass", "Sex"])

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpgve7kme1.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by(["Pclass", "Sex"])
    .agg(pl.count().alias("count"))
    .sort(["Pclass", "Sex"])
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpg5eqaj48.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

groupby_single_column

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Group the data by Pclass and calculate the average Age and Fare for each class
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars group-by syntax: df.group_by("id").agg(pl.col("value").mean())

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass").agg([
    pl.col("Age").mean().alias("Average_Age"),
    pl.col("Fare").mean().alias("Average_Fare")
])

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp3gya1ivp.py", line 6, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("Average_Age"),
    pl.col("Fare").mean().alias("Average_Fare")
])

print(result)

Output:

shape: (3, 3)
┌────────┬─────────────┬──────────────┐
│ Pclass ┆ Average_Age ┆ Average_Fare │
│ ---    ┆ ---         ┆ ---          │
│ i64    ┆ f64         ┆ f64          │
╞════════╪═════════════╪══════════════╡
│ 2      ┆ 29.87763    ┆ 20.662183    │
│ 1      ┆ 38.233441   ┆ 84.154687    │
│ 3      ┆ 25.14062    ┆ 13.67555     │
└────────┴─────────────┴──────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("avg_age"),
    pl.col("Fare").mean().alias("avg_fare")
])

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.groupby("Pclass").agg([
    pl.col("Age").mean().alias("avg_Age"),
    pl.col("Fare").mean().alias("avg_Fare")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpi3pwccbd.py", line 6, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.groupby("Pclass").agg([
    pl.col("Age").mean().alias("avg_Age"),
    pl.col("Fare").mean().alias("avg_Fare")
])
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp79qj7vkd.py", line 6, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass").agg([
    pl.col("Age").mean().alias("avg_Age"),
    pl.col("Fare").mean().alias("avg_Fare")
])

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfyhv7e2d.py", line 8, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").mean(),
    pl.col("Fare").mean()
])

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpir8zpghd.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("Average Age"),
    pl.col("Fare").mean().alias("Average Fare")
])

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpninpacvr.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("Average_Age"),
    pl.col("Fare").mean().alias("Average_Fare")
])

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpu63zsl6t.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

class_stats = df.group_by("Pclass").agg(
    pl.mean("Age").alias("avg_age"),
    pl.mean("Fare").alias("avg_fare")
)

print(class_stats)

Output:

shape: (3, 3)
┌────────┬───────────┬───────────┐
│ Pclass ┆ avg_age   ┆ avg_fare  │
│ ---    ┆ ---       ┆ ---       │
│ i64    ┆ f64       ┆ f64       │
╞════════╪═══════════╪═══════════╡
│ 1      ┆ 38.233441 ┆ 84.154687 │
│ 3      ┆ 25.14062  ┆ 13.67555  │
│ 2      ┆ 29.87763  ┆ 20.662183 │
└────────┴───────────┴───────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.col("Age").mean().alias("AverageAge"),
    pl.col("Fare").mean().alias("AverageFare")
)

print(result)

Output:

shape: (3, 3)
┌────────┬────────────┬─────────────┐
│ Pclass ┆ AverageAge ┆ AverageFare │
│ ---    ┆ ---        ┆ ---         │
│ i64    ┆ f64        ┆ f64         │
╞════════╪════════════╪═════════════╡
│ 2      ┆ 29.87763   ┆ 20.662183   │
│ 1      ┆ 38.233441  ┆ 84.154687   │
│ 3      ┆ 25.14062   ┆ 13.67555    │
└────────┴────────────┴─────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.col("Age").mean().alias("Average_Age"),
    pl.col("Fare").mean().alias("Average_Fare")
)

print(result)

Output:

shape: (3, 3)
┌────────┬─────────────┬──────────────┐
│ Pclass ┆ Average_Age ┆ Average_Fare │
│ ---    ┆ ---         ┆ ---          │
│ i64    ┆ f64         ┆ f64          │
╞════════╪═════════════╪══════════════╡
│ 1      ┆ 38.233441   ┆ 84.154687    │
│ 3      ┆ 25.14062    ┆ 13.67555     │
│ 2      ┆ 29.87763    ┆ 20.662183    │
└────────┴─────────────┴──────────────┘

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass").agg(
    ["mean", "mean"]
).rename({
    "mean": "Age",
    "mean_1": "Fare",
})

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp50egl1g_.py", line 7, in 
    result = df.groupby("Pclass").agg(
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

grouped_df = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("Average Age"),
    pl.col("Fare").mean().alias("Average Fare")
])

print(grouped_df)

Output:

shape: (3, 3)
┌────────┬─────────────┬──────────────┐
│ Pclass ┆ Average Age ┆ Average Fare │
│ ---    ┆ ---         ┆ ---          │
│ i64    ┆ f64         ┆ f64          │
╞════════╪═════════════╪══════════════╡
│ 1      ┆ 38.233441   ┆ 84.154687    │
│ 2      ┆ 29.87763    ┆ 20.662183    │
│ 3      ┆ 25.14062    ┆ 13.67555     │
└────────┴─────────────┴──────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

grouped_df = df.group_by("Pclass").agg(
    pl.col("Age").mean().alias("Average_Age"),
    pl.col("Fare").mean().alias("Average_Fare")
)

print(grouped_df)

Output:

shape: (3, 3)
┌────────┬─────────────┬──────────────┐
│ Pclass ┆ Average_Age ┆ Average_Fare │
│ ---    ┆ ---         ┆ ---          │
│ i64    ┆ f64         ┆ f64          │
╞════════╪═════════════╪══════════════╡
│ 1      ┆ 38.233441   ┆ 84.154687    │
│ 2      ┆ 29.87763    ┆ 20.662183    │
│ 3      ┆ 25.14062    ┆ 13.67555     │
└────────┴─────────────┴──────────────┘

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.col("Age").mean().alias("Average Age"),
    pl.col("Fare").mean().alias("Average Fare")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg(
    pl.col("Age").mean().alias("Average Age"),
    pl.col("Fare").mean().alias("Average Fare")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg(
    pl.col("Age").mean().alias("Average_Age"),
    pl.col("Fare").mean().alias("Average_Fare")
)

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.groupby("Pclass").agg([
    pl.col("Age").mean().alias("Average_Age"),
    pl.col("Fare").mean().alias("Average_Fare")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpmxp6sx8h.py", line 3, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("Average_Age"),
    pl.col("Fare").mean().alias("Average_Fare")
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Group by Pclass and calculate the average Age and Fare
result = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("Average_Age"),
    pl.col("Fare").mean().alias("Average_Fare")
])

# Output the result
print(result)

Output:

shape: (3, 3)
┌────────┬─────────────┬──────────────┐
│ Pclass ┆ Average_Age ┆ Average_Fare │
│ ---    ┆ ---         ┆ ---          │
│ i64    ┆ f64         ┆ f64          │
╞════════╪═════════════╪══════════════╡
│ 2      ┆ 29.87763    ┆ 20.662183    │
│ 1      ┆ 38.233441   ┆ 84.154687    │
│ 3      ┆ 25.14062    ┆ 13.67555     │
└────────┴─────────────┴──────────────┘

Status: ✓ Success

groupby_survival_rate

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Calculate the survival rate by Pclass:
# 1. Group by Pclass
# 2. Calculate the mean of the Survived column (this gives the survival rate)
# 3. Sort by survival rate in descending order
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# In Polars the method is called group_by and not groupby: df.group_by("id").agg(pl.col("value").mean())

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
survival_rate_by_pclass = (
    df
    .groupby("Pclass")
    .agg(pl.col("Survived").mean().alias("SurvivalRate"))
    .sort("SurvivalRate", reverse=True)
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpf525u055.py", line 5, in 
    .groupby("Pclass")
     ^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
survival_rate_by_pclass = (
    df.group_by("Pclass")
    .agg(pl.col("Survived").mean().alias("SurvivalRate"))
    .sort("SurvivalRate", descending=True)
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the Titanic data
df = pl.read_csv("data/titanic.csv")

# Calculate the survival rate by Pclass
result = (
    df.group_by("Pclass")
    .agg(pl.col("Survived").mean().alias("Survival_Rate"))
    .sort("Survival_Rate", descending=True)
)

print(result)

Output:

shape: (3, 2)
┌────────┬───────────────┐
│ Pclass ┆ Survival_Rate │
│ ---    ┆ ---           │
│ i64    ┆ f64           │
╞════════╪═══════════════╡
│ 1      ┆ 0.62963       │
│ 2      ┆ 0.472826      │
│ 3      ┆ 0.242363      │
└────────┴───────────────┘

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

survival_rate_by_pclass = df.group_by("Pclass").agg(
    pl.mean("Survived").alias("survival_rate")
).sort("survival_rate", descending=True)

# The result is stored in the survival_rate_by_pclass variable
# To display it, you could use:
# print(survival_rate_by_pclass)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data using Polars
df = pl.read_csv("data/titanic.csv")

# Calculate the survival rate by Pclass
survival_rate_by_pclass = df.group_by("Pclass").agg(
    pl.col("Survived").mean().alias("survival_rate")
).sort("survival_rate", descending=True)

# Print the result (optional)
print(survival_rate_by_pclass)

Output:

shape: (3, 2)
┌────────┬───────────────┐
│ Pclass ┆ survival_rate │
│ ---    ┆ ---           │
│ i64    ┆ f64           │
╞════════╪═══════════════╡
│ 1      ┆ 1.0           │
│ 3      ┆ 0.0           │
│ 2      ┆ 0.0           │
└────────┴───────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Calculate the survival rate by Pclass:
# 1. Group by Pclass
# 2. Calculate the mean of the Survived column (this gives the survival rate)
# 3. Sort by survival rate in descending order
survival_rate_by_pclass = (
    df.group_by("Pclass")
    .agg(
        pl.mean("Survived").alias("survival_rate")  # Calculate mean and alias the result column
    )
    .sort("survival_rate", descending=True)      # Sort by the calculated survival rate
)

# Optional: print the result to see it
# print(survival_rate_by_pclass)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.groupby('Pclass').agg(pl.col('Survived').mean()).sort('Survived', descending=True)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpectcktnj.py", line 3, in 
    df.groupby('Pclass').agg(pl.col('Survived').mean()).sort('Survived', descending=True)
    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

survival_by_class = (
    df.group_by("Pclass")
    .agg(pl.col("Survived").mean().alias("survival_rate"))
    .sort("survival_rate", descending=True)
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

(df.group_by("Pclass")
   .agg(pl.col("Survived").mean().alias("survival_rate"))
   .sort("survival_rate", descending=True))

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
survival_rates = df.group_by("Pclass").agg(pl.col("Survived").mean().alias("SurvivalRate")).sort("SurvivalRate", descending=True)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg(pl.col("Survived").mean()).sort("Survived", descending=True)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(pl.col("Survived").mean().alias("survival_rate")).sort("survival_rate", descending=True)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

survival_rate_by_class = (df
                         .group_by("Pclass")
                         .agg(pl.col("Survived").mean().alias("survival_rate"))
                         .sort("survival_rate", descending=True))

print(survival_rate_by_class)

Output:

shape: (3, 2)
┌────────┬───────────────┐
│ Pclass ┆ survival_rate │
│ ---    ┆ ---           │
│ i64    ┆ f64           │
╞════════╪═══════════════╡
│ 1      ┆ 0.62963       │
│ 2      ┆ 0.472826      │
│ 3      ┆ 0.242363      │
└────────┴───────────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

survival_rate_by_class = (
    df.group_by("Pclass")
    .agg(pl.col("Survived").mean().alias("SurvivalRate"))
    .sort("SurvivalRate", descending=True)
)

print(survival_rate_by_class)

Output:

shape: (3, 2)
┌────────┬──────────────┐
│ Pclass ┆ SurvivalRate │
│ ---    ┆ ---          │
│ i64    ┆ f64          │
╞════════╪══════════════╡
│ 1      ┆ 0.62963      │
│ 2      ┆ 0.472826     │
│ 3      ┆ 0.242363     │
└────────┴──────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

survival_by_class = (
    df.group_by("Pclass")
    .agg(pl.col("Survived").mean().alias("survival_rate"))
    .sort("survival_rate", descending=True)
)

print(survival_by_class)

Output:

shape: (3, 2)
┌────────┬───────────────┐
│ Pclass ┆ survival_rate │
│ ---    ┆ ---           │
│ i64    ┆ f64           │
╞════════╪═══════════════╡
│ 1      ┆ 0.62963       │
│ 2      ┆ 0.472826      │
│ 3      ┆ 0.242363      │
└────────┴───────────────┘

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_survival_rate_by_pclass(df: pl.DataFrame) -> pl.DataFrame:
    """
    Calculates the survival rate by Pclass.

    Args:
        df: Input DataFrame.

    Returns:
        DataFrame with survival rate by Pclass.
    """
    result = (
        df.group_by("Pclass")
        .agg(pl.col("Survived").mean().alias("Survival_Rate"))
        .sort("Survival_Rate", descending=True)
    )
    return result

if __name__ == '__main__':
    # Example Usage
    df = pl.read_csv("data/titanic.csv")
    survival_rate = calculate_survival_rate_by_pclass(df)
    print(survival_rate)

Output:

shape: (3, 2)
┌────────┬───────────────┐
│ Pclass ┆ Survival_Rate │
│ ---    ┆ ---           │
│ i64    ┆ f64           │
╞════════╪═══════════════╡
│ 1      ┆ 1.0           │
│ 2      ┆ 0.0           │
│ 3      ┆ 0.0           │
└────────┴───────────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_survival_rate_by_pclass(df: pl.DataFrame) -> pl.DataFrame:
    """
    Calculates the survival rate by Pclass.

    Args:
        df: Input Polars DataFrame.

    Returns:
        Polars DataFrame with survival rate by Pclass, sorted in descending order.
    """
    return (
        df.group_by("Pclass")
        .agg(pl.col("Survived").mean().alias("Survival_Rate"))
        .sort("Survival_Rate", descending=True)
    )

if __name__ == '__main__':
    # Create a dummy DataFrame for testing
    data = {
        "PassengerId": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
        "Survived": [0, 1, 1, 0, 1, 0, 1, 0, 1, 0],
        "Pclass": [3, 1, 3, 1, 2, 3, 2, 3, 1, 2],
        "Name": ["A", "B", "C", "D", "E", "F", "G", "H", "I", "J"],
        "Sex": ["male", "female", "male", "female", "male", "female", "male", "female", "male", "female"],
        "Age": [22, 38, 26, 35, 35, None, 54, 2, 27, 14],
        "SibSp": [1, 1, 0, 1, 0, 0, 0, 3, 2, 1],
        "Parch": [0, 0, 0, 0, 0, 0, 0, 1, 2, 0],
        "Ticket": ["A/5 21171", "PC 17599", "STON/O2. 3101282", "113803", "373450", "330877", "17463", "349909", "347742", "237736"],
        "Fare": [7.25, 71.2833, 7.925, 53.1, 8.05, 8.4583, 51.8625, 21.075, 11.1333, 30.0708],
        "Cabin": [None, "C85", None, "C123", None, None, "E46", None, None, None],
        "Embarked": ["S", "C", "S", "S", "S", "Q", "S", "S", "S", "C"]
    }
    
    df = pl.DataFrame(data)
    
    # Calculate and print the survival rate by Pclass
    survival_rates = calculate_survival_rate_by_pclass(df)
    print(survival_rates)

Output:

shape: (3, 2)
┌────────┬───────────────┐
│ Pclass ┆ Survival_Rate │
│ ---    ┆ ---           │
│ i64    ┆ f64           │
╞════════╪═══════════════╡
│ 1      ┆ 0.666667      │
│ 2      ┆ 0.666667      │
│ 3      ┆ 0.25          │
└────────┴───────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_survival_rate_by_pclass(df: pl.DataFrame) -> pl.DataFrame:
    """
    Calculates the survival rate by Pclass in the Titanic dataset.

    Args:
        df: A Polars DataFrame representing the Titanic dataset.

    Returns:
        A Polars DataFrame with Pclass and survival rate, sorted by survival rate in descending order.
    """
    return (
        df.group_by("Pclass")
        .agg(pl.col("Survived").mean().alias("survival_rate"))
        .sort("survival_rate", descending=True)
    )

if __name__ == '__main__':
    # Example usage with dummy data
    data = {
        'PassengerId': [1, 2, 3, 4, 5, 6],
        'Survived': [0, 1, 0, 1, 0, 1],
        'Pclass': [1, 1, 2, 2, 3, 3],
        'Name': ['A', 'B', 'C', 'D', 'E', 'F'],
        'Sex': ['male', 'female', 'male', 'female', 'male', 'female'],
        'Age': [20, 30, 25, 35, 40, 45],
        'SibSp': [1, 0, 1, 0, 1, 0],
        'Parch': [0, 1, 0, 1, 0, 1],
        'Ticket': ['123', '456', '789', '101', '112', '131'],
        'Fare': [100, 200, 150, 250, 120, 220],
        'Cabin': ['A1', 'B2', 'C3', 'D4', 'E5', 'F6'],
        'Embarked': ['S', 'C', 'S', 'C', 'S', 'C']
    }
    df = pl.DataFrame(data)
    
    survival_rate_df = calculate_survival_rate_by_pclass(df)
    print(survival_rate_df)

Output:

shape: (3, 2)
┌────────┬───────────────┐
│ Pclass ┆ survival_rate │
│ ---    ┆ ---           │
│ i64    ┆ f64           │
╞════════╪═══════════════╡
│ 3      ┆ 0.5           │
│ 2      ┆ 0.5           │
│ 1      ┆ 0.5           │
└────────┴───────────────┘

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
survival_rate_by_pclass = df.groupby("Pclass").agg(pl	col("Survived").mean().alias("Survival Rate")).sort("Survival Rate", reverse=True)
print(survival_rate_by_pclass)

Error:

invalid syntax. Perhaps you forgot a comma? (, line 7)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass").agg(pl.col("Survived").mean()).sort("Survived", reverse=True)

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code for the task:

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df
   .groupby("Pclass")
   .agg(pl.col("Survived").mean())
   .sort("Survived_mean", descending=True)
)

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

survival_rates = (
    df.group_by("Pclass")
    .agg(pl.mean("Survived"))
    .sort("Survived", descending=True)
)

print(survival_rates)

Output:

shape: (3, 2)
┌────────┬──────────┐
│ Pclass ┆ Survived │
│ ---    ┆ ---      │
│ i64    ┆ f64      │
╞════════╪══════════╡
│ 1      ┆ 0.62963  │
│ 2      ┆ 0.472826 │
│ 3      ┆ 0.242363 │
└────────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

survival_rate_by_pclass = (
    df.group_by("Pclass")
    .agg(pl.col("Survived").mean())
    .sort("Survived", descending=True)
)

print(survival_rate_by_pclass)

Output:

shape: (3, 2)
┌────────┬──────────┐
│ Pclass ┆ Survived │
│ ---    ┆ ---      │
│ i64    ┆ f64      │
╞════════╪══════════╡
│ 1      ┆ 0.62963  │
│ 2      ┆ 0.472826 │
│ 3      ┆ 0.242363 │
└────────┴──────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(pl.col("Survived").mean())
    .sort(pl.col("Survived"), descending=True)
)

Status: ✓ Success

groupby_with_filter

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Group by Pclass, calculate the average Fare, then filter to show only classes where the average fare is above 30
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars group-by syntax: df.group_by("id").agg(pl.col("value").mean())

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (df
    .group_by("Pclass")
    .agg(pl.mean("Fare").alias("Average_Fare"))
    .filter(pl.col("Average_Fare") > 30)
)

print(result)

Output:

shape: (1, 2)
┌────────┬──────────────┐
│ Pclass ┆ Average_Fare │
│ ---    ┆ ---          │
│ i64    ┆ f64          │
╞════════╪══════════════╡
│ 1      ┆ 84.154687    │
└────────┴──────────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.col("Fare").mean().alias("average_fare")
).filter(
    pl.col("average_fare") > 30
)

print(result)

Output:

shape: (1, 2)
┌────────┬──────────────┐
│ Pclass ┆ average_fare │
│ ---    ┆ ---          │
│ i64    ┆ f64          │
╞════════╪══════════════╡
│ 1      ┆ 84.154687    │
└────────┴──────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean().alias("avg_fare"))
    .filter(pl.col("avg_fare") > 30)
)

print(result)

Output:

shape: (1, 2)
┌────────┬───────────┐
│ Pclass ┆ avg_fare  │
│ ---    ┆ ---       │
│ i64    ┆ f64       │
╞════════╪═══════════╡
│ 1      ┆ 84.154687 │
└────────┴───────────┘

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.groupby("Pclass").agg(pl.col("Fare").mean()).filter(pl.col("Fare") > 30)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpn9h2vnnt.py", line 7, in 
    result = df.groupby("Pclass").agg(pl.col("Fare").mean()).filter(pl.col("Fare") > 30)
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg(pl.col("Fare").mean()).filter(pl.col("Fare") > 30)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(pl.col("Fare").mean()).filter(pl.col("Fare") > 30)
print(result)

Output:

shape: (1, 2)
┌────────┬───────────┐
│ Pclass ┆ Fare      │
│ ---    ┆ ---       │
│ i64    ┆ f64       │
╞════════╪═══════════╡
│ 1      ┆ 84.154687 │
└────────┴───────────┘

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg(pl.col("Fare").mean()).filter(pl.col("Fare") > 30)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(pl.col("Fare").mean()).filter(pl.col("Fare") > 30)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean().alias("avg_fare"))
    .filter(pl.col("avg_fare") > 30)
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean().alias("Average_Fare"))
    .filter(pl.col("Average_Fare") > 30)
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpaookjcb7.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean().alias("avg_fare"))
    .filter(pl.col("avg_fare") > 30)
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpj3qf3mv1.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean().alias("avg_fare"))
    .filter(pl.col("avg_fare") > 30)
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpiadadnd5.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass").agg(
    pl.col("Fare").mean().alias("avg_fare")
).filter(pl.col("avg_fare") > 30)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmppfnu80_0.py", line 6, in 
    result = df.groupby("Pclass").agg(
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = (df.group_by("Pclass")
           .agg(pl.col("Fare").mean().alias("avg_fare"))
           .filter(pl.col("avg_fare") > 30))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
(
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean().alias("avg_fare"))
    .filter(pl.col("avg_fare") > 30)
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean().alias("AverageFare"))
    .filter(pl.col("AverageFare") > 30)
)

print(result)

Output:

shape: (1, 2)
┌────────┬─────────────┐
│ Pclass ┆ AverageFare │
│ ---    ┆ ---         │
│ i64    ┆ f64         │
╞════════╪═════════════╡
│ 1      ┆ 84.154687   │
└────────┴─────────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean())
    .filter(pl.col("Fare") > 30)
)

print(result)

Output:

shape: (1, 2)
┌────────┬───────────┐
│ Pclass ┆ Fare      │
│ ---    ┆ ---       │
│ i64    ┆ f64       │
╞════════╪═══════════╡
│ 1      ┆ 84.154687 │
└────────┴───────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean())
    .filter(pl.col("Fare") > 30)
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = (
    df.groupby("Pclass")
    .agg(pl.col("Fare").mean().alias("average_fare"))
    .filter(pl.col("average_fare") > 30)
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmph6u29zw5.py", line 4, in 
    df.groupby("Pclass")
    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_grouped = df.group_by("Pclass").agg(pl.col("Fare").mean().alias("avg_fare"))
df_filtered = df_grouped.filter(pl.col("avg_fare") > 30)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(pl.col("Fare").mean().alias("avg_fare"))
    .filter(pl.col("avg_fare") > 30)
)

print(result)

Output:

shape: (1, 2)
┌────────┬───────────┐
│ Pclass ┆ avg_fare  │
│ ---    ┆ ---       │
│ i64    ┆ f64       │
╞════════╪═══════════╡
│ 1      ┆ 84.154687 │
└────────┴───────────┘

Status: ✓ Success

iterate_through_groups

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Iterate through groups of passengers by Pclass and print:
# 1. The class number
# 2. The number of passengers in that class
# 3. The average fare for that class
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of iterating through groups:
# for (col,) group_df in df.group_by("column"):
#     print(f"Group {col}: {len(group_df)} rows")

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Group by Pclass
grouped_df = df.group_by("Pclass").agg(
    pl.count().alias("passenger_count"),
    pl.mean("Fare").alias("average_fare")
)

# Iterate through groups and print the required information
for row in grouped_df.iter_rows(named=True):
    print(f"Class: {row['Pclass']}")
    print(f"Number of passengers: {row['passenger_count']}")
    print(f"Average fare: ${row['average_fare']:.2f}")
    print()

Output:

Class: 1
Number of passengers: 216
Average fare: $84.15

Class: 2
Number of passengers: 184
Average fare: $20.66

Class: 3
Number of passengers: 491
Average fare: $13.68

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmppdjmiae9.py:10: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("passenger_count"),

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for (pclass,), group_df in df.group_by("Pclass"):
    avg_fare = group_df["Fare"].mean()
    num_passengers = len(group_df)
    print(f"Class: {pclass}, Passengers: {num_passengers}, Average Fare: ${avg_fare:.2f}")

Output:

Class: 1, Passengers: 216, Average Fare: $84.15
Class: 2, Passengers: 184, Average Fare: $20.66
Class: 3, Passengers: 491, Average Fare: $13.68

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Group by Pclass and get count and average fare
grouped_df = df.group_by("Pclass").agg(
    pl.count().alias("passenger_count"),
    pl.col("Fare").mean().alias("avg_fare")
)

# Iterate through groups and print the required information
for row in grouped_df.iter_rows(named=True):
    print(f"Class: {row['Pclass']}")
    print(f"Number of passengers: {row['passenger_count']}")
    print(f"Average fare: ${row['avg_fare']:.2f}")
    print()

Output:

Class: 3
Number of passengers: 491
Average fare: $13.68

Class: 2
Number of passengers: 184
Average fare: $20.66

Class: 1
Number of passengers: 216
Average fare: $84.15

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpms8b7x1s.py:9: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("passenger_count"),

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

for group, group_df in df.groupby("Pclass"):
    print(f"Class: {group}")
    print(f"Number of passengers: {group_df.height}")
    print(f"Average fare: {group_df['Fare'].mean():.2f}")
    print("---")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8ysn88hq.py", line 8, in 
    for group, group_df in df.groupby("Pclass"):
                           ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

for (pclass,), group_df in df.group_by("Pclass").iter_groups():
    avg_fare = group_df.select(pl.col("Fare").mean()).item()
    print(f"Pclass {pclass}: {len(group_df)} passengers, average fare: {avg_fare}")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpbyasg_on.py", line 8, in 
    for (pclass,), group_df in df.group_by("Pclass").iter_groups():
                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'GroupBy' object has no attribute 'iter_groups'. Did you mean: 'map_groups'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

for group, group_df in df.groupby("Pclass"):
    print(f"Class: {group}")
    print(f"Number of passengers: {group_df.height}")
    print(f"Average fare: {group_df.select(pl.col('Fare').mean()).item()[0]}")
    print()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpjl_iwttp.py", line 8, in 
    for group, group_df in df.groupby("Pclass"):
                           ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
for pclass in sorted(df['Pclass'].unique()):
    group = df.filter(pl.col('Pclass') == pclass)
    passenger_count = len(group)
    avg_fare = group['Fare'].mean()
    print(f"Class: {pclass}")
    print(f"Number of passengers: {passenger_count}")
    print(f"Average fare: ${avg_fare:.2f}")
    print()

Output:

Class: 1
Number of passengers: 216
Average fare: $84.15

Class: 2
Number of passengers: 184
Average fare: $20.66

Class: 3
Number of passengers: 491
Average fare: $13.68

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

for pclass, group_df in df.group_by("Pclass"):
    passenger_count = len(group_df)
    avg_fare = group_df["Fare"].mean()
    print(f"Class {pclass}:")
    print(f"Number of passengers: {passenger_count}")
    print(f"Average fare: ${avg_fare:.2f}")
    print()

Output:

Class (2,):
Number of passengers: 184
Average fare: $20.66

Class (3,):
Number of passengers: 491
Average fare: $13.68

Class (1,):
Number of passengers: 216
Average fare: $84.15

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

grouped = (df.group_by("Pclass")
          .agg([
              pl.col("PassengerId").count().alias("passenger_count"),
              pl.col("Fare").mean().alias("avg_fare")
          ])
          .sort("Pclass"))

for row in grouped.iter_rows():
    print(f"Class {int(row[0])}:")
    print(f"Number of passengers: {int(row[1])}")
    print(f"Average fare: ${row[2]:.2f}")
    print()

Output:

Class 1:
Number of passengers: 216
Average fare: $84.15

Class 2:
Number of passengers: 184
Average fare: $20.66

Class 3:
Number of passengers: 491
Average fare: $13.68

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for pclass in df["Pclass"].unique():
    df_pclass = df.filter(pl.col("Pclass") == pclass)
    num_passengers = df_pclass.height
    avg_fare = df_pclass["Fare"].mean()
    print(f"Class: {pclass}")
    print(f"Number of passengers: {num_passengers}")
    print(f"Average fare: {avg_fare:.2f}")
    print("-" * 20)

Output:

Class: 1
Number of passengers: 216
Average fare: 84.15
--------------------
Class: 2
Number of passengers: 184
Average fare: 20.66
--------------------
Class: 3
Number of passengers: 491
Average fare: 13.68
--------------------

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for (pclass,) group_df in df.group_by("Pclass"):
    print(f"Class: {pclass}")
    print(f"Number of passengers: {len(group_df)}")
    print(f"Average fare: {group_df.select(pl.col("Fare").mean()).item()}")

Error:

invalid syntax (, line 7)

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Iterate through groups of passengers by Pclass
for group in df.group_by("Pclass"):
    class_number = group[0]
    num_passengers = group.height
    avg_fare = group.select(pl.col("Fare").mean()).item()

    print(f"Class: {class_number}")
    print(f"Number of passengers: {num_passengers}")
    print(f"Average fare: {avg_fare}")
    print("-" * 20)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpj46t1khw.py", line 11, in 
    num_passengers = group.height
                     ^^^^^^^^^^^^
AttributeError: 'tuple' object has no attribute 'height'

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for pclass, group in df.group_by("Pclass"):
    print(f"Class: {pclass}")
    print(f"Number of passengers: {len(group)}")
    print(f"Average fare: {group['Fare'].mean()}\n")

Output:

Class: (1,)
Number of passengers: 216
Average fare: 84.15468750000001

Class: (3,)
Number of passengers: 491
Average fare: 13.675550101832991

Class: (2,)
Number of passengers: 184
Average fare: 20.66218315217391

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

for (pclass,), group_df in df.group_by("Pclass"):
    avg_fare = group_df["Fare"].mean()
    print(f"Class {pclass}: {len(group_df)} passengers, average fare {avg_fare:.2f}")

Output:

Class 2: 184 passengers, average fare 20.66
Class 1: 216 passengers, average fare 84.15
Class 3: 491 passengers, average fare 13.68

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

for group in df.group_by("Pclass").agg([
    pl.count().alias("passenger_count"),
    pl.col("Fare").mean().alias("average_fare")
]).iter_rows():
    print(f"Class: {group[0]}")
    print(f"Passenger count: {group[1]}")
    print(f"Average fare: {group[2]}")
    print()

Output:

Class: 3
Passenger count: 491
Average fare: 13.675550101832997

Class: 2
Passenger count: 184
Average fare: 20.66218315217391

Class: 1
Passenger count: 216
Average fare: 84.15468749999992

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmptluhtcad.py:7: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("passenger_count"),

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

grouped = df.group_by("Pclass")

for group in grouped:
    pclass = group["Pclass"][0]
    count = group.height
    avg_fare = group["Fare"].mean()
    print(f"Class: {pclass}")
    print(f"Number of passengers: {count}")
    print(f"Average fare: {avg_fare}")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp2h7w47a9.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for (pclass,) , group_df in df.group_by("Pclass"):
    print(f"Class: {pclass}")
    print(f"Number of Passengers: {len(group_df)}")
    print(f"Average Fare: {group_df['Fare'].mean()}")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp6acwziau.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

grouped = df.group_by("Pclass").agg([
    pl.count().alias("passenger_count"),
    pl.col("Fare").mean().alias("average_fare")
])

for row in grouped.rows(named=True):
    pclass = row['Pclass']
    passenger_count = row['passenger_count']
    average_fare = row['average_fare']
    print(f"Class: {pclass}, Passenger Count: {passenger_count}, Average Fare: {average_fare:.2f}")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpi3gwfj1u.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for group in df.groupby("Pclass"):
    pclass = group["Pclass"][0]
    num_passengers = group.height
    avg_fare = group["Fare"].mean()
    
    print(f"Class: {pclass}")
    print(f"Number of passengers: {num_passengers}")
    print(f"Average fare: {avg_fare:.2f}")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpzhsf246t.py", line 7, in 
    for group in df.groupby("Pclass"):
                 ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for (pclass,), group_df in df.groupby("Pclass"):
    num_passengers = len(group_df)
    avg_fare = group_df['Fare'].mean()
    print(f"Class {pclass}: {num_passengers} passengers, Average Fare: {avg_fare:.2f}")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpsxef61vl.py", line 7, in 
    for (pclass,), group_df in df.groupby("Pclass"):
                               ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for group in df.group_by("Pclass").agg([
    pl.col("PassengerId").count().alias("count"),
    pl.col("Fare").mean().alias("avg_fare")
]).iter_rows(named=True):
    print(f"Class: {group['Pclass']}, Number of Passengers: {group['count']}, Average Fare: {group['avg_fare']:.2f}")

Output:

Class: 3, Number of Passengers: 491, Average Fare: 13.68
Class: 2, Number of Passengers: 184, Average Fare: 20.66
Class: 1, Number of Passengers: 216, Average Fare: 84.15

Status: ✓ Success

iterate_through_rows

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Iterate through the first 5 rows of the dataframe and print:
# 1. The passenger name
# 2. Their class
# 3. Their fare
# Use df.iter_rows() for efficient row iteration
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of iterating through rows:
# for row in df.iter_rows(named=True):
#     print(f"Name: {row['name']}")

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

for row in df.head(5).iter_rows(named=True):
    print(f"Name: {row['Name']}, Class: {row['Pclass']}, Fare: {row['Fare']}")

Output:

Name: Braund, Mr. Owen Harris, Class: 3, Fare: 7.25
Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Class: 1, Fare: 71.2833
Name: Heikkinen, Miss. Laina, Class: 3, Fare: 7.925
Name: Futrelle, Mrs. Jacques Heath (Lily May Peel), Class: 1, Fare: 53.1
Name: Allen, Mr. William Henry, Class: 3, Fare: 8.05

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

for row in df.head(5).iter_rows(named=True):
    print(f"Name: {row['Name']}, Class: {row['Pclass']}, Fare: {row['Fare']}")

Output:

Name: Braund, Mr. Owen Harris, Class: 3, Fare: 7.25
Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Class: 1, Fare: 71.2833
Name: Heikkinen, Miss. Laina, Class: 3, Fare: 7.925
Name: Futrelle, Mrs. Jacques Heath (Lily May Peel), Class: 1, Fare: 53.1
Name: Allen, Mr. William Henry, Class: 3, Fare: 8.05

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

for row in df.head(5).iter_rows():
    print(f"Name: {row[3]}, Class: {row[2]}, Fare: {row[9]}")

Output:

Name: Braund, Mr. Owen Harris, Class: 3, Fare: 7.25
Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Class: 1, Fare: 71.2833
Name: Heikkinen, Miss. Laina, Class: 3, Fare: 7.925
Name: Futrelle, Mrs. Jacques Heath (Lily May Peel), Class: 1, Fare: 53.1
Name: Allen, Mr. William Henry, Class: 3, Fare: 8.05

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")

for row in df.head(5).iter_rows():
    print(f"Passenger: {row['Name']}, Class: {row['Pclass']}, Fare: {row['Fare']}")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpmj5aii0h.py", line 8, in 
    print(f"Passenger: {row['Name']}, Class: {row['Pclass']}, Fare: {row['Fare']}")
                        ~~~^^^^^^^^
TypeError: tuple indices must be integers or slices, not str

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")

for row in df.head(5).iter_rows(named=True):
    print(f"Name: {row['Name']}, Class: {row['Pclass']}, Fare: {row['Fare']}")

Output:

Name: Braund, Mr. Owen Harris, Class: 3, Fare: 7.25
Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Class: 1, Fare: 71.2833
Name: Heikkinen, Miss. Laina, Class: 3, Fare: 7.925
Name: Futrelle, Mrs. Jacques Heath (Lily May Peel), Class: 1, Fare: 53.1
Name: Allen, Mr. William Henry, Class: 3, Fare: 8.05

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
for row in df.iter_rows(n_rows=5):
    print(f"Passenger Name: {row['Name']}, Class: {row['Pclass']}, Fare: {row['Fare']}")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpyanjckmm.py", line 6, in 
    for row in df.iter_rows(n_rows=5):
               ^^^^^^^^^^^^^^^^^^^^^^
TypeError: DataFrame.iter_rows() got an unexpected keyword argument 'n_rows'

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for i, row in enumerate(df.iter_rows()):
    if i >= 5:
        break
    name = row[3]
    pclass = row[2]
    fare = row[9]
    print(f"Name: {name}, Class: {pclass}, Fare: {fare}")

Output:

Name: Braund, Mr. Owen Harris, Class: 3, Fare: 7.25
Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Class: 1, Fare: 71.2833
Name: Heikkinen, Miss. Laina, Class: 3, Fare: 7.925
Name: Futrelle, Mrs. Jacques Heath (Lily May Peel), Class: 1, Fare: 53.1
Name: Allen, Mr. William Henry, Class: 3, Fare: 8.05

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for row in df.iter_rows(named=True):
    print(f"Name: {row['Name']}")
    print(f"Class: {row['Pclass']}")
    print(f"Fare: {row['Fare']}")
    break
for row in df.iter_rows(named=True):
    print(f"Name: {row['Name']}")
    print(f"Class: {row['Pclass']}")
    print(f"Fare: {row['Fare']}")
    break
for row in df.iter_rows(named=True):
    print(f"Name: {row['Name']}")
    print(f"Class: {row['Pclass']}")
    print(f"Fare: {row['Fare']}")
    break
for row in df.iter_rows(named=True):
    print(f"Name: {row['Name']}")
    print(f"Class: {row['Pclass']}")
    print(f"Fare: {row['Fare']}")
    break
for row in df.iter_rows(named=True):
    print(f"Name: {row['Name']}")
    print(f"Class: {row['Pclass']}")
    print(f"Fare: {row['Fare']}")
    break

Output:

Name: Braund, Mr. Owen Harris
Class: 3
Fare: 7.25
Name: Braund, Mr. Owen Harris
Class: 3
Fare: 7.25
Name: Braund, Mr. Owen Harris
Class: 3
Fare: 7.25
Name: Braund, Mr. Owen Harris
Class: 3
Fare: 7.25
Name: Braund, Mr. Owen Harris
Class: 3
Fare: 7.25

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Iterate through the first 5 rows and print the requested information
for i, row in enumerate(df.iter_rows()):
    if i >= 5:
        break
    name = row[3]
    pclass = row[2]
    fare = row[9]
    print(f"Passenger Name: {name}, Class: {pclass}, Fare: {fare}")

Output:

Passenger Name: Braund, Mr. Owen Harris, Class: 3, Fare: 7.25
Passenger Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Class: 1, Fare: 71.2833
Passenger Name: Heikkinen, Miss. Laina, Class: 3, Fare: 7.925
Passenger Name: Futrelle, Mrs. Jacques Heath (Lily May Peel), Class: 1, Fare: 53.1
Passenger Name: Allen, Mr. William Henry, Class: 3, Fare: 8.05

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for row in df.iter_rows():
    name = row[3]
    pclass = row[2]
    fare = row[9]
    print(f"Name: {name}, Class: {pclass}, Fare: {fare}")

    if i == 4:
        break

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpp323xxwd.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for row in df.head(5).iter_rows(named=True):
    print(f"Name: {row['Name']}")
    print(f"Class: {row['Pclass']}")
    print(f"Fare: {row['Fare']}")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp3tiqrchz.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for row in df.head(5).iter_rows():
    name = row[3]
    pclass = row[2]
    fare = row[9]
    print(f"Name: {name}, Class: {pclass}, Fare: {fare}")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpt2r1ja73.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Get the indices of the required columns for efficient access
name_idx = df.columns.index("Name")
class_idx = df.columns.index("Pclass")
fare_idx = df.columns.index("Fare")

# Iterate through the first 5 rows
for i, row in enumerate(df.iter_rows()):
    if i >= 5:
        break
    
    name = row[name_idx]
    passenger_class = row[class_idx]
    fare = row[fare_idx]
    
    print(f"Passenger: {name}")
    print(f"Class: {passenger_class}")
    print(f"Fare: {fare}")
    print()

Output:

Passenger: Braund, Mr. Owen Harris
Class: 3
Fare: 7.25

Passenger: Cumings, Mrs. John Bradley (Florence Briggs Thayer)
Class: 1
Fare: 71.2833

Passenger: Heikkinen, Miss. Laina
Class: 3
Fare: 7.925

Passenger: Futrelle, Mrs. Jacques Heath (Lily May Peel)
Class: 1
Fare: 53.1

Passenger: Allen, Mr. William Henry
Class: 3
Fare: 8.05

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

for i, row in enumerate(df.iter_rows(named=True)):
    if i >= 5:
        break
    print(f"Name: {row['Name']}, Class: {row['Pclass']}, Fare: {row['Fare']}")

Output:

Name: Braund, Mr. Owen Harris, Class: 3, Fare: 7.25
Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Class: 1, Fare: 71.2833
Name: Heikkinen, Miss. Laina, Class: 3, Fare: 7.925
Name: Futrelle, Mrs. Jacques Heath (Lily May Peel), Class: 1, Fare: 53.1
Name: Allen, Mr. William Henry, Class: 3, Fare: 8.05

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Iterate through the first 5 rows
for i, row in enumerate(df.iter_rows()):
    if i >= 5:
        break
        
    name = row[df.columns.index("Name")]
    pclass = row[df.columns.index("Pclass")]
    fare = row[df.columns.index("Fare")]
    
    print(f"Name: {name}")
    print(f"Class: {pclass}")
    print(f"Fare: {fare}")
    print("-" * 30)

Output:

Name: Braund, Mr. Owen Harris
Class: 3
Fare: 7.25
------------------------------
Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer)
Class: 1
Fare: 71.2833
------------------------------
Name: Heikkinen, Miss. Laina
Class: 3
Fare: 7.925
------------------------------
Name: Futrelle, Mrs. Jacques Heath (Lily May Peel)
Class: 1
Fare: 53.1
------------------------------
Name: Allen, Mr. William Henry
Class: 3
Fare: 8.05
------------------------------

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for row in df.iter_rows(named=True):
    print(f"Name: {row['Name']}, Class: {row['Pclass']}, Fare: {row['Fare']}")

Output:

Name: Braund, Mr. Owen Harris, Class: 3, Fare: 7.25
Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Class: 1, Fare: 71.2833
Name: Heikkinen, Miss. Laina, Class: 3, Fare: 7.925
Name: Futrelle, Mrs. Jacques Heath (Lily May Peel), Class: 1, Fare: 53.1
Name: Allen, Mr. William Henry, Class: 3, Fare: 8.05
Name: Moran, Mr. James, Class: 3, Fare: 8.4583
Name: McCarthy, Mr. Timothy J, Class: 1, Fare: 51.8625
Name: Palsson, Master. Gosta Leonard, Class: 3, Fare: 21.075
Name: Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg), Class: 3, Fare: 11.1333
Name: Nasser, Mrs. Nicholas (Adele Achem), Class: 2, Fare: 30.0708
Name: Sandstrom, Miss. Marguerite Rut, Class: 3, Fare: 16.7
Name: Bonnell, Miss. Elizabeth, Class: 1, Fare: 26.55
Name: Saundercock, Mr. William Henry, Class: 3, Fare: 8.05
Name: Andersson, Mr. Anders Johan, Class: 3, Fare: 31.275
Name: Vestrom, Miss. Hulda Amanda Adolfina, Class: 3, Fare: 7.8542
Name: Hewlett, Mrs. (Mary D Kingcome) , Class: 2, Fare: 16.0
Name: Rice, Master. Eugene, Class: 3, Fare: 29.125
Name: Williams, Mr. Charles Eugene, Class: 2, Fare: 13.0
Name: Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele), Class: 3, Fare: 18.0
Name: Masselmani, Mrs. Fatima, Class: 3, Fare: 7.225
Name: Fynney, Mr. Joseph J, Class: 2, Fare: 26.0
Name: Beesley, Mr. Lawrence, Class: 2, Fare: 13.0
Name: McGowan, Miss. Anna "Annie", Class: 3, Fare: 8.0292
Name: Sloper, Mr. William Thompson, Class: 1, Fare: 35.5
Name: Palsson, Miss. Torborg Danira, Class: 3, Fare: 21.075
Name: Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson), Class: 3, Fare: 31.3875
Name: Emir, Mr. Farred Chehab, Class: 3, Fare: 7.225
Name: Fortune, Mr. Charles Alexander, Class: 1, Fare: 263.0
Name: O'Dwyer, Miss. Ellen "Nellie", Class: 3, Fare: 7.8792
Name: Todoroff, Mr. Lalio, Class: 3, Fare: 7.8958
Name: Uruchurtu, Don. Manuel E, Class: 1, Fare: 27.7208
Name: Spencer, Mrs. William Augustus (Marie Eugenie), Class: 1, Fare: 146.5208
Name: Glynn, Miss. Mary Agatha, Class: 3, Fare: 7.75
Name: Wheadon, Mr. Edward H, Class: 2, Fare: 10.5
Name: Meyer, Mr. Edgar Joseph, Class: 1, Fare: 82.1708
Name: Holverson, Mr. Alexander Oskar, Class: 1, Fare: 52.0
Name: Mamee, Mr. Hanna, Class: 3, Fare: 7.2292
Name: Cann, Mr. Ernest Charles, Class: 3, Fare: 8.05
Name: Vander Planke, Miss. Augusta Maria, Class: 3, Fare: 18.0
Name: Nicola-Yarred, Miss. Jamila, Class: 3, Fare: 11.2417
Name: Ahlin, Mrs. Johan (Johanna Persdotter Larsson), Class: 3, Fare: 9.475
Name: Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott), Class: 2, Fare: 21.0
Name: Kraeff, Mr. Theodor, Class: 3, Fare: 7.8958
Name: Laroche, Miss. Simonne Marie Anne Andree, Class: 2, Fare: 41.5792
Name: Devaney, Miss. Margaret Delia, Class: 3, Fare: 7.8792
Name: Rogers, Mr. William John, Class: 3, Fare: 8.05
Name: Lennon, Mr. Denis, Class: 3, Fare: 15.5
Name: O'Driscoll, Miss. Bridget, Class: 3, Fare: 7.75
Name: Samaan, Mr. Youssef, Class: 3, Fare: 21.6792
Name: Arnold-Franchi, Mrs. Josef (Josefine Franchi), Class: 3, Fare: 17.8
Name: Panula, Master. Juha Niilo, Class: 3, Fare: 39.6875
Name: Nosworthy, Mr. Richard Cater, Class: 3, Fare: 7.8
Name: Harper, Mrs. Henry Sleeper (Myna Haxtun), Class: 1, Fare: 76.7292
Name: Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson), Class: 2, Fare: 26.0
Name: Ostby, Mr. Engelhart Cornelius, Class: 1, Fare: 61.9792
Name: Woolner, Mr. Hugh, Class: 1, Fare: 35.5
Name: Rugg, Miss. Emily, Class: 2, Fare: 10.5
Name: Novel, Mr. Mansouer, Class: 3, Fare: 7.2292
Name: West, Miss. Constance Mirium, Class: 2, Fare: 27.75
Name: Goodwin, Master. William Frederick, Class: 3, Fare: 46.9
Name: Sirayanian, Mr. Orsen, Class: 3, Fare: 7.2292
Name: Icard, Miss. Amelie, Class: 1, Fare: 80.0
Name: Harris, Mr. Henry Birkhardt, Class: 1, Fare: 83.475
Name: Skoog, Master. Harald, Class: 3, Fare: 27.9
Name: Stewart, Mr. Albert A, Class: 1, Fare: 27.7208
Name: Moubarek, Master. Gerios, Class: 3, Fare: 15.2458
Name: Nye, Mrs. (Elizabeth Ramell), Class: 2, Fare: 10.5
Name: Crease, Mr. Ernest James, Class: 3, Fare: 8.1583
Name: Andersson, Miss. Erna Alexandra, Class: 3, Fare: 7.925
Name: Kink, Mr. Vincenz, Class: 3, Fare: 8.6625
Name: Jenkin, Mr. Stephen Curnow, Class: 2, Fare: 10.5
Name: Goodwin, Miss. Lillian Amy, Class: 3, Fare: 46.9
Name: Hood, Mr. Ambrose Jr, Class: 2, Fare: 73.5
Name: Chronopoulos, Mr. Apostolos, Class: 3, Fare: 14.4542
Name: Bing, Mr. Lee, Class: 3, Fare: 56.4958
Name: Moen, Mr. Sigurd Hansen, Class: 3, Fare: 7.65
Name: Staneff, Mr. Ivan, Class: 3, Fare: 7.8958
Name: Moutal, Mr. Rahamin Haim, Class: 3, Fare: 8.05
Name: Caldwell, Master. Alden Gates, Class: 2, Fare: 29.0
Name: Dowdell, Miss. Elizabeth, Class: 3, Fare: 12.475
Name: Waelens, Mr. Achille, Class: 3, Fare: 9.0
Name: Sheerlinck, Mr. Jan Baptist, Class: 3, Fare: 9.5
Name: McDermott, Miss. Brigdet Delia, Class: 3, Fare: 7.7875
Name: Carrau, Mr. Francisco M, Class: 1, Fare: 47.1
Name: Ilett, Miss. Bertha, Class: 2, Fare: 10.5
Name: Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson), Class: 3, Fare: 15.85
Name: Ford, Mr. William Neal, Class: 3, Fare: 34.375
Name: Slocovski, Mr. Selman Francis, Class: 3, Fare: 8.05
Name: Fortune, Miss. Mabel Helen, Class: 1, Fare: 263.0
Name: Celotti, Mr. Francesco, Class: 3, Fare: 8.05
Name: Christmann, Mr. Emil, Class: 3, Fare: 8.05
Name: Andreasson, Mr. Paul Edvin, Class: 3, Fare: 7.8542
Name: Chaffee, Mr. Herbert Fuller, Class: 1, Fare: 61.175
Name: Dean, Mr. Bertram Frank, Class: 3, Fare: 20.575
Name: Coxon, Mr. Daniel, Class: 3, Fare: 7.25
Name: Shorney, Mr. Charles Joseph, Class: 3, Fare: 8.05
Name: Goldschmidt, Mr. George B, Class: 1, Fare: 34.6542
Name: Greenfield, Mr. William Bertram, Class: 1, Fare: 63.3583
Name: Doling, Mrs. John T (Ada Julia Bone), Class: 2, Fare: 23.0
Name: Kantor, Mr. Sinai, Class: 2, Fare: 26.0
Name: Petranec, Miss. Matilda, Class: 3, Fare: 7.8958
Name: Petroff, Mr. Pastcho ("Pentcho"), Class: 3, Fare: 7.8958
Name: White, Mr. Richard Frasar, Class: 1, Fare: 77.2875
Name: Johansson, Mr. Gustaf Joel, Class: 3, Fare: 8.6542
Name: Gustafsson, Mr. Anders Vilhelm, Class: 3, Fare: 7.925
Name: Mionoff, Mr. Stoytcho, Class: 3, Fare: 7.8958
Name: Salkjelsvik, Miss. Anna Kristine, Class: 3, Fare: 7.65
Name: Moss, Mr. Albert Johan, Class: 3, Fare: 7.775
Name: Rekic, Mr. Tido, Class: 3, Fare: 7.8958
Name: Moran, Miss. Bertha, Class: 3, Fare: 24.15
Name: Porter, Mr. Walter Chamberlain, Class: 1, Fare: 52.0
Name: Zabour, Miss. Hileni, Class: 3, Fare: 14.4542
Name: Barton, Mr. David John, Class: 3, Fare: 8.05
Name: Jussila, Miss. Katriina, Class: 3, Fare: 9.825
Name: Attalah, Miss. Malake, Class: 3, Fare: 14.4583
Name: Pekoniemi, Mr. Edvard, Class: 3, Fare: 7.925
Name: Connors, Mr. Patrick, Class: 3, Fare: 7.75
Name: Turpin, Mr. William John Robert, Class: 2, Fare: 21.0
Name: Baxter, Mr. Quigg Edmond, Class: 1, Fare: 247.5208
Name: Andersson, Miss. Ellis Anna Maria, Class: 3, Fare: 31.275
Name: Hickman, Mr. Stanley George, Class: 2, Fare: 73.5
Name: Moore, Mr. Leonard Charles, Class: 3, Fare: 8.05
Name: Nasser, Mr. Nicholas, Class: 2, Fare: 30.0708
Name: Webber, Miss. Susan, Class: 2, Fare: 13.0
Name: White, Mr. Percival Wayland, Class: 1, Fare: 77.2875
Name: Nicola-Yarred, Master. Elias, Class: 3, Fare: 11.2417
Name: McMahon, Mr. Martin, Class: 3, Fare: 7.75
Name: Madsen, Mr. Fridtjof Arne, Class: 3, Fare: 7.1417
Name: Peter, Miss. Anna, Class: 3, Fare: 22.3583
Name: Ekstrom, Mr. Johan, Class: 3, Fare: 6.975
Name: Drazenoic, Mr. Jozef, Class: 3, Fare: 7.8958
Name: Coelho, Mr. Domingos Fernandeo, Class: 3, Fare: 7.05
Name: Robins, Mrs. Alexander A (Grace Charity Laury), Class: 3, Fare: 14.5
Name: Weisz, Mrs. Leopold (Mathilde Francoise Pede), Class: 2, Fare: 26.0
Name: Sobey, Mr. Samuel James Hayden, Class: 2, Fare: 13.0
Name: Richard, Mr. Emile, Class: 2, Fare: 15.0458
Name: Newsom, Miss. Helen Monypeny, Class: 1, Fare: 26.2833
Name: Futrelle, Mr. Jacques Heath, Class: 1, Fare: 53.1
Name: Osen, Mr. Olaf Elon, Class: 3, Fare: 9.2167
Name: Giglio, Mr. Victor, Class: 1, Fare: 79.2
Name: Boulos, Mrs. Joseph (Sultana), Class: 3, Fare: 15.2458
Name: Nysten, Miss. Anna Sofia, Class: 3, Fare: 7.75
Name: Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck), Class: 3, Fare: 15.85
Name: Burke, Mr. Jeremiah, Class: 3, Fare: 6.75
Name: Andrew, Mr. Edgardo Samuel, Class: 2, Fare: 11.5
Name: Nicholls, Mr. Joseph Charles, Class: 2, Fare: 36.75
Name: Andersson, Mr. August Edvard ("Wennerstrom"), Class: 3, Fare: 7.7958
Name: Ford, Miss. Robina Maggie "Ruby", Class: 3, Fare: 34.375
Name: Navratil, Mr. Michel ("Louis M Hoffman"), Class: 2, Fare: 26.0
Name: Byles, Rev. Thomas Roussel Davids, Class: 2, Fare: 13.0
Name: Bateman, Rev. Robert James, Class: 2, Fare: 12.525
Name: Pears, Mrs. Thomas (Edith Wearne), Class: 1, Fare: 66.6
Name: Meo, Mr. Alfonzo, Class: 3, Fare: 8.05
Name: van Billiard, Mr. Austin Blyler, Class: 3, Fare: 14.5
Name: Olsen, Mr. Ole Martin, Class: 3, Fare: 7.3125
Name: Williams, Mr. Charles Duane, Class: 1, Fare: 61.3792
Name: Gilnagh, Miss. Katherine "Katie", Class: 3, Fare: 7.7333
Name: Corn, Mr. Harry, Class: 3, Fare: 8.05
Name: Smiljanic, Mr. Mile, Class: 3, Fare: 8.6625
Name: Sage, Master. Thomas Henry, Class: 3, Fare: 69.55
Name: Cribb, Mr. John Hatfield, Class: 3, Fare: 16.1
Name: Watt, Mrs. James (Elizabeth "Bessie" Inglis Milne), Class: 2, Fare: 15.75
Name: Bengtsson, Mr. John Viktor, Class: 3, Fare: 7.775
Name: Calic, Mr. Jovo, Class: 3, Fare: 8.6625
Name: Panula, Master. Eino Viljami, Class: 3, Fare: 39.6875
Name: Goldsmith, Master. Frank John William "Frankie", Class: 3, Fare: 20.525
Name: Chibnall, Mrs. (Edith Martha Bowerman), Class: 1, Fare: 55.0
Name: Skoog, Mrs. William (Anna Bernhardina Karlsson), Class: 3, Fare: 27.9
Name: Baumann, Mr. John D, Class: 1, Fare: 25.925
Name: Ling, Mr. Lee, Class: 3, Fare: 56.4958
Name: Van der hoef, Mr. Wyckoff, Class: 1, Fare: 33.5
Name: Rice, Master. Arthur, Class: 3, Fare: 29.125
Name: Johnson, Miss. Eleanor Ileen, Class: 3, Fare: 11.1333
Name: Sivola, Mr. Antti Wilhelm, Class: 3, Fare: 7.925
Name: Smith, Mr. James Clinch, Class: 1, Fare: 30.6958
Name: Klasen, Mr. Klas Albin, Class: 3, Fare: 7.8542
Name: Lefebre, Master. Henry Forbes, Class: 3, Fare: 25.4667
Name: Isham, Miss. Ann Elizabeth, Class: 1, Fare: 28.7125
Name: Hale, Mr. Reginald, Class: 2, Fare: 13.0
Name: Leonard, Mr. Lionel, Class: 3, Fare: 0.0
Name: Sage, Miss. Constance Gladys, Class: 3, Fare: 69.55
Name: Pernot, Mr. Rene, Class: 2, Fare: 15.05
Name: Asplund, Master. Clarence Gustaf Hugo, Class: 3, Fare: 31.3875
Name: Becker, Master. Richard F, Class: 2, Fare: 39.0
Name: Kink-Heilmann, Miss. Luise Gretchen, Class: 3, Fare: 22.025
Name: Rood, Mr. Hugh Roscoe, Class: 1, Fare: 50.0
Name: O'Brien, Mrs. Thomas (Johanna "Hannah" Godfrey), Class: 3, Fare: 15.5
Name: Romaine, Mr. Charles Hallace ("Mr C Rolmane"), Class: 1, Fare: 26.55
Name: Bourke, Mr. John, Class: 3, Fare: 15.5
Name: Turcin, Mr. Stjepan, Class: 3, Fare: 7.8958
Name: Pinsky, Mrs. (Rosa), Class: 2, Fare: 13.0
Name: Carbines, Mr. William, Class: 2, Fare: 13.0
Name: Andersen-Jensen, Miss. Carla Christine Nielsine, Class: 3, Fare: 7.8542
Name: Navratil, Master. Michel M, Class: 2, Fare: 26.0
Name: Brown, Mrs. James Joseph (Margaret Tobin), Class: 1, Fare: 27.7208
Name: Lurette, Miss. Elise, Class: 1, Fare: 146.5208
Name: Mernagh, Mr. Robert, Class: 3, Fare: 7.75
Name: Olsen, Mr. Karl Siegwart Andreas, Class: 3, Fare: 8.4042
Name: Madigan, Miss. Margaret "Maggie", Class: 3, Fare: 7.75
Name: Yrois, Miss. Henriette ("Mrs Harbeck"), Class: 2, Fare: 13.0
Name: Vande Walle, Mr. Nestor Cyriel, Class: 3, Fare: 9.5
Name: Sage, Mr. Frederick, Class: 3, Fare: 69.55
Name: Johanson, Mr. Jakob Alfred, Class: 3, Fare: 6.4958
Name: Youseff, Mr. Gerious, Class: 3, Fare: 7.225
Name: Cohen, Mr. Gurshon "Gus", Class: 3, Fare: 8.05
Name: Strom, Miss. Telma Matilda, Class: 3, Fare: 10.4625
Name: Backstrom, Mr. Karl Alfred, Class: 3, Fare: 15.85
Name: Albimona, Mr. Nassef Cassem, Class: 3, Fare: 18.7875
Name: Carr, Miss. Helen "Ellen", Class: 3, Fare: 7.75
Name: Blank, Mr. Henry, Class: 1, Fare: 31.0
Name: Ali, Mr. Ahmed, Class: 3, Fare: 7.05
Name: Cameron, Miss. Clear Annie, Class: 2, Fare: 21.0
Name: Perkin, Mr. John Henry, Class: 3, Fare: 7.25
Name: Givard, Mr. Hans Kristensen, Class: 2, Fare: 13.0
Name: Kiernan, Mr. Philip, Class: 3, Fare: 7.75
Name: Newell, Miss. Madeleine, Class: 1, Fare: 113.275
Name: Honkanen, Miss. Eliina, Class: 3, Fare: 7.925
Name: Jacobsohn, Mr. Sidney Samuel, Class: 2, Fare: 27.0
Name: Bazzani, Miss. Albina, Class: 1, Fare: 76.2917
Name: Harris, Mr. Walter, Class: 2, Fare: 10.5
Name: Sunderland, Mr. Victor Francis, Class: 3, Fare: 8.05
Name: Bracken, Mr. James H, Class: 2, Fare: 13.0
Name: Green, Mr. George Henry, Class: 3, Fare: 8.05
Name: Nenkoff, Mr. Christo, Class: 3, Fare: 7.8958
Name: Hoyt, Mr. Frederick Maxfield, Class: 1, Fare: 90.0
Name: Berglund, Mr. Karl Ivar Sven, Class: 3, Fare: 9.35
Name: Mellors, Mr. William John, Class: 2, Fare: 10.5
Name: Lovell, Mr. John Hall ("Henry"), Class: 3, Fare: 7.25
Name: Fahlstrom, Mr. Arne Jonas, Class: 2, Fare: 13.0
Name: Lefebre, Miss. Mathilde, Class: 3, Fare: 25.4667
Name: Harris, Mrs. Henry Birkhardt (Irene Wallach), Class: 1, Fare: 83.475
Name: Larsson, Mr. Bengt Edvin, Class: 3, Fare: 7.775
Name: Sjostedt, Mr. Ernst Adolf, Class: 2, Fare: 13.5
Name: Asplund, Miss. Lillian Gertrud, Class: 3, Fare: 31.3875
Name: Leyson, Mr. Robert William Norman, Class: 2, Fare: 10.5
Name: Harknett, Miss. Alice Phoebe, Class: 3, Fare: 7.55
Name: Hold, Mr. Stephen, Class: 2, Fare: 26.0
Name: Collyer, Miss. Marjorie "Lottie", Class: 2, Fare: 26.25
Name: Pengelly, Mr. Frederick William, Class: 2, Fare: 10.5
Name: Hunt, Mr. George Henry, Class: 2, Fare: 12.275
Name: Zabour, Miss. Thamine, Class: 3, Fare: 14.4542
Name: Murphy, Miss. Katherine "Kate", Class: 3, Fare: 15.5
Name: Coleridge, Mr. Reginald Charles, Class: 2, Fare: 10.5
Name: Maenpaa, Mr. Matti Alexanteri, Class: 3, Fare: 7.125
Name: Attalah, Mr. Sleiman, Class: 3, Fare: 7.225
Name: Minahan, Dr. William Edward, Class: 1, Fare: 90.0
Name: Lindahl, Miss. Agda Thorilda Viktoria, Class: 3, Fare: 7.775
Name: Hamalainen, Mrs. William (Anna), Class: 2, Fare: 14.5
Name: Beckwith, Mr. Richard Leonard, Class: 1, Fare: 52.5542
Name: Carter, Rev. Ernest Courtenay, Class: 2, Fare: 26.0
Name: Reed, Mr. James George, Class: 3, Fare: 7.25
Name: Strom, Mrs. Wilhelm (Elna Matilda Persson), Class: 3, Fare: 10.4625
Name: Stead, Mr. William Thomas, Class: 1, Fare: 26.55
Name: Lobb, Mr. William Arthur, Class: 3, Fare: 16.1
Name: Rosblom, Mrs. Viktor (Helena Wilhelmina), Class: 3, Fare: 20.2125
Name: Touma, Mrs. Darwis (Hanne Youssef Razi), Class: 3, Fare: 15.2458
Name: Thorne, Mrs. Gertrude Maybelle, Class: 1, Fare: 79.2
Name: Cherry, Miss. Gladys, Class: 1, Fare: 86.5
Name: Ward, Miss. Anna, Class: 1, Fare: 512.3292
Name: Parrish, Mrs. (Lutie Davis), Class: 2, Fare: 26.0
Name: Smith, Mr. Thomas, Class: 3, Fare: 7.75
Name: Asplund, Master. Edvin Rojj Felix, Class: 3, Fare: 31.3875
Name: Taussig, Mr. Emil, Class: 1, Fare: 79.65
Name: Harrison, Mr. William, Class: 1, Fare: 0.0
Name: Henry, Miss. Delia, Class: 3, Fare: 7.75
Name: Reeves, Mr. David, Class: 2, Fare: 10.5
Name: Panula, Mr. Ernesti Arvid, Class: 3, Fare: 39.6875
Name: Persson, Mr. Ernst Ulrik, Class: 3, Fare: 7.775
Name: Graham, Mrs. William Thompson (Edith Junkins), Class: 1, Fare: 153.4625
Name: Bissette, Miss. Amelia, Class: 1, Fare: 135.6333
Name: Cairns, Mr. Alexander, Class: 1, Fare: 31.0
Name: Tornquist, Mr. William Henry, Class: 3, Fare: 0.0
Name: Mellinger, Mrs. (Elizabeth Anne Maidment), Class: 2, Fare: 19.5
Name: Natsch, Mr. Charles H, Class: 1, Fare: 29.7
Name: Healy, Miss. Hanora "Nora", Class: 3, Fare: 7.75
Name: Andrews, Miss. Kornelia Theodosia, Class: 1, Fare: 77.9583
Name: Lindblom, Miss. Augusta Charlotta, Class: 3, Fare: 7.75
Name: Parkes, Mr. Francis "Frank", Class: 2, Fare: 0.0
Name: Rice, Master. Eric, Class: 3, Fare: 29.125
Name: Abbott, Mrs. Stanton (Rosa Hunt), Class: 3, Fare: 20.25
Name: Duane, Mr. Frank, Class: 3, Fare: 7.75
Name: Olsson, Mr. Nils Johan Goransson, Class: 3, Fare: 7.8542
Name: de Pelsmaeker, Mr. Alfons, Class: 3, Fare: 9.5
Name: Dorking, Mr. Edward Arthur, Class: 3, Fare: 8.05
Name: Smith, Mr. Richard William, Class: 1, Fare: 26.0
Name: Stankovic, Mr. Ivan, Class: 3, Fare: 8.6625
Name: de Mulder, Mr. Theodore, Class: 3, Fare: 9.5
Name: Naidenoff, Mr. Penko, Class: 3, Fare: 7.8958
Name: Hosono, Mr. Masabumi, Class: 2, Fare: 13.0
Name: Connolly, Miss. Kate, Class: 3, Fare: 7.75
Name: Barber, Miss. Ellen "Nellie", Class: 1, Fare: 78.85
Name: Bishop, Mrs. Dickinson H (Helen Walton), Class: 1, Fare: 91.0792
Name: Levy, Mr. Rene Jacques, Class: 2, Fare: 12.875
Name: Haas, Miss. Aloisia, Class: 3, Fare: 8.85
Name: Mineff, Mr. Ivan, Class: 3, Fare: 7.8958
Name: Lewy, Mr. Ervin G, Class: 1, Fare: 27.7208
Name: Hanna, Mr. Mansour, Class: 3, Fare: 7.2292
Name: Allison, Miss. Helen Loraine, Class: 1, Fare: 151.55
Name: Saalfeld, Mr. Adolphe, Class: 1, Fare: 30.5
Name: Baxter, Mrs. James (Helene DeLaudeniere Chaput), Class: 1, Fare: 247.5208
Name: Kelly, Miss. Anna Katherine "Annie Kate", Class: 3, Fare: 7.75
Name: McCoy, Mr. Bernard, Class: 3, Fare: 23.25
Name: Johnson, Mr. William Cahoone Jr, Class: 3, Fare: 0.0
Name: Keane, Miss. Nora A, Class: 2, Fare: 12.35
Name: Williams, Mr. Howard Hugh "Harry", Class: 3, Fare: 8.05
Name: Allison, Master. Hudson Trevor, Class: 1, Fare: 151.55
Name: Fleming, Miss. Margaret, Class: 1, Fare: 110.8833
Name: Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo), Class: 1, Fare: 108.9
Name: Abelson, Mr. Samuel, Class: 2, Fare: 24.0
Name: Francatelli, Miss. Laura Mabel, Class: 1, Fare: 56.9292
Name: Hays, Miss. Margaret Bechstein, Class: 1, Fare: 83.1583
Name: Ryerson, Miss. Emily Borie, Class: 1, Fare: 262.375
Name: Lahtinen, Mrs. William (Anna Sylfven), Class: 2, Fare: 26.0
Name: Hendekovic, Mr. Ignjac, Class: 3, Fare: 7.8958
Name: Hart, Mr. Benjamin, Class: 2, Fare: 26.25
Name: Nilsson, Miss. Helmina Josefina, Class: 3, Fare: 7.8542
Name: Kantor, Mrs. Sinai (Miriam Sternin), Class: 2, Fare: 26.0
Name: Moraweck, Dr. Ernest, Class: 2, Fare: 14.0
Name: Wick, Miss. Mary Natalie, Class: 1, Fare: 164.8667
Name: Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone), Class: 1, Fare: 134.5
Name: Dennis, Mr. Samuel, Class: 3, Fare: 7.25
Name: Danoff, Mr. Yoto, Class: 3, Fare: 7.8958
Name: Slayter, Miss. Hilda Mary, Class: 2, Fare: 12.35
Name: Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh), Class: 2, Fare: 29.0
Name: Sage, Mr. George John Jr, Class: 3, Fare: 69.55
Name: Young, Miss. Marie Grice, Class: 1, Fare: 135.6333
Name: Nysveen, Mr. Johan Hansen, Class: 3, Fare: 6.2375
Name: Ball, Mrs. (Ada E Hall), Class: 2, Fare: 13.0
Name: Goldsmith, Mrs. Frank John (Emily Alice Brown), Class: 3, Fare: 20.525
Name: Hippach, Miss. Jean Gertrude, Class: 1, Fare: 57.9792
Name: McCoy, Miss. Agnes, Class: 3, Fare: 23.25
Name: Partner, Mr. Austen, Class: 1, Fare: 28.5
Name: Graham, Mr. George Edward, Class: 1, Fare: 153.4625
Name: Vander Planke, Mr. Leo Edmondus, Class: 3, Fare: 18.0
Name: Frauenthal, Mrs. Henry William (Clara Heinsheimer), Class: 1, Fare: 133.65
Name: Denkoff, Mr. Mitto, Class: 3, Fare: 7.8958
Name: Pears, Mr. Thomas Clinton, Class: 1, Fare: 66.6
Name: Burns, Miss. Elizabeth Margaret, Class: 1, Fare: 134.5
Name: Dahl, Mr. Karl Edwart, Class: 3, Fare: 8.05
Name: Blackwell, Mr. Stephen Weart, Class: 1, Fare: 35.5
Name: Navratil, Master. Edmond Roger, Class: 2, Fare: 26.0
Name: Fortune, Miss. Alice Elizabeth, Class: 1, Fare: 263.0
Name: Collander, Mr. Erik Gustaf, Class: 2, Fare: 13.0
Name: Sedgwick, Mr. Charles Frederick Waddington, Class: 2, Fare: 13.0
Name: Fox, Mr. Stanley Hubert, Class: 2, Fare: 13.0
Name: Brown, Miss. Amelia "Mildred", Class: 2, Fare: 13.0
Name: Smith, Miss. Marion Elsie, Class: 2, Fare: 13.0
Name: Davison, Mrs. Thomas Henry (Mary E Finck), Class: 3, Fare: 16.1
Name: Coutts, Master. William Loch "William", Class: 3, Fare: 15.9
Name: Dimic, Mr. Jovan, Class: 3, Fare: 8.6625
Name: Odahl, Mr. Nils Martin, Class: 3, Fare: 9.225
Name: Williams-Lambert, Mr. Fletcher Fellows, Class: 1, Fare: 35.0
Name: Elias, Mr. Tannous, Class: 3, Fare: 7.2292
Name: Arnold-Franchi, Mr. Josef, Class: 3, Fare: 17.8
Name: Yousif, Mr. Wazli, Class: 3, Fare: 7.225
Name: Vanden Steen, Mr. Leo Peter, Class: 3, Fare: 9.5
Name: Bowerman, Miss. Elsie Edith, Class: 1, Fare: 55.0
Name: Funk, Miss. Annie Clemmer, Class: 2, Fare: 13.0
Name: McGovern, Miss. Mary, Class: 3, Fare: 7.8792
Name: Mockler, Miss. Helen Mary "Ellie", Class: 3, Fare: 7.8792
Name: Skoog, Mr. Wilhelm, Class: 3, Fare: 27.9
Name: del Carlo, Mr. Sebastiano, Class: 2, Fare: 27.7208
Name: Barbara, Mrs. (Catherine David), Class: 3, Fare: 14.4542
Name: Asim, Mr. Adola, Class: 3, Fare: 7.05
Name: O'Brien, Mr. Thomas, Class: 3, Fare: 15.5
Name: Adahl, Mr. Mauritz Nils Martin, Class: 3, Fare: 7.25
Name: Warren, Mrs. Frank Manley (Anna Sophia Atkinson), Class: 1, Fare: 75.25
Name: Moussa, Mrs. (Mantoura Boulos), Class: 3, Fare: 7.2292
Name: Jermyn, Miss. Annie, Class: 3, Fare: 7.75
Name: Aubart, Mme. Leontine Pauline, Class: 1, Fare: 69.3
Name: Harder, Mr. George Achilles, Class: 1, Fare: 55.4417
Name: Wiklund, Mr. Jakob Alfred, Class: 3, Fare: 6.4958
Name: Beavan, Mr. William Thomas, Class: 3, Fare: 8.05
Name: Ringhini, Mr. Sante, Class: 1, Fare: 135.6333
Name: Palsson, Miss. Stina Viola, Class: 3, Fare: 21.075
Name: Meyer, Mrs. Edgar Joseph (Leila Saks), Class: 1, Fare: 82.1708
Name: Landergren, Miss. Aurora Adelia, Class: 3, Fare: 7.25
Name: Widener, Mr. Harry Elkins, Class: 1, Fare: 211.5
Name: Betros, Mr. Tannous, Class: 3, Fare: 4.0125
Name: Gustafsson, Mr. Karl Gideon, Class: 3, Fare: 7.775
Name: Bidois, Miss. Rosalie, Class: 1, Fare: 227.525
Name: Nakid, Miss. Maria ("Mary"), Class: 3, Fare: 15.7417
Name: Tikkanen, Mr. Juho, Class: 3, Fare: 7.925
Name: Holverson, Mrs. Alexander Oskar (Mary Aline Towner), Class: 1, Fare: 52.0
Name: Plotcharsky, Mr. Vasil, Class: 3, Fare: 7.8958
Name: Davies, Mr. Charles Henry, Class: 2, Fare: 73.5
Name: Goodwin, Master. Sidney Leonard, Class: 3, Fare: 46.9
Name: Buss, Miss. Kate, Class: 2, Fare: 13.0
Name: Sadlier, Mr. Matthew, Class: 3, Fare: 7.7292
Name: Lehmann, Miss. Bertha, Class: 2, Fare: 12.0
Name: Carter, Mr. William Ernest, Class: 1, Fare: 120.0
Name: Jansson, Mr. Carl Olof, Class: 3, Fare: 7.7958
Name: Gustafsson, Mr. Johan Birger, Class: 3, Fare: 7.925
Name: Newell, Miss. Marjorie, Class: 1, Fare: 113.275
Name: Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson), Class: 3, Fare: 16.7
Name: Johansson, Mr. Erik, Class: 3, Fare: 7.7958
Name: Olsson, Miss. Elina, Class: 3, Fare: 7.8542
Name: McKane, Mr. Peter David, Class: 2, Fare: 26.0
Name: Pain, Dr. Alfred, Class: 2, Fare: 10.5
Name: Trout, Mrs. William H (Jessie L), Class: 2, Fare: 12.65
Name: Niskanen, Mr. Juha, Class: 3, Fare: 7.925
Name: Adams, Mr. John, Class: 3, Fare: 8.05
Name: Jussila, Miss. Mari Aina, Class: 3, Fare: 9.825
Name: Hakkarainen, Mr. Pekka Pietari, Class: 3, Fare: 15.85
Name: Oreskovic, Miss. Marija, Class: 3, Fare: 8.6625
Name: Gale, Mr. Shadrach, Class: 2, Fare: 21.0
Name: Widegren, Mr. Carl/Charles Peter, Class: 3, Fare: 7.75
Name: Richards, Master. William Rowe, Class: 2, Fare: 18.75
Name: Birkeland, Mr. Hans Martin Monsen, Class: 3, Fare: 7.775
Name: Lefebre, Miss. Ida, Class: 3, Fare: 25.4667
Name: Sdycoff, Mr. Todor, Class: 3, Fare: 7.8958
Name: Hart, Mr. Henry, Class: 3, Fare: 6.8583
Name: Minahan, Miss. Daisy E, Class: 1, Fare: 90.0
Name: Cunningham, Mr. Alfred Fleming, Class: 2, Fare: 0.0
Name: Sundman, Mr. Johan Julian, Class: 3, Fare: 7.925
Name: Meek, Mrs. Thomas (Annie Louise Rowley), Class: 3, Fare: 8.05
Name: Drew, Mrs. James Vivian (Lulu Thorne Christian), Class: 2, Fare: 32.5
Name: Silven, Miss. Lyyli Karoliina, Class: 2, Fare: 13.0
Name: Matthews, Mr. William John, Class: 2, Fare: 13.0
Name: Van Impe, Miss. Catharina, Class: 3, Fare: 24.15
Name: Gheorgheff, Mr. Stanio, Class: 3, Fare: 7.8958
Name: Charters, Mr. David, Class: 3, Fare: 7.7333
Name: Zimmerman, Mr. Leo, Class: 3, Fare: 7.875
Name: Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren), Class: 3, Fare: 14.4
Name: Rosblom, Mr. Viktor Richard, Class: 3, Fare: 20.2125
Name: Wiseman, Mr. Phillippe, Class: 3, Fare: 7.25
Name: Clarke, Mrs. Charles V (Ada Maria Winfield), Class: 2, Fare: 26.0
Name: Phillips, Miss. Kate Florence ("Mrs Kate Louise Phillips Marshall"), Class: 2, Fare: 26.0
Name: Flynn, Mr. James, Class: 3, Fare: 7.75
Name: Pickard, Mr. Berk (Berk Trembisky), Class: 3, Fare: 8.05
Name: Bjornstrom-Steffansson, Mr. Mauritz Hakan, Class: 1, Fare: 26.55
Name: Thorneycroft, Mrs. Percival (Florence Kate White), Class: 3, Fare: 16.1
Name: Louch, Mrs. Charles Alexander (Alice Adelaide Slow), Class: 2, Fare: 26.0
Name: Kallio, Mr. Nikolai Erland, Class: 3, Fare: 7.125
Name: Silvey, Mr. William Baird, Class: 1, Fare: 55.9
Name: Carter, Miss. Lucile Polk, Class: 1, Fare: 120.0
Name: Ford, Miss. Doolina Margaret "Daisy", Class: 3, Fare: 34.375
Name: Richards, Mrs. Sidney (Emily Hocking), Class: 2, Fare: 18.75
Name: Fortune, Mr. Mark, Class: 1, Fare: 263.0
Name: Kvillner, Mr. Johan Henrik Johannesson, Class: 2, Fare: 10.5
Name: Hart, Mrs. Benjamin (Esther Ada Bloomfield), Class: 2, Fare: 26.25
Name: Hampe, Mr. Leon, Class: 3, Fare: 9.5
Name: Petterson, Mr. Johan Emil, Class: 3, Fare: 7.775
Name: Reynaldo, Ms. Encarnacion, Class: 2, Fare: 13.0
Name: Johannesen-Bratthammer, Mr. Bernt, Class: 3, Fare: 8.1125
Name: Dodge, Master. Washington, Class: 1, Fare: 81.8583
Name: Mellinger, Miss. Madeleine Violet, Class: 2, Fare: 19.5
Name: Seward, Mr. Frederic Kimber, Class: 1, Fare: 26.55
Name: Baclini, Miss. Marie Catherine, Class: 3, Fare: 19.2583
Name: Peuchen, Major. Arthur Godfrey, Class: 1, Fare: 30.5
Name: West, Mr. Edwy Arthur, Class: 2, Fare: 27.75
Name: Hagland, Mr. Ingvald Olai Olsen, Class: 3, Fare: 19.9667
Name: Foreman, Mr. Benjamin Laventall, Class: 1, Fare: 27.75
Name: Goldenberg, Mr. Samuel L, Class: 1, Fare: 89.1042
Name: Peduzzi, Mr. Joseph, Class: 3, Fare: 8.05
Name: Jalsevac, Mr. Ivan, Class: 3, Fare: 7.8958
Name: Millet, Mr. Francis Davis, Class: 1, Fare: 26.55
Name: Kenyon, Mrs. Frederick R (Marion), Class: 1, Fare: 51.8625
Name: Toomey, Miss. Ellen, Class: 2, Fare: 10.5
Name: O'Connor, Mr. Maurice, Class: 3, Fare: 7.75
Name: Anderson, Mr. Harry, Class: 1, Fare: 26.55
Name: Morley, Mr. William, Class: 3, Fare: 8.05
Name: Gee, Mr. Arthur H, Class: 1, Fare: 38.5
Name: Milling, Mr. Jacob Christian, Class: 2, Fare: 13.0
Name: Maisner, Mr. Simon, Class: 3, Fare: 8.05
Name: Goncalves, Mr. Manuel Estanslas, Class: 3, Fare: 7.05
Name: Campbell, Mr. William, Class: 2, Fare: 0.0
Name: Smart, Mr. John Montgomery, Class: 1, Fare: 26.55
Name: Scanlan, Mr. James, Class: 3, Fare: 7.725
Name: Baclini, Miss. Helene Barbara, Class: 3, Fare: 19.2583
Name: Keefe, Mr. Arthur, Class: 3, Fare: 7.25
Name: Cacic, Mr. Luka, Class: 3, Fare: 8.6625
Name: West, Mrs. Edwy Arthur (Ada Mary Worth), Class: 2, Fare: 27.75
Name: Jerwan, Mrs. Amin S (Marie Marthe Thuillard), Class: 2, Fare: 13.7917
Name: Strandberg, Miss. Ida Sofia, Class: 3, Fare: 9.8375
Name: Clifford, Mr. George Quincy, Class: 1, Fare: 52.0
Name: Renouf, Mr. Peter Henry, Class: 2, Fare: 21.0
Name: Braund, Mr. Lewis Richard, Class: 3, Fare: 7.0458
Name: Karlsson, Mr. Nils August, Class: 3, Fare: 7.5208
Name: Hirvonen, Miss. Hildur E, Class: 3, Fare: 12.2875
Name: Goodwin, Master. Harold Victor, Class: 3, Fare: 46.9
Name: Frost, Mr. Anthony Wood "Archie", Class: 2, Fare: 0.0
Name: Rouse, Mr. Richard Henry, Class: 3, Fare: 8.05
Name: Turkula, Mrs. (Hedwig), Class: 3, Fare: 9.5875
Name: Bishop, Mr. Dickinson H, Class: 1, Fare: 91.0792
Name: Lefebre, Miss. Jeannie, Class: 3, Fare: 25.4667
Name: Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby), Class: 1, Fare: 90.0
Name: Kent, Mr. Edward Austin, Class: 1, Fare: 29.7
Name: Somerton, Mr. Francis William, Class: 3, Fare: 8.05
Name: Coutts, Master. Eden Leslie "Neville", Class: 3, Fare: 15.9
Name: Hagland, Mr. Konrad Mathias Reiersen, Class: 3, Fare: 19.9667
Name: Windelov, Mr. Einar, Class: 3, Fare: 7.25
Name: Molson, Mr. Harry Markland, Class: 1, Fare: 30.5
Name: Artagaveytia, Mr. Ramon, Class: 1, Fare: 49.5042
Name: Stanley, Mr. Edward Roland, Class: 3, Fare: 8.05
Name: Yousseff, Mr. Gerious, Class: 3, Fare: 14.4583
Name: Eustis, Miss. Elizabeth Mussey, Class: 1, Fare: 78.2667
Name: Shellard, Mr. Frederick William, Class: 3, Fare: 15.1
Name: Allison, Mrs. Hudson J C (Bessie Waldo Daniels), Class: 1, Fare: 151.55
Name: Svensson, Mr. Olof, Class: 3, Fare: 7.7958
Name: Calic, Mr. Petar, Class: 3, Fare: 8.6625
Name: Canavan, Miss. Mary, Class: 3, Fare: 7.75
Name: O'Sullivan, Miss. Bridget Mary, Class: 3, Fare: 7.6292
Name: Laitinen, Miss. Kristina Sofia, Class: 3, Fare: 9.5875
Name: Maioni, Miss. Roberta, Class: 1, Fare: 86.5
Name: Penasco y Castellana, Mr. Victor de Satode, Class: 1, Fare: 108.9
Name: Quick, Mrs. Frederick Charles (Jane Richards), Class: 2, Fare: 26.0
Name: Bradley, Mr. George ("George Arthur Brayton"), Class: 1, Fare: 26.55
Name: Olsen, Mr. Henry Margido, Class: 3, Fare: 22.525
Name: Lang, Mr. Fang, Class: 3, Fare: 56.4958
Name: Daly, Mr. Eugene Patrick, Class: 3, Fare: 7.75
Name: Webber, Mr. James, Class: 3, Fare: 8.05
Name: McGough, Mr. James Robert, Class: 1, Fare: 26.2875
Name: Rothschild, Mrs. Martin (Elizabeth L. Barrett), Class: 1, Fare: 59.4
Name: Coleff, Mr. Satio, Class: 3, Fare: 7.4958
Name: Walker, Mr. William Anderson, Class: 1, Fare: 34.0208
Name: Lemore, Mrs. (Amelia Milley), Class: 2, Fare: 10.5
Name: Ryan, Mr. Patrick, Class: 3, Fare: 24.15
Name: Angle, Mrs. William A (Florence "Mary" Agnes Hughes), Class: 2, Fare: 26.0
Name: Pavlovic, Mr. Stefo, Class: 3, Fare: 7.8958
Name: Perreault, Miss. Anne, Class: 1, Fare: 93.5
Name: Vovk, Mr. Janko, Class: 3, Fare: 7.8958
Name: Lahoud, Mr. Sarkis, Class: 3, Fare: 7.225
Name: Hippach, Mrs. Louis Albert (Ida Sophia Fischer), Class: 1, Fare: 57.9792
Name: Kassem, Mr. Fared, Class: 3, Fare: 7.2292
Name: Farrell, Mr. James, Class: 3, Fare: 7.75
Name: Ridsdale, Miss. Lucy, Class: 2, Fare: 10.5
Name: Farthing, Mr. John, Class: 1, Fare: 221.7792
Name: Salonen, Mr. Johan Werner, Class: 3, Fare: 7.925
Name: Hocking, Mr. Richard George, Class: 2, Fare: 11.5
Name: Quick, Miss. Phyllis May, Class: 2, Fare: 26.0
Name: Toufik, Mr. Nakli, Class: 3, Fare: 7.2292
Name: Elias, Mr. Joseph Jr, Class: 3, Fare: 7.2292
Name: Peter, Mrs. Catherine (Catherine Rizk), Class: 3, Fare: 22.3583
Name: Cacic, Miss. Marija, Class: 3, Fare: 8.6625
Name: Hart, Miss. Eva Miriam, Class: 2, Fare: 26.25
Name: Butt, Major. Archibald Willingham, Class: 1, Fare: 26.55
Name: LeRoy, Miss. Bertha, Class: 1, Fare: 106.425
Name: Risien, Mr. Samuel Beard, Class: 3, Fare: 14.5
Name: Frolicher, Miss. Hedwig Margaritha, Class: 1, Fare: 49.5
Name: Crosby, Miss. Harriet R, Class: 1, Fare: 71.0
Name: Andersson, Miss. Ingeborg Constanzia, Class: 3, Fare: 31.275
Name: Andersson, Miss. Sigrid Elisabeth, Class: 3, Fare: 31.275
Name: Beane, Mr. Edward, Class: 2, Fare: 26.0
Name: Douglas, Mr. Walter Donald, Class: 1, Fare: 106.425
Name: Nicholson, Mr. Arthur Ernest, Class: 1, Fare: 26.0
Name: Beane, Mrs. Edward (Ethel Clarke), Class: 2, Fare: 26.0
Name: Padro y Manent, Mr. Julian, Class: 2, Fare: 13.8625
Name: Goldsmith, Mr. Frank John, Class: 3, Fare: 20.525
Name: Davies, Master. John Morgan Jr, Class: 2, Fare: 36.75
Name: Thayer, Mr. John Borland Jr, Class: 1, Fare: 110.8833
Name: Sharp, Mr. Percival James R, Class: 2, Fare: 26.0
Name: O'Brien, Mr. Timothy, Class: 3, Fare: 7.8292
Name: Leeni, Mr. Fahim ("Philip Zenni"), Class: 3, Fare: 7.225
Name: Ohman, Miss. Velin, Class: 3, Fare: 7.775
Name: Wright, Mr. George, Class: 1, Fare: 26.55
Name: Duff Gordon, Lady. (Lucille Christiana Sutherland) ("Mrs Morgan"), Class: 1, Fare: 39.6
Name: Robbins, Mr. Victor, Class: 1, Fare: 227.525
Name: Taussig, Mrs. Emil (Tillie Mandelbaum), Class: 1, Fare: 79.65
Name: de Messemaeker, Mrs. Guillaume Joseph (Emma), Class: 3, Fare: 17.4
Name: Morrow, Mr. Thomas Rowan, Class: 3, Fare: 7.75
Name: Sivic, Mr. Husein, Class: 3, Fare: 7.8958
Name: Norman, Mr. Robert Douglas, Class: 2, Fare: 13.5
Name: Simmons, Mr. John, Class: 3, Fare: 8.05
Name: Meanwell, Miss. (Marion Ogden), Class: 3, Fare: 8.05
Name: Davies, Mr. Alfred J, Class: 3, Fare: 24.15
Name: Stoytcheff, Mr. Ilia, Class: 3, Fare: 7.8958
Name: Palsson, Mrs. Nils (Alma Cornelia Berglund), Class: 3, Fare: 21.075
Name: Doharr, Mr. Tannous, Class: 3, Fare: 7.2292
Name: Jonsson, Mr. Carl, Class: 3, Fare: 7.8542
Name: Harris, Mr. George, Class: 2, Fare: 10.5
Name: Appleton, Mrs. Edward Dale (Charlotte Lamson), Class: 1, Fare: 51.4792
Name: Flynn, Mr. John Irwin ("Irving"), Class: 1, Fare: 26.3875
Name: Kelly, Miss. Mary, Class: 3, Fare: 7.75
Name: Rush, Mr. Alfred George John, Class: 3, Fare: 8.05
Name: Patchett, Mr. George, Class: 3, Fare: 14.5
Name: Garside, Miss. Ethel, Class: 2, Fare: 13.0
Name: Silvey, Mrs. William Baird (Alice Munger), Class: 1, Fare: 55.9
Name: Caram, Mrs. Joseph (Maria Elias), Class: 3, Fare: 14.4583
Name: Jussila, Mr. Eiriik, Class: 3, Fare: 7.925
Name: Christy, Miss. Julie Rachel, Class: 2, Fare: 30.0
Name: Thayer, Mrs. John Borland (Marian Longstreth Morris), Class: 1, Fare: 110.8833
Name: Downton, Mr. William James, Class: 2, Fare: 26.0
Name: Ross, Mr. John Hugo, Class: 1, Fare: 40.125
Name: Paulner, Mr. Uscher, Class: 3, Fare: 8.7125
Name: Taussig, Miss. Ruth, Class: 1, Fare: 79.65
Name: Jarvis, Mr. John Denzil, Class: 2, Fare: 15.0
Name: Frolicher-Stehli, Mr. Maxmillian, Class: 1, Fare: 79.2
Name: Gilinski, Mr. Eliezer, Class: 3, Fare: 8.05
Name: Murdlin, Mr. Joseph, Class: 3, Fare: 8.05
Name: Rintamaki, Mr. Matti, Class: 3, Fare: 7.125
Name: Stephenson, Mrs. Walter Bertram (Martha Eustis), Class: 1, Fare: 78.2667
Name: Elsbury, Mr. William James, Class: 3, Fare: 7.25
Name: Bourke, Miss. Mary, Class: 3, Fare: 7.75
Name: Chapman, Mr. John Henry, Class: 2, Fare: 26.0
Name: Van Impe, Mr. Jean Baptiste, Class: 3, Fare: 24.15
Name: Leitch, Miss. Jessie Wills, Class: 2, Fare: 33.0
Name: Johnson, Mr. Alfred, Class: 3, Fare: 0.0
Name: Boulos, Mr. Hanna, Class: 3, Fare: 7.225
Name: Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan"), Class: 1, Fare: 56.9292
Name: Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy), Class: 2, Fare: 27.0
Name: Slabenoff, Mr. Petco, Class: 3, Fare: 7.8958
Name: Harrington, Mr. Charles H, Class: 1, Fare: 42.4
Name: Torber, Mr. Ernst William, Class: 3, Fare: 8.05
Name: Homer, Mr. Harry ("Mr E Haven"), Class: 1, Fare: 26.55
Name: Lindell, Mr. Edvard Bengtsson, Class: 3, Fare: 15.55
Name: Karaic, Mr. Milan, Class: 3, Fare: 7.8958
Name: Daniel, Mr. Robert Williams, Class: 1, Fare: 30.5
Name: Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue), Class: 2, Fare: 41.5792
Name: Shutes, Miss. Elizabeth W, Class: 1, Fare: 153.4625
Name: Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren), Class: 3, Fare: 31.275
Name: Jardin, Mr. Jose Neto, Class: 3, Fare: 7.05
Name: Murphy, Miss. Margaret Jane, Class: 3, Fare: 15.5
Name: Horgan, Mr. John, Class: 3, Fare: 7.75
Name: Brocklebank, Mr. William Alfred, Class: 3, Fare: 8.05
Name: Herman, Miss. Alice, Class: 2, Fare: 65.0
Name: Danbom, Mr. Ernst Gilbert, Class: 3, Fare: 14.4
Name: Lobb, Mrs. William Arthur (Cordelia K Stanlick), Class: 3, Fare: 16.1
Name: Becker, Miss. Marion Louise, Class: 2, Fare: 39.0
Name: Gavey, Mr. Lawrence, Class: 2, Fare: 10.5
Name: Yasbeck, Mr. Antoni, Class: 3, Fare: 14.4542
Name: Kimball, Mr. Edwin Nelson Jr, Class: 1, Fare: 52.5542
Name: Nakid, Mr. Sahid, Class: 3, Fare: 15.7417
Name: Hansen, Mr. Henry Damsgaard, Class: 3, Fare: 7.8542
Name: Bowen, Mr. David John "Dai", Class: 3, Fare: 16.1
Name: Sutton, Mr. Frederick, Class: 1, Fare: 32.3208
Name: Kirkland, Rev. Charles Leonard, Class: 2, Fare: 12.35
Name: Longley, Miss. Gretchen Fiske, Class: 1, Fare: 77.9583
Name: Bostandyeff, Mr. Guentcho, Class: 3, Fare: 7.8958
Name: O'Connell, Mr. Patrick D, Class: 3, Fare: 7.7333
Name: Barkworth, Mr. Algernon Henry Wilson, Class: 1, Fare: 30.0
Name: Lundahl, Mr. Johan Svensson, Class: 3, Fare: 7.0542
Name: Stahelin-Maeglin, Dr. Max, Class: 1, Fare: 30.5
Name: Parr, Mr. William Henry Marsh, Class: 1, Fare: 0.0
Name: Skoog, Miss. Mabel, Class: 3, Fare: 27.9
Name: Davis, Miss. Mary, Class: 2, Fare: 13.0
Name: Leinonen, Mr. Antti Gustaf, Class: 3, Fare: 7.925
Name: Collyer, Mr. Harvey, Class: 2, Fare: 26.25
Name: Panula, Mrs. Juha (Maria Emilia Ojala), Class: 3, Fare: 39.6875
Name: Thorneycroft, Mr. Percival, Class: 3, Fare: 16.1
Name: Jensen, Mr. Hans Peder, Class: 3, Fare: 7.8542
Name: Sagesser, Mlle. Emma, Class: 1, Fare: 69.3
Name: Skoog, Miss. Margit Elizabeth, Class: 3, Fare: 27.9
Name: Foo, Mr. Choong, Class: 3, Fare: 56.4958
Name: Baclini, Miss. Eugenie, Class: 3, Fare: 19.2583
Name: Harper, Mr. Henry Sleeper, Class: 1, Fare: 76.7292
Name: Cor, Mr. Liudevit, Class: 3, Fare: 7.8958
Name: Simonius-Blumer, Col. Oberst Alfons, Class: 1, Fare: 35.5
Name: Willey, Mr. Edward, Class: 3, Fare: 7.55
Name: Stanley, Miss. Amy Zillah Elsie, Class: 3, Fare: 7.55
Name: Mitkoff, Mr. Mito, Class: 3, Fare: 7.8958
Name: Doling, Miss. Elsie, Class: 2, Fare: 23.0
Name: Kalvik, Mr. Johannes Halvorsen, Class: 3, Fare: 8.4333
Name: O'Leary, Miss. Hanora "Norah", Class: 3, Fare: 7.8292
Name: Hegarty, Miss. Hanora "Nora", Class: 3, Fare: 6.75
Name: Hickman, Mr. Leonard Mark, Class: 2, Fare: 73.5
Name: Radeff, Mr. Alexander, Class: 3, Fare: 7.8958
Name: Bourke, Mrs. John (Catherine), Class: 3, Fare: 15.5
Name: Eitemiller, Mr. George Floyd, Class: 2, Fare: 13.0
Name: Newell, Mr. Arthur Webster, Class: 1, Fare: 113.275
Name: Frauenthal, Dr. Henry William, Class: 1, Fare: 133.65
Name: Badt, Mr. Mohamed, Class: 3, Fare: 7.225
Name: Colley, Mr. Edward Pomeroy, Class: 1, Fare: 25.5875
Name: Coleff, Mr. Peju, Class: 3, Fare: 7.4958
Name: Lindqvist, Mr. Eino William, Class: 3, Fare: 7.925
Name: Hickman, Mr. Lewis, Class: 2, Fare: 73.5
Name: Butler, Mr. Reginald Fenton, Class: 2, Fare: 13.0
Name: Rommetvedt, Mr. Knud Paust, Class: 3, Fare: 7.775
Name: Cook, Mr. Jacob, Class: 3, Fare: 8.05
Name: Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright), Class: 1, Fare: 52.0
Name: Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford), Class: 2, Fare: 39.0
Name: Davidson, Mr. Thornton, Class: 1, Fare: 52.0
Name: Mitchell, Mr. Henry Michael, Class: 2, Fare: 10.5
Name: Wilhelms, Mr. Charles, Class: 2, Fare: 13.0
Name: Watson, Mr. Ennis Hastings, Class: 2, Fare: 0.0
Name: Edvardsson, Mr. Gustaf Hjalmar, Class: 3, Fare: 7.775
Name: Sawyer, Mr. Frederick Charles, Class: 3, Fare: 8.05
Name: Turja, Miss. Anna Sofia, Class: 3, Fare: 9.8417
Name: Goodwin, Mrs. Frederick (Augusta Tyler), Class: 3, Fare: 46.9
Name: Cardeza, Mr. Thomas Drake Martinez, Class: 1, Fare: 512.3292
Name: Peters, Miss. Katie, Class: 3, Fare: 8.1375
Name: Hassab, Mr. Hammad, Class: 1, Fare: 76.7292
Name: Olsvigen, Mr. Thor Anderson, Class: 3, Fare: 9.225
Name: Goodwin, Mr. Charles Edward, Class: 3, Fare: 46.9
Name: Brown, Mr. Thomas William Solomon, Class: 2, Fare: 39.0
Name: Laroche, Mr. Joseph Philippe Lemercier, Class: 2, Fare: 41.5792
Name: Panula, Mr. Jaako Arnold, Class: 3, Fare: 39.6875
Name: Dakic, Mr. Branko, Class: 3, Fare: 10.1708
Name: Fischer, Mr. Eberhard Thelander, Class: 3, Fare: 7.7958
Name: Madill, Miss. Georgette Alexandra, Class: 1, Fare: 211.3375
Name: Dick, Mr. Albert Adrian, Class: 1, Fare: 57.0
Name: Karun, Miss. Manca, Class: 3, Fare: 13.4167
Name: Lam, Mr. Ali, Class: 3, Fare: 56.4958
Name: Saad, Mr. Khalil, Class: 3, Fare: 7.225
Name: Weir, Col. John, Class: 1, Fare: 26.55
Name: Chapman, Mr. Charles Henry, Class: 2, Fare: 13.5
Name: Kelly, Mr. James, Class: 3, Fare: 8.05
Name: Mullens, Miss. Katherine "Katie", Class: 3, Fare: 7.7333
Name: Thayer, Mr. John Borland, Class: 1, Fare: 110.8833
Name: Humblen, Mr. Adolf Mathias Nicolai Olsen, Class: 3, Fare: 7.65
Name: Astor, Mrs. John Jacob (Madeleine Talmadge Force), Class: 1, Fare: 227.525
Name: Silverthorne, Mr. Spencer Victor, Class: 1, Fare: 26.2875
Name: Barbara, Miss. Saiide, Class: 3, Fare: 14.4542
Name: Gallagher, Mr. Martin, Class: 3, Fare: 7.7417
Name: Hansen, Mr. Henrik Juul, Class: 3, Fare: 7.8542
Name: Morley, Mr. Henry Samuel ("Mr Henry Marshall"), Class: 2, Fare: 26.0
Name: Kelly, Mrs. Florence "Fannie", Class: 2, Fare: 13.5
Name: Calderhead, Mr. Edward Pennington, Class: 1, Fare: 26.2875
Name: Cleaver, Miss. Alice, Class: 1, Fare: 151.55
Name: Moubarek, Master. Halim Gonios ("William George"), Class: 3, Fare: 15.2458
Name: Mayne, Mlle. Berthe Antonine ("Mrs de Villiers"), Class: 1, Fare: 49.5042
Name: Klaber, Mr. Herman, Class: 1, Fare: 26.55
Name: Taylor, Mr. Elmer Zebley, Class: 1, Fare: 52.0
Name: Larsson, Mr. August Viktor, Class: 3, Fare: 9.4833
Name: Greenberg, Mr. Samuel, Class: 2, Fare: 13.0
Name: Soholt, Mr. Peter Andreas Lauritz Andersen, Class: 3, Fare: 7.65
Name: Endres, Miss. Caroline Louise, Class: 1, Fare: 227.525
Name: Troutt, Miss. Edwina Celia "Winnie", Class: 2, Fare: 10.5
Name: McEvoy, Mr. Michael, Class: 3, Fare: 15.5
Name: Johnson, Mr. Malkolm Joackim, Class: 3, Fare: 7.775
Name: Harper, Miss. Annie Jessie "Nina", Class: 2, Fare: 33.0
Name: Jensen, Mr. Svend Lauritz, Class: 3, Fare: 7.0542
Name: Gillespie, Mr. William Henry, Class: 2, Fare: 13.0
Name: Hodges, Mr. Henry Price, Class: 2, Fare: 13.0
Name: Chambers, Mr. Norman Campbell, Class: 1, Fare: 53.1
Name: Oreskovic, Mr. Luka, Class: 3, Fare: 8.6625
Name: Renouf, Mrs. Peter Henry (Lillian Jefferys), Class: 2, Fare: 21.0
Name: Mannion, Miss. Margareth, Class: 3, Fare: 7.7375
Name: Bryhl, Mr. Kurt Arnold Gottfrid, Class: 2, Fare: 26.0
Name: Ilmakangas, Miss. Pieta Sofia, Class: 3, Fare: 7.925
Name: Allen, Miss. Elisabeth Walton, Class: 1, Fare: 211.3375
Name: Hassan, Mr. Houssein G N, Class: 3, Fare: 18.7875
Name: Knight, Mr. Robert J, Class: 2, Fare: 0.0
Name: Berriman, Mr. William John, Class: 2, Fare: 13.0
Name: Troupiansky, Mr. Moses Aaron, Class: 2, Fare: 13.0
Name: Williams, Mr. Leslie, Class: 3, Fare: 16.1
Name: Ford, Mrs. Edward (Margaret Ann Watson), Class: 3, Fare: 34.375
Name: Lesurer, Mr. Gustave J, Class: 1, Fare: 512.3292
Name: Ivanoff, Mr. Kanio, Class: 3, Fare: 7.8958
Name: Nankoff, Mr. Minko, Class: 3, Fare: 7.8958
Name: Hawksford, Mr. Walter James, Class: 1, Fare: 30.0
Name: Cavendish, Mr. Tyrell William, Class: 1, Fare: 78.85
Name: Ryerson, Miss. Susan Parker "Suzette", Class: 1, Fare: 262.375
Name: McNamee, Mr. Neal, Class: 3, Fare: 16.1
Name: Stranden, Mr. Juho, Class: 3, Fare: 7.925
Name: Crosby, Capt. Edward Gifford, Class: 1, Fare: 71.0
Name: Abbott, Mr. Rossmore Edward, Class: 3, Fare: 20.25
Name: Sinkkonen, Miss. Anna, Class: 2, Fare: 13.0
Name: Marvin, Mr. Daniel Warner, Class: 1, Fare: 53.1
Name: Connaghton, Mr. Michael, Class: 3, Fare: 7.75
Name: Wells, Miss. Joan, Class: 2, Fare: 23.0
Name: Moor, Master. Meier, Class: 3, Fare: 12.475
Name: Vande Velde, Mr. Johannes Joseph, Class: 3, Fare: 9.5
Name: Jonkoff, Mr. Lalio, Class: 3, Fare: 7.8958
Name: Herman, Mrs. Samuel (Jane Laver), Class: 2, Fare: 65.0
Name: Hamalainen, Master. Viljo, Class: 2, Fare: 14.5
Name: Carlsson, Mr. August Sigfrid, Class: 3, Fare: 7.7958
Name: Bailey, Mr. Percy Andrew, Class: 2, Fare: 11.5
Name: Theobald, Mr. Thomas Leonard, Class: 3, Fare: 8.05
Name: Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards), Class: 1, Fare: 86.5
Name: Garfirth, Mr. John, Class: 3, Fare: 14.5
Name: Nirva, Mr. Iisakki Antino Aijo, Class: 3, Fare: 7.125
Name: Barah, Mr. Hanna Assi, Class: 3, Fare: 7.2292
Name: Carter, Mrs. William Ernest (Lucile Polk), Class: 1, Fare: 120.0
Name: Eklund, Mr. Hans Linus, Class: 3, Fare: 7.775
Name: Hogeboom, Mrs. John C (Anna Andrews), Class: 1, Fare: 77.9583
Name: Brewe, Dr. Arthur Jackson, Class: 1, Fare: 39.6
Name: Mangan, Miss. Mary, Class: 3, Fare: 7.75
Name: Moran, Mr. Daniel J, Class: 3, Fare: 24.15
Name: Gronnestad, Mr. Daniel Danielsen, Class: 3, Fare: 8.3625
Name: Lievens, Mr. Rene Aime, Class: 3, Fare: 9.5
Name: Jensen, Mr. Niels Peder, Class: 3, Fare: 7.8542
Name: Mack, Mrs. (Mary), Class: 2, Fare: 10.5
Name: Elias, Mr. Dibo, Class: 3, Fare: 7.225
Name: Hocking, Mrs. Elizabeth (Eliza Needs), Class: 2, Fare: 23.0
Name: Myhrman, Mr. Pehr Fabian Oliver Malkolm, Class: 3, Fare: 7.75
Name: Tobin, Mr. Roger, Class: 3, Fare: 7.75
Name: Emanuel, Miss. Virginia Ethel, Class: 3, Fare: 12.475
Name: Kilgannon, Mr. Thomas J, Class: 3, Fare: 7.7375
Name: Robert, Mrs. Edward Scott (Elisabeth Walton McMillan), Class: 1, Fare: 211.3375
Name: Ayoub, Miss. Banoura, Class: 3, Fare: 7.2292
Name: Dick, Mrs. Albert Adrian (Vera Gillespie), Class: 1, Fare: 57.0
Name: Long, Mr. Milton Clyde, Class: 1, Fare: 30.0
Name: Johnston, Mr. Andrew G, Class: 3, Fare: 23.45
Name: Ali, Mr. William, Class: 3, Fare: 7.05
Name: Harmer, Mr. Abraham (David Lishin), Class: 3, Fare: 7.25
Name: Sjoblom, Miss. Anna Sofia, Class: 3, Fare: 7.4958
Name: Rice, Master. George Hugh, Class: 3, Fare: 29.125
Name: Dean, Master. Bertram Vere, Class: 3, Fare: 20.575
Name: Guggenheim, Mr. Benjamin, Class: 1, Fare: 79.2
Name: Keane, Mr. Andrew "Andy", Class: 3, Fare: 7.75
Name: Gaskell, Mr. Alfred, Class: 2, Fare: 26.0
Name: Sage, Miss. Stella Anna, Class: 3, Fare: 69.55
Name: Hoyt, Mr. William Fisher, Class: 1, Fare: 30.6958
Name: Dantcheff, Mr. Ristiu, Class: 3, Fare: 7.8958
Name: Otter, Mr. Richard, Class: 2, Fare: 13.0
Name: Leader, Dr. Alice (Farnham), Class: 1, Fare: 25.9292
Name: Osman, Mrs. Mara, Class: 3, Fare: 8.6833
Name: Ibrahim Shawah, Mr. Yousseff, Class: 3, Fare: 7.2292
Name: Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert), Class: 3, Fare: 24.15
Name: Ponesell, Mr. Martin, Class: 2, Fare: 13.0
Name: Collyer, Mrs. Harvey (Charlotte Annie Tate), Class: 2, Fare: 26.25
Name: Carter, Master. William Thornton II, Class: 1, Fare: 120.0
Name: Thomas, Master. Assad Alexander, Class: 3, Fare: 8.5167
Name: Hedman, Mr. Oskar Arvid, Class: 3, Fare: 6.975
Name: Johansson, Mr. Karl Johan, Class: 3, Fare: 7.775
Name: Andrews, Mr. Thomas Jr, Class: 1, Fare: 0.0
Name: Pettersson, Miss. Ellen Natalia, Class: 3, Fare: 7.775
Name: Meyer, Mr. August, Class: 2, Fare: 13.0
Name: Chambers, Mrs. Norman Campbell (Bertha Griggs), Class: 1, Fare: 53.1
Name: Alexander, Mr. William, Class: 3, Fare: 7.8875
Name: Lester, Mr. James, Class: 3, Fare: 24.15
Name: Slemen, Mr. Richard James, Class: 2, Fare: 10.5
Name: Andersson, Miss. Ebba Iris Alfrida, Class: 3, Fare: 31.275
Name: Tomlin, Mr. Ernest Portage, Class: 3, Fare: 8.05
Name: Fry, Mr. Richard, Class: 1, Fare: 0.0
Name: Heininen, Miss. Wendla Maria, Class: 3, Fare: 7.925
Name: Mallet, Mr. Albert, Class: 2, Fare: 37.0042
Name: Holm, Mr. John Fredrik Alexander, Class: 3, Fare: 6.45
Name: Skoog, Master. Karl Thorsten, Class: 3, Fare: 27.9
Name: Hays, Mrs. Charles Melville (Clara Jennings Gregg), Class: 1, Fare: 93.5
Name: Lulic, Mr. Nikola, Class: 3, Fare: 8.6625
Name: Reuchlin, Jonkheer. John George, Class: 1, Fare: 0.0
Name: Moor, Mrs. (Beila), Class: 3, Fare: 12.475
Name: Panula, Master. Urho Abraham, Class: 3, Fare: 39.6875
Name: Flynn, Mr. John, Class: 3, Fare: 6.95
Name: Lam, Mr. Len, Class: 3, Fare: 56.4958
Name: Mallet, Master. Andre, Class: 2, Fare: 37.0042
Name: McCormack, Mr. Thomas Joseph, Class: 3, Fare: 7.75
Name: Stone, Mrs. George Nelson (Martha Evelyn), Class: 1, Fare: 80.0
Name: Yasbeck, Mrs. Antoni (Selini Alexander), Class: 3, Fare: 14.4542
Name: Richards, Master. George Sibley, Class: 2, Fare: 18.75
Name: Saad, Mr. Amin, Class: 3, Fare: 7.2292
Name: Augustsson, Mr. Albert, Class: 3, Fare: 7.8542
Name: Allum, Mr. Owen George, Class: 3, Fare: 8.3
Name: Compton, Miss. Sara Rebecca, Class: 1, Fare: 83.1583
Name: Pasic, Mr. Jakob, Class: 3, Fare: 8.6625
Name: Sirota, Mr. Maurice, Class: 3, Fare: 8.05
Name: Chip, Mr. Chang, Class: 3, Fare: 56.4958
Name: Marechal, Mr. Pierre, Class: 1, Fare: 29.7
Name: Alhomaki, Mr. Ilmari Rudolf, Class: 3, Fare: 7.925
Name: Mudd, Mr. Thomas Charles, Class: 2, Fare: 10.5
Name: Serepeca, Miss. Augusta, Class: 1, Fare: 31.0
Name: Lemberopolous, Mr. Peter L, Class: 3, Fare: 6.4375
Name: Culumovic, Mr. Jeso, Class: 3, Fare: 8.6625
Name: Abbing, Mr. Anthony, Class: 3, Fare: 7.55
Name: Sage, Mr. Douglas Bullen, Class: 3, Fare: 69.55
Name: Markoff, Mr. Marin, Class: 3, Fare: 7.8958
Name: Harper, Rev. John, Class: 2, Fare: 33.0
Name: Goldenberg, Mrs. Samuel L (Edwiga Grabowska), Class: 1, Fare: 89.1042
Name: Andersson, Master. Sigvard Harald Elias, Class: 3, Fare: 31.275
Name: Svensson, Mr. Johan, Class: 3, Fare: 7.775
Name: Boulos, Miss. Nourelain, Class: 3, Fare: 15.2458
Name: Lines, Miss. Mary Conover, Class: 1, Fare: 39.4
Name: Carter, Mrs. Ernest Courtenay (Lilian Hughes), Class: 2, Fare: 26.0
Name: Aks, Mrs. Sam (Leah Rosen), Class: 3, Fare: 9.35
Name: Wick, Mrs. George Dennick (Mary Hitchcock), Class: 1, Fare: 164.8667
Name: Daly, Mr. Peter Denis , Class: 1, Fare: 26.55
Name: Baclini, Mrs. Solomon (Latifa Qurban), Class: 3, Fare: 19.2583
Name: Razi, Mr. Raihed, Class: 3, Fare: 7.2292
Name: Hansen, Mr. Claus Peter, Class: 3, Fare: 14.1083
Name: Giles, Mr. Frederick Edward, Class: 2, Fare: 11.5
Name: Swift, Mrs. Frederick Joel (Margaret Welles Barron), Class: 1, Fare: 25.9292
Name: Sage, Miss. Dorothy Edith "Dolly", Class: 3, Fare: 69.55
Name: Gill, Mr. John William, Class: 2, Fare: 13.0
Name: Bystrom, Mrs. (Karolina), Class: 2, Fare: 13.0
Name: Duran y More, Miss. Asuncion, Class: 2, Fare: 13.8583
Name: Roebling, Mr. Washington Augustus II, Class: 1, Fare: 50.4958
Name: van Melkebeke, Mr. Philemon, Class: 3, Fare: 9.5
Name: Johnson, Master. Harold Theodor, Class: 3, Fare: 11.1333
Name: Balkic, Mr. Cerin, Class: 3, Fare: 7.8958
Name: Beckwith, Mrs. Richard Leonard (Sallie Monypeny), Class: 1, Fare: 52.5542
Name: Carlsson, Mr. Frans Olof, Class: 1, Fare: 5.0
Name: Vander Cruyssen, Mr. Victor, Class: 3, Fare: 9.0
Name: Abelson, Mrs. Samuel (Hannah Wizosky), Class: 2, Fare: 24.0
Name: Najib, Miss. Adele Kiamie "Jane", Class: 3, Fare: 7.225
Name: Gustafsson, Mr. Alfred Ossian, Class: 3, Fare: 9.8458
Name: Petroff, Mr. Nedelio, Class: 3, Fare: 7.8958
Name: Laleff, Mr. Kristo, Class: 3, Fare: 7.8958
Name: Potter, Mrs. Thomas Jr (Lily Alexenia Wilson), Class: 1, Fare: 83.1583
Name: Shelley, Mrs. William (Imanita Parrish Hall), Class: 2, Fare: 26.0
Name: Markun, Mr. Johann, Class: 3, Fare: 7.8958
Name: Dahlberg, Miss. Gerda Ulrika, Class: 3, Fare: 10.5167
Name: Banfield, Mr. Frederick James, Class: 2, Fare: 10.5
Name: Sutehall, Mr. Henry Jr, Class: 3, Fare: 7.05
Name: Rice, Mrs. William (Margaret Norton), Class: 3, Fare: 29.125
Name: Montvila, Rev. Juozas, Class: 2, Fare: 13.0
Name: Graham, Miss. Margaret Edith, Class: 1, Fare: 30.0
Name: Johnston, Miss. Catherine Helen "Carrie", Class: 3, Fare: 23.45
Name: Behr, Mr. Karl Howell, Class: 1, Fare: 30.0
Name: Dooley, Mr. Patrick, Class: 3, Fare: 7.75

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
for row in df.iter_rows(named=True):
    print(f"Name: {row['Name']}, Class: {row['Pclass']}, Fare: {row['Fare']}")

Output:

Name: Braund, Mr. Owen Harris, Class: 3, Fare: 7.25
Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer), Class: 1, Fare: 71.2833
Name: Heikkinen, Miss. Laina, Class: 3, Fare: 7.925
Name: Futrelle, Mrs. Jacques Heath (Lily May Peel), Class: 1, Fare: 53.1
Name: Allen, Mr. William Henry, Class: 3, Fare: 8.05
Name: Moran, Mr. James, Class: 3, Fare: 8.4583
Name: McCarthy, Mr. Timothy J, Class: 1, Fare: 51.8625
Name: Palsson, Master. Gosta Leonard, Class: 3, Fare: 21.075
Name: Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg), Class: 3, Fare: 11.1333
Name: Nasser, Mrs. Nicholas (Adele Achem), Class: 2, Fare: 30.0708
Name: Sandstrom, Miss. Marguerite Rut, Class: 3, Fare: 16.7
Name: Bonnell, Miss. Elizabeth, Class: 1, Fare: 26.55
Name: Saundercock, Mr. William Henry, Class: 3, Fare: 8.05
Name: Andersson, Mr. Anders Johan, Class: 3, Fare: 31.275
Name: Vestrom, Miss. Hulda Amanda Adolfina, Class: 3, Fare: 7.8542
Name: Hewlett, Mrs. (Mary D Kingcome) , Class: 2, Fare: 16.0
Name: Rice, Master. Eugene, Class: 3, Fare: 29.125
Name: Williams, Mr. Charles Eugene, Class: 2, Fare: 13.0
Name: Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele), Class: 3, Fare: 18.0
Name: Masselmani, Mrs. Fatima, Class: 3, Fare: 7.225
Name: Fynney, Mr. Joseph J, Class: 2, Fare: 26.0
Name: Beesley, Mr. Lawrence, Class: 2, Fare: 13.0
Name: McGowan, Miss. Anna "Annie", Class: 3, Fare: 8.0292
Name: Sloper, Mr. William Thompson, Class: 1, Fare: 35.5
Name: Palsson, Miss. Torborg Danira, Class: 3, Fare: 21.075
Name: Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson), Class: 3, Fare: 31.3875
Name: Emir, Mr. Farred Chehab, Class: 3, Fare: 7.225
Name: Fortune, Mr. Charles Alexander, Class: 1, Fare: 263.0
Name: O'Dwyer, Miss. Ellen "Nellie", Class: 3, Fare: 7.8792
Name: Todoroff, Mr. Lalio, Class: 3, Fare: 7.8958
Name: Uruchurtu, Don. Manuel E, Class: 1, Fare: 27.7208
Name: Spencer, Mrs. William Augustus (Marie Eugenie), Class: 1, Fare: 146.5208
Name: Glynn, Miss. Mary Agatha, Class: 3, Fare: 7.75
Name: Wheadon, Mr. Edward H, Class: 2, Fare: 10.5
Name: Meyer, Mr. Edgar Joseph, Class: 1, Fare: 82.1708
Name: Holverson, Mr. Alexander Oskar, Class: 1, Fare: 52.0
Name: Mamee, Mr. Hanna, Class: 3, Fare: 7.2292
Name: Cann, Mr. Ernest Charles, Class: 3, Fare: 8.05
Name: Vander Planke, Miss. Augusta Maria, Class: 3, Fare: 18.0
Name: Nicola-Yarred, Miss. Jamila, Class: 3, Fare: 11.2417
Name: Ahlin, Mrs. Johan (Johanna Persdotter Larsson), Class: 3, Fare: 9.475
Name: Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott), Class: 2, Fare: 21.0
Name: Kraeff, Mr. Theodor, Class: 3, Fare: 7.8958
Name: Laroche, Miss. Simonne Marie Anne Andree, Class: 2, Fare: 41.5792
Name: Devaney, Miss. Margaret Delia, Class: 3, Fare: 7.8792
Name: Rogers, Mr. William John, Class: 3, Fare: 8.05
Name: Lennon, Mr. Denis, Class: 3, Fare: 15.5
Name: O'Driscoll, Miss. Bridget, Class: 3, Fare: 7.75
Name: Samaan, Mr. Youssef, Class: 3, Fare: 21.6792
Name: Arnold-Franchi, Mrs. Josef (Josefine Franchi), Class: 3, Fare: 17.8
Name: Panula, Master. Juha Niilo, Class: 3, Fare: 39.6875
Name: Nosworthy, Mr. Richard Cater, Class: 3, Fare: 7.8
Name: Harper, Mrs. Henry Sleeper (Myna Haxtun), Class: 1, Fare: 76.7292
Name: Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson), Class: 2, Fare: 26.0
Name: Ostby, Mr. Engelhart Cornelius, Class: 1, Fare: 61.9792
Name: Woolner, Mr. Hugh, Class: 1, Fare: 35.5
Name: Rugg, Miss. Emily, Class: 2, Fare: 10.5
Name: Novel, Mr. Mansouer, Class: 3, Fare: 7.2292
Name: West, Miss. Constance Mirium, Class: 2, Fare: 27.75
Name: Goodwin, Master. William Frederick, Class: 3, Fare: 46.9
Name: Sirayanian, Mr. Orsen, Class: 3, Fare: 7.2292
Name: Icard, Miss. Amelie, Class: 1, Fare: 80.0
Name: Harris, Mr. Henry Birkhardt, Class: 1, Fare: 83.475
Name: Skoog, Master. Harald, Class: 3, Fare: 27.9
Name: Stewart, Mr. Albert A, Class: 1, Fare: 27.7208
Name: Moubarek, Master. Gerios, Class: 3, Fare: 15.2458
Name: Nye, Mrs. (Elizabeth Ramell), Class: 2, Fare: 10.5
Name: Crease, Mr. Ernest James, Class: 3, Fare: 8.1583
Name: Andersson, Miss. Erna Alexandra, Class: 3, Fare: 7.925
Name: Kink, Mr. Vincenz, Class: 3, Fare: 8.6625
Name: Jenkin, Mr. Stephen Curnow, Class: 2, Fare: 10.5
Name: Goodwin, Miss. Lillian Amy, Class: 3, Fare: 46.9
Name: Hood, Mr. Ambrose Jr, Class: 2, Fare: 73.5
Name: Chronopoulos, Mr. Apostolos, Class: 3, Fare: 14.4542
Name: Bing, Mr. Lee, Class: 3, Fare: 56.4958
Name: Moen, Mr. Sigurd Hansen, Class: 3, Fare: 7.65
Name: Staneff, Mr. Ivan, Class: 3, Fare: 7.8958
Name: Moutal, Mr. Rahamin Haim, Class: 3, Fare: 8.05
Name: Caldwell, Master. Alden Gates, Class: 2, Fare: 29.0
Name: Dowdell, Miss. Elizabeth, Class: 3, Fare: 12.475
Name: Waelens, Mr. Achille, Class: 3, Fare: 9.0
Name: Sheerlinck, Mr. Jan Baptist, Class: 3, Fare: 9.5
Name: McDermott, Miss. Brigdet Delia, Class: 3, Fare: 7.7875
Name: Carrau, Mr. Francisco M, Class: 1, Fare: 47.1
Name: Ilett, Miss. Bertha, Class: 2, Fare: 10.5
Name: Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson), Class: 3, Fare: 15.85
Name: Ford, Mr. William Neal, Class: 3, Fare: 34.375
Name: Slocovski, Mr. Selman Francis, Class: 3, Fare: 8.05
Name: Fortune, Miss. Mabel Helen, Class: 1, Fare: 263.0
Name: Celotti, Mr. Francesco, Class: 3, Fare: 8.05
Name: Christmann, Mr. Emil, Class: 3, Fare: 8.05
Name: Andreasson, Mr. Paul Edvin, Class: 3, Fare: 7.8542
Name: Chaffee, Mr. Herbert Fuller, Class: 1, Fare: 61.175
Name: Dean, Mr. Bertram Frank, Class: 3, Fare: 20.575
Name: Coxon, Mr. Daniel, Class: 3, Fare: 7.25
Name: Shorney, Mr. Charles Joseph, Class: 3, Fare: 8.05
Name: Goldschmidt, Mr. George B, Class: 1, Fare: 34.6542
Name: Greenfield, Mr. William Bertram, Class: 1, Fare: 63.3583
Name: Doling, Mrs. John T (Ada Julia Bone), Class: 2, Fare: 23.0
Name: Kantor, Mr. Sinai, Class: 2, Fare: 26.0
Name: Petranec, Miss. Matilda, Class: 3, Fare: 7.8958
Name: Petroff, Mr. Pastcho ("Pentcho"), Class: 3, Fare: 7.8958
Name: White, Mr. Richard Frasar, Class: 1, Fare: 77.2875
Name: Johansson, Mr. Gustaf Joel, Class: 3, Fare: 8.6542
Name: Gustafsson, Mr. Anders Vilhelm, Class: 3, Fare: 7.925
Name: Mionoff, Mr. Stoytcho, Class: 3, Fare: 7.8958
Name: Salkjelsvik, Miss. Anna Kristine, Class: 3, Fare: 7.65
Name: Moss, Mr. Albert Johan, Class: 3, Fare: 7.775
Name: Rekic, Mr. Tido, Class: 3, Fare: 7.8958
Name: Moran, Miss. Bertha, Class: 3, Fare: 24.15
Name: Porter, Mr. Walter Chamberlain, Class: 1, Fare: 52.0
Name: Zabour, Miss. Hileni, Class: 3, Fare: 14.4542
Name: Barton, Mr. David John, Class: 3, Fare: 8.05
Name: Jussila, Miss. Katriina, Class: 3, Fare: 9.825
Name: Attalah, Miss. Malake, Class: 3, Fare: 14.4583
Name: Pekoniemi, Mr. Edvard, Class: 3, Fare: 7.925
Name: Connors, Mr. Patrick, Class: 3, Fare: 7.75
Name: Turpin, Mr. William John Robert, Class: 2, Fare: 21.0
Name: Baxter, Mr. Quigg Edmond, Class: 1, Fare: 247.5208
Name: Andersson, Miss. Ellis Anna Maria, Class: 3, Fare: 31.275
Name: Hickman, Mr. Stanley George, Class: 2, Fare: 73.5
Name: Moore, Mr. Leonard Charles, Class: 3, Fare: 8.05
Name: Nasser, Mr. Nicholas, Class: 2, Fare: 30.0708
Name: Webber, Miss. Susan, Class: 2, Fare: 13.0
Name: White, Mr. Percival Wayland, Class: 1, Fare: 77.2875
Name: Nicola-Yarred, Master. Elias, Class: 3, Fare: 11.2417
Name: McMahon, Mr. Martin, Class: 3, Fare: 7.75
Name: Madsen, Mr. Fridtjof Arne, Class: 3, Fare: 7.1417
Name: Peter, Miss. Anna, Class: 3, Fare: 22.3583
Name: Ekstrom, Mr. Johan, Class: 3, Fare: 6.975
Name: Drazenoic, Mr. Jozef, Class: 3, Fare: 7.8958
Name: Coelho, Mr. Domingos Fernandeo, Class: 3, Fare: 7.05
Name: Robins, Mrs. Alexander A (Grace Charity Laury), Class: 3, Fare: 14.5
Name: Weisz, Mrs. Leopold (Mathilde Francoise Pede), Class: 2, Fare: 26.0
Name: Sobey, Mr. Samuel James Hayden, Class: 2, Fare: 13.0
Name: Richard, Mr. Emile, Class: 2, Fare: 15.0458
Name: Newsom, Miss. Helen Monypeny, Class: 1, Fare: 26.2833
Name: Futrelle, Mr. Jacques Heath, Class: 1, Fare: 53.1
Name: Osen, Mr. Olaf Elon, Class: 3, Fare: 9.2167
Name: Giglio, Mr. Victor, Class: 1, Fare: 79.2
Name: Boulos, Mrs. Joseph (Sultana), Class: 3, Fare: 15.2458
Name: Nysten, Miss. Anna Sofia, Class: 3, Fare: 7.75
Name: Hakkarainen, Mrs. Pekka Pietari (Elin Matilda Dolck), Class: 3, Fare: 15.85
Name: Burke, Mr. Jeremiah, Class: 3, Fare: 6.75
Name: Andrew, Mr. Edgardo Samuel, Class: 2, Fare: 11.5
Name: Nicholls, Mr. Joseph Charles, Class: 2, Fare: 36.75
Name: Andersson, Mr. August Edvard ("Wennerstrom"), Class: 3, Fare: 7.7958
Name: Ford, Miss. Robina Maggie "Ruby", Class: 3, Fare: 34.375
Name: Navratil, Mr. Michel ("Louis M Hoffman"), Class: 2, Fare: 26.0
Name: Byles, Rev. Thomas Roussel Davids, Class: 2, Fare: 13.0
Name: Bateman, Rev. Robert James, Class: 2, Fare: 12.525
Name: Pears, Mrs. Thomas (Edith Wearne), Class: 1, Fare: 66.6
Name: Meo, Mr. Alfonzo, Class: 3, Fare: 8.05
Name: van Billiard, Mr. Austin Blyler, Class: 3, Fare: 14.5
Name: Olsen, Mr. Ole Martin, Class: 3, Fare: 7.3125
Name: Williams, Mr. Charles Duane, Class: 1, Fare: 61.3792
Name: Gilnagh, Miss. Katherine "Katie", Class: 3, Fare: 7.7333
Name: Corn, Mr. Harry, Class: 3, Fare: 8.05
Name: Smiljanic, Mr. Mile, Class: 3, Fare: 8.6625
Name: Sage, Master. Thomas Henry, Class: 3, Fare: 69.55
Name: Cribb, Mr. John Hatfield, Class: 3, Fare: 16.1
Name: Watt, Mrs. James (Elizabeth "Bessie" Inglis Milne), Class: 2, Fare: 15.75
Name: Bengtsson, Mr. John Viktor, Class: 3, Fare: 7.775
Name: Calic, Mr. Jovo, Class: 3, Fare: 8.6625
Name: Panula, Master. Eino Viljami, Class: 3, Fare: 39.6875
Name: Goldsmith, Master. Frank John William "Frankie", Class: 3, Fare: 20.525
Name: Chibnall, Mrs. (Edith Martha Bowerman), Class: 1, Fare: 55.0
Name: Skoog, Mrs. William (Anna Bernhardina Karlsson), Class: 3, Fare: 27.9
Name: Baumann, Mr. John D, Class: 1, Fare: 25.925
Name: Ling, Mr. Lee, Class: 3, Fare: 56.4958
Name: Van der hoef, Mr. Wyckoff, Class: 1, Fare: 33.5
Name: Rice, Master. Arthur, Class: 3, Fare: 29.125
Name: Johnson, Miss. Eleanor Ileen, Class: 3, Fare: 11.1333
Name: Sivola, Mr. Antti Wilhelm, Class: 3, Fare: 7.925
Name: Smith, Mr. James Clinch, Class: 1, Fare: 30.6958
Name: Klasen, Mr. Klas Albin, Class: 3, Fare: 7.8542
Name: Lefebre, Master. Henry Forbes, Class: 3, Fare: 25.4667
Name: Isham, Miss. Ann Elizabeth, Class: 1, Fare: 28.7125
Name: Hale, Mr. Reginald, Class: 2, Fare: 13.0
Name: Leonard, Mr. Lionel, Class: 3, Fare: 0.0
Name: Sage, Miss. Constance Gladys, Class: 3, Fare: 69.55
Name: Pernot, Mr. Rene, Class: 2, Fare: 15.05
Name: Asplund, Master. Clarence Gustaf Hugo, Class: 3, Fare: 31.3875
Name: Becker, Master. Richard F, Class: 2, Fare: 39.0
Name: Kink-Heilmann, Miss. Luise Gretchen, Class: 3, Fare: 22.025
Name: Rood, Mr. Hugh Roscoe, Class: 1, Fare: 50.0
Name: O'Brien, Mrs. Thomas (Johanna "Hannah" Godfrey), Class: 3, Fare: 15.5
Name: Romaine, Mr. Charles Hallace ("Mr C Rolmane"), Class: 1, Fare: 26.55
Name: Bourke, Mr. John, Class: 3, Fare: 15.5
Name: Turcin, Mr. Stjepan, Class: 3, Fare: 7.8958
Name: Pinsky, Mrs. (Rosa), Class: 2, Fare: 13.0
Name: Carbines, Mr. William, Class: 2, Fare: 13.0
Name: Andersen-Jensen, Miss. Carla Christine Nielsine, Class: 3, Fare: 7.8542
Name: Navratil, Master. Michel M, Class: 2, Fare: 26.0
Name: Brown, Mrs. James Joseph (Margaret Tobin), Class: 1, Fare: 27.7208
Name: Lurette, Miss. Elise, Class: 1, Fare: 146.5208
Name: Mernagh, Mr. Robert, Class: 3, Fare: 7.75
Name: Olsen, Mr. Karl Siegwart Andreas, Class: 3, Fare: 8.4042
Name: Madigan, Miss. Margaret "Maggie", Class: 3, Fare: 7.75
Name: Yrois, Miss. Henriette ("Mrs Harbeck"), Class: 2, Fare: 13.0
Name: Vande Walle, Mr. Nestor Cyriel, Class: 3, Fare: 9.5
Name: Sage, Mr. Frederick, Class: 3, Fare: 69.55
Name: Johanson, Mr. Jakob Alfred, Class: 3, Fare: 6.4958
Name: Youseff, Mr. Gerious, Class: 3, Fare: 7.225
Name: Cohen, Mr. Gurshon "Gus", Class: 3, Fare: 8.05
Name: Strom, Miss. Telma Matilda, Class: 3, Fare: 10.4625
Name: Backstrom, Mr. Karl Alfred, Class: 3, Fare: 15.85
Name: Albimona, Mr. Nassef Cassem, Class: 3, Fare: 18.7875
Name: Carr, Miss. Helen "Ellen", Class: 3, Fare: 7.75
Name: Blank, Mr. Henry, Class: 1, Fare: 31.0
Name: Ali, Mr. Ahmed, Class: 3, Fare: 7.05
Name: Cameron, Miss. Clear Annie, Class: 2, Fare: 21.0
Name: Perkin, Mr. John Henry, Class: 3, Fare: 7.25
Name: Givard, Mr. Hans Kristensen, Class: 2, Fare: 13.0
Name: Kiernan, Mr. Philip, Class: 3, Fare: 7.75
Name: Newell, Miss. Madeleine, Class: 1, Fare: 113.275
Name: Honkanen, Miss. Eliina, Class: 3, Fare: 7.925
Name: Jacobsohn, Mr. Sidney Samuel, Class: 2, Fare: 27.0
Name: Bazzani, Miss. Albina, Class: 1, Fare: 76.2917
Name: Harris, Mr. Walter, Class: 2, Fare: 10.5
Name: Sunderland, Mr. Victor Francis, Class: 3, Fare: 8.05
Name: Bracken, Mr. James H, Class: 2, Fare: 13.0
Name: Green, Mr. George Henry, Class: 3, Fare: 8.05
Name: Nenkoff, Mr. Christo, Class: 3, Fare: 7.8958
Name: Hoyt, Mr. Frederick Maxfield, Class: 1, Fare: 90.0
Name: Berglund, Mr. Karl Ivar Sven, Class: 3, Fare: 9.35
Name: Mellors, Mr. William John, Class: 2, Fare: 10.5
Name: Lovell, Mr. John Hall ("Henry"), Class: 3, Fare: 7.25
Name: Fahlstrom, Mr. Arne Jonas, Class: 2, Fare: 13.0
Name: Lefebre, Miss. Mathilde, Class: 3, Fare: 25.4667
Name: Harris, Mrs. Henry Birkhardt (Irene Wallach), Class: 1, Fare: 83.475
Name: Larsson, Mr. Bengt Edvin, Class: 3, Fare: 7.775
Name: Sjostedt, Mr. Ernst Adolf, Class: 2, Fare: 13.5
Name: Asplund, Miss. Lillian Gertrud, Class: 3, Fare: 31.3875
Name: Leyson, Mr. Robert William Norman, Class: 2, Fare: 10.5
Name: Harknett, Miss. Alice Phoebe, Class: 3, Fare: 7.55
Name: Hold, Mr. Stephen, Class: 2, Fare: 26.0
Name: Collyer, Miss. Marjorie "Lottie", Class: 2, Fare: 26.25
Name: Pengelly, Mr. Frederick William, Class: 2, Fare: 10.5
Name: Hunt, Mr. George Henry, Class: 2, Fare: 12.275
Name: Zabour, Miss. Thamine, Class: 3, Fare: 14.4542
Name: Murphy, Miss. Katherine "Kate", Class: 3, Fare: 15.5
Name: Coleridge, Mr. Reginald Charles, Class: 2, Fare: 10.5
Name: Maenpaa, Mr. Matti Alexanteri, Class: 3, Fare: 7.125
Name: Attalah, Mr. Sleiman, Class: 3, Fare: 7.225
Name: Minahan, Dr. William Edward, Class: 1, Fare: 90.0
Name: Lindahl, Miss. Agda Thorilda Viktoria, Class: 3, Fare: 7.775
Name: Hamalainen, Mrs. William (Anna), Class: 2, Fare: 14.5
Name: Beckwith, Mr. Richard Leonard, Class: 1, Fare: 52.5542
Name: Carter, Rev. Ernest Courtenay, Class: 2, Fare: 26.0
Name: Reed, Mr. James George, Class: 3, Fare: 7.25
Name: Strom, Mrs. Wilhelm (Elna Matilda Persson), Class: 3, Fare: 10.4625
Name: Stead, Mr. William Thomas, Class: 1, Fare: 26.55
Name: Lobb, Mr. William Arthur, Class: 3, Fare: 16.1
Name: Rosblom, Mrs. Viktor (Helena Wilhelmina), Class: 3, Fare: 20.2125
Name: Touma, Mrs. Darwis (Hanne Youssef Razi), Class: 3, Fare: 15.2458
Name: Thorne, Mrs. Gertrude Maybelle, Class: 1, Fare: 79.2
Name: Cherry, Miss. Gladys, Class: 1, Fare: 86.5
Name: Ward, Miss. Anna, Class: 1, Fare: 512.3292
Name: Parrish, Mrs. (Lutie Davis), Class: 2, Fare: 26.0
Name: Smith, Mr. Thomas, Class: 3, Fare: 7.75
Name: Asplund, Master. Edvin Rojj Felix, Class: 3, Fare: 31.3875
Name: Taussig, Mr. Emil, Class: 1, Fare: 79.65
Name: Harrison, Mr. William, Class: 1, Fare: 0.0
Name: Henry, Miss. Delia, Class: 3, Fare: 7.75
Name: Reeves, Mr. David, Class: 2, Fare: 10.5
Name: Panula, Mr. Ernesti Arvid, Class: 3, Fare: 39.6875
Name: Persson, Mr. Ernst Ulrik, Class: 3, Fare: 7.775
Name: Graham, Mrs. William Thompson (Edith Junkins), Class: 1, Fare: 153.4625
Name: Bissette, Miss. Amelia, Class: 1, Fare: 135.6333
Name: Cairns, Mr. Alexander, Class: 1, Fare: 31.0
Name: Tornquist, Mr. William Henry, Class: 3, Fare: 0.0
Name: Mellinger, Mrs. (Elizabeth Anne Maidment), Class: 2, Fare: 19.5
Name: Natsch, Mr. Charles H, Class: 1, Fare: 29.7
Name: Healy, Miss. Hanora "Nora", Class: 3, Fare: 7.75
Name: Andrews, Miss. Kornelia Theodosia, Class: 1, Fare: 77.9583
Name: Lindblom, Miss. Augusta Charlotta, Class: 3, Fare: 7.75
Name: Parkes, Mr. Francis "Frank", Class: 2, Fare: 0.0
Name: Rice, Master. Eric, Class: 3, Fare: 29.125
Name: Abbott, Mrs. Stanton (Rosa Hunt), Class: 3, Fare: 20.25
Name: Duane, Mr. Frank, Class: 3, Fare: 7.75
Name: Olsson, Mr. Nils Johan Goransson, Class: 3, Fare: 7.8542
Name: de Pelsmaeker, Mr. Alfons, Class: 3, Fare: 9.5
Name: Dorking, Mr. Edward Arthur, Class: 3, Fare: 8.05
Name: Smith, Mr. Richard William, Class: 1, Fare: 26.0
Name: Stankovic, Mr. Ivan, Class: 3, Fare: 8.6625
Name: de Mulder, Mr. Theodore, Class: 3, Fare: 9.5
Name: Naidenoff, Mr. Penko, Class: 3, Fare: 7.8958
Name: Hosono, Mr. Masabumi, Class: 2, Fare: 13.0
Name: Connolly, Miss. Kate, Class: 3, Fare: 7.75
Name: Barber, Miss. Ellen "Nellie", Class: 1, Fare: 78.85
Name: Bishop, Mrs. Dickinson H (Helen Walton), Class: 1, Fare: 91.0792
Name: Levy, Mr. Rene Jacques, Class: 2, Fare: 12.875
Name: Haas, Miss. Aloisia, Class: 3, Fare: 8.85
Name: Mineff, Mr. Ivan, Class: 3, Fare: 7.8958
Name: Lewy, Mr. Ervin G, Class: 1, Fare: 27.7208
Name: Hanna, Mr. Mansour, Class: 3, Fare: 7.2292
Name: Allison, Miss. Helen Loraine, Class: 1, Fare: 151.55
Name: Saalfeld, Mr. Adolphe, Class: 1, Fare: 30.5
Name: Baxter, Mrs. James (Helene DeLaudeniere Chaput), Class: 1, Fare: 247.5208
Name: Kelly, Miss. Anna Katherine "Annie Kate", Class: 3, Fare: 7.75
Name: McCoy, Mr. Bernard, Class: 3, Fare: 23.25
Name: Johnson, Mr. William Cahoone Jr, Class: 3, Fare: 0.0
Name: Keane, Miss. Nora A, Class: 2, Fare: 12.35
Name: Williams, Mr. Howard Hugh "Harry", Class: 3, Fare: 8.05
Name: Allison, Master. Hudson Trevor, Class: 1, Fare: 151.55
Name: Fleming, Miss. Margaret, Class: 1, Fare: 110.8833
Name: Penasco y Castellana, Mrs. Victor de Satode (Maria Josefa Perez de Soto y Vallejo), Class: 1, Fare: 108.9
Name: Abelson, Mr. Samuel, Class: 2, Fare: 24.0
Name: Francatelli, Miss. Laura Mabel, Class: 1, Fare: 56.9292
Name: Hays, Miss. Margaret Bechstein, Class: 1, Fare: 83.1583
Name: Ryerson, Miss. Emily Borie, Class: 1, Fare: 262.375
Name: Lahtinen, Mrs. William (Anna Sylfven), Class: 2, Fare: 26.0
Name: Hendekovic, Mr. Ignjac, Class: 3, Fare: 7.8958
Name: Hart, Mr. Benjamin, Class: 2, Fare: 26.25
Name: Nilsson, Miss. Helmina Josefina, Class: 3, Fare: 7.8542
Name: Kantor, Mrs. Sinai (Miriam Sternin), Class: 2, Fare: 26.0
Name: Moraweck, Dr. Ernest, Class: 2, Fare: 14.0
Name: Wick, Miss. Mary Natalie, Class: 1, Fare: 164.8667
Name: Spedden, Mrs. Frederic Oakley (Margaretta Corning Stone), Class: 1, Fare: 134.5
Name: Dennis, Mr. Samuel, Class: 3, Fare: 7.25
Name: Danoff, Mr. Yoto, Class: 3, Fare: 7.8958
Name: Slayter, Miss. Hilda Mary, Class: 2, Fare: 12.35
Name: Caldwell, Mrs. Albert Francis (Sylvia Mae Harbaugh), Class: 2, Fare: 29.0
Name: Sage, Mr. George John Jr, Class: 3, Fare: 69.55
Name: Young, Miss. Marie Grice, Class: 1, Fare: 135.6333
Name: Nysveen, Mr. Johan Hansen, Class: 3, Fare: 6.2375
Name: Ball, Mrs. (Ada E Hall), Class: 2, Fare: 13.0
Name: Goldsmith, Mrs. Frank John (Emily Alice Brown), Class: 3, Fare: 20.525
Name: Hippach, Miss. Jean Gertrude, Class: 1, Fare: 57.9792
Name: McCoy, Miss. Agnes, Class: 3, Fare: 23.25
Name: Partner, Mr. Austen, Class: 1, Fare: 28.5
Name: Graham, Mr. George Edward, Class: 1, Fare: 153.4625
Name: Vander Planke, Mr. Leo Edmondus, Class: 3, Fare: 18.0
Name: Frauenthal, Mrs. Henry William (Clara Heinsheimer), Class: 1, Fare: 133.65
Name: Denkoff, Mr. Mitto, Class: 3, Fare: 7.8958
Name: Pears, Mr. Thomas Clinton, Class: 1, Fare: 66.6
Name: Burns, Miss. Elizabeth Margaret, Class: 1, Fare: 134.5
Name: Dahl, Mr. Karl Edwart, Class: 3, Fare: 8.05
Name: Blackwell, Mr. Stephen Weart, Class: 1, Fare: 35.5
Name: Navratil, Master. Edmond Roger, Class: 2, Fare: 26.0
Name: Fortune, Miss. Alice Elizabeth, Class: 1, Fare: 263.0
Name: Collander, Mr. Erik Gustaf, Class: 2, Fare: 13.0
Name: Sedgwick, Mr. Charles Frederick Waddington, Class: 2, Fare: 13.0
Name: Fox, Mr. Stanley Hubert, Class: 2, Fare: 13.0
Name: Brown, Miss. Amelia "Mildred", Class: 2, Fare: 13.0
Name: Smith, Miss. Marion Elsie, Class: 2, Fare: 13.0
Name: Davison, Mrs. Thomas Henry (Mary E Finck), Class: 3, Fare: 16.1
Name: Coutts, Master. William Loch "William", Class: 3, Fare: 15.9
Name: Dimic, Mr. Jovan, Class: 3, Fare: 8.6625
Name: Odahl, Mr. Nils Martin, Class: 3, Fare: 9.225
Name: Williams-Lambert, Mr. Fletcher Fellows, Class: 1, Fare: 35.0
Name: Elias, Mr. Tannous, Class: 3, Fare: 7.2292
Name: Arnold-Franchi, Mr. Josef, Class: 3, Fare: 17.8
Name: Yousif, Mr. Wazli, Class: 3, Fare: 7.225
Name: Vanden Steen, Mr. Leo Peter, Class: 3, Fare: 9.5
Name: Bowerman, Miss. Elsie Edith, Class: 1, Fare: 55.0
Name: Funk, Miss. Annie Clemmer, Class: 2, Fare: 13.0
Name: McGovern, Miss. Mary, Class: 3, Fare: 7.8792
Name: Mockler, Miss. Helen Mary "Ellie", Class: 3, Fare: 7.8792
Name: Skoog, Mr. Wilhelm, Class: 3, Fare: 27.9
Name: del Carlo, Mr. Sebastiano, Class: 2, Fare: 27.7208
Name: Barbara, Mrs. (Catherine David), Class: 3, Fare: 14.4542
Name: Asim, Mr. Adola, Class: 3, Fare: 7.05
Name: O'Brien, Mr. Thomas, Class: 3, Fare: 15.5
Name: Adahl, Mr. Mauritz Nils Martin, Class: 3, Fare: 7.25
Name: Warren, Mrs. Frank Manley (Anna Sophia Atkinson), Class: 1, Fare: 75.25
Name: Moussa, Mrs. (Mantoura Boulos), Class: 3, Fare: 7.2292
Name: Jermyn, Miss. Annie, Class: 3, Fare: 7.75
Name: Aubart, Mme. Leontine Pauline, Class: 1, Fare: 69.3
Name: Harder, Mr. George Achilles, Class: 1, Fare: 55.4417
Name: Wiklund, Mr. Jakob Alfred, Class: 3, Fare: 6.4958
Name: Beavan, Mr. William Thomas, Class: 3, Fare: 8.05
Name: Ringhini, Mr. Sante, Class: 1, Fare: 135.6333
Name: Palsson, Miss. Stina Viola, Class: 3, Fare: 21.075
Name: Meyer, Mrs. Edgar Joseph (Leila Saks), Class: 1, Fare: 82.1708
Name: Landergren, Miss. Aurora Adelia, Class: 3, Fare: 7.25
Name: Widener, Mr. Harry Elkins, Class: 1, Fare: 211.5
Name: Betros, Mr. Tannous, Class: 3, Fare: 4.0125
Name: Gustafsson, Mr. Karl Gideon, Class: 3, Fare: 7.775
Name: Bidois, Miss. Rosalie, Class: 1, Fare: 227.525
Name: Nakid, Miss. Maria ("Mary"), Class: 3, Fare: 15.7417
Name: Tikkanen, Mr. Juho, Class: 3, Fare: 7.925
Name: Holverson, Mrs. Alexander Oskar (Mary Aline Towner), Class: 1, Fare: 52.0
Name: Plotcharsky, Mr. Vasil, Class: 3, Fare: 7.8958
Name: Davies, Mr. Charles Henry, Class: 2, Fare: 73.5
Name: Goodwin, Master. Sidney Leonard, Class: 3, Fare: 46.9
Name: Buss, Miss. Kate, Class: 2, Fare: 13.0
Name: Sadlier, Mr. Matthew, Class: 3, Fare: 7.7292
Name: Lehmann, Miss. Bertha, Class: 2, Fare: 12.0
Name: Carter, Mr. William Ernest, Class: 1, Fare: 120.0
Name: Jansson, Mr. Carl Olof, Class: 3, Fare: 7.7958
Name: Gustafsson, Mr. Johan Birger, Class: 3, Fare: 7.925
Name: Newell, Miss. Marjorie, Class: 1, Fare: 113.275
Name: Sandstrom, Mrs. Hjalmar (Agnes Charlotta Bengtsson), Class: 3, Fare: 16.7
Name: Johansson, Mr. Erik, Class: 3, Fare: 7.7958
Name: Olsson, Miss. Elina, Class: 3, Fare: 7.8542
Name: McKane, Mr. Peter David, Class: 2, Fare: 26.0
Name: Pain, Dr. Alfred, Class: 2, Fare: 10.5
Name: Trout, Mrs. William H (Jessie L), Class: 2, Fare: 12.65
Name: Niskanen, Mr. Juha, Class: 3, Fare: 7.925
Name: Adams, Mr. John, Class: 3, Fare: 8.05
Name: Jussila, Miss. Mari Aina, Class: 3, Fare: 9.825
Name: Hakkarainen, Mr. Pekka Pietari, Class: 3, Fare: 15.85
Name: Oreskovic, Miss. Marija, Class: 3, Fare: 8.6625
Name: Gale, Mr. Shadrach, Class: 2, Fare: 21.0
Name: Widegren, Mr. Carl/Charles Peter, Class: 3, Fare: 7.75
Name: Richards, Master. William Rowe, Class: 2, Fare: 18.75
Name: Birkeland, Mr. Hans Martin Monsen, Class: 3, Fare: 7.775
Name: Lefebre, Miss. Ida, Class: 3, Fare: 25.4667
Name: Sdycoff, Mr. Todor, Class: 3, Fare: 7.8958
Name: Hart, Mr. Henry, Class: 3, Fare: 6.8583
Name: Minahan, Miss. Daisy E, Class: 1, Fare: 90.0
Name: Cunningham, Mr. Alfred Fleming, Class: 2, Fare: 0.0
Name: Sundman, Mr. Johan Julian, Class: 3, Fare: 7.925
Name: Meek, Mrs. Thomas (Annie Louise Rowley), Class: 3, Fare: 8.05
Name: Drew, Mrs. James Vivian (Lulu Thorne Christian), Class: 2, Fare: 32.5
Name: Silven, Miss. Lyyli Karoliina, Class: 2, Fare: 13.0
Name: Matthews, Mr. William John, Class: 2, Fare: 13.0
Name: Van Impe, Miss. Catharina, Class: 3, Fare: 24.15
Name: Gheorgheff, Mr. Stanio, Class: 3, Fare: 7.8958
Name: Charters, Mr. David, Class: 3, Fare: 7.7333
Name: Zimmerman, Mr. Leo, Class: 3, Fare: 7.875
Name: Danbom, Mrs. Ernst Gilbert (Anna Sigrid Maria Brogren), Class: 3, Fare: 14.4
Name: Rosblom, Mr. Viktor Richard, Class: 3, Fare: 20.2125
Name: Wiseman, Mr. Phillippe, Class: 3, Fare: 7.25
Name: Clarke, Mrs. Charles V (Ada Maria Winfield), Class: 2, Fare: 26.0
Name: Phillips, Miss. Kate Florence ("Mrs Kate Louise Phillips Marshall"), Class: 2, Fare: 26.0
Name: Flynn, Mr. James, Class: 3, Fare: 7.75
Name: Pickard, Mr. Berk (Berk Trembisky), Class: 3, Fare: 8.05
Name: Bjornstrom-Steffansson, Mr. Mauritz Hakan, Class: 1, Fare: 26.55
Name: Thorneycroft, Mrs. Percival (Florence Kate White), Class: 3, Fare: 16.1
Name: Louch, Mrs. Charles Alexander (Alice Adelaide Slow), Class: 2, Fare: 26.0
Name: Kallio, Mr. Nikolai Erland, Class: 3, Fare: 7.125
Name: Silvey, Mr. William Baird, Class: 1, Fare: 55.9
Name: Carter, Miss. Lucile Polk, Class: 1, Fare: 120.0
Name: Ford, Miss. Doolina Margaret "Daisy", Class: 3, Fare: 34.375
Name: Richards, Mrs. Sidney (Emily Hocking), Class: 2, Fare: 18.75
Name: Fortune, Mr. Mark, Class: 1, Fare: 263.0
Name: Kvillner, Mr. Johan Henrik Johannesson, Class: 2, Fare: 10.5
Name: Hart, Mrs. Benjamin (Esther Ada Bloomfield), Class: 2, Fare: 26.25
Name: Hampe, Mr. Leon, Class: 3, Fare: 9.5
Name: Petterson, Mr. Johan Emil, Class: 3, Fare: 7.775
Name: Reynaldo, Ms. Encarnacion, Class: 2, Fare: 13.0
Name: Johannesen-Bratthammer, Mr. Bernt, Class: 3, Fare: 8.1125
Name: Dodge, Master. Washington, Class: 1, Fare: 81.8583
Name: Mellinger, Miss. Madeleine Violet, Class: 2, Fare: 19.5
Name: Seward, Mr. Frederic Kimber, Class: 1, Fare: 26.55
Name: Baclini, Miss. Marie Catherine, Class: 3, Fare: 19.2583
Name: Peuchen, Major. Arthur Godfrey, Class: 1, Fare: 30.5
Name: West, Mr. Edwy Arthur, Class: 2, Fare: 27.75
Name: Hagland, Mr. Ingvald Olai Olsen, Class: 3, Fare: 19.9667
Name: Foreman, Mr. Benjamin Laventall, Class: 1, Fare: 27.75
Name: Goldenberg, Mr. Samuel L, Class: 1, Fare: 89.1042
Name: Peduzzi, Mr. Joseph, Class: 3, Fare: 8.05
Name: Jalsevac, Mr. Ivan, Class: 3, Fare: 7.8958
Name: Millet, Mr. Francis Davis, Class: 1, Fare: 26.55
Name: Kenyon, Mrs. Frederick R (Marion), Class: 1, Fare: 51.8625
Name: Toomey, Miss. Ellen, Class: 2, Fare: 10.5
Name: O'Connor, Mr. Maurice, Class: 3, Fare: 7.75
Name: Anderson, Mr. Harry, Class: 1, Fare: 26.55
Name: Morley, Mr. William, Class: 3, Fare: 8.05
Name: Gee, Mr. Arthur H, Class: 1, Fare: 38.5
Name: Milling, Mr. Jacob Christian, Class: 2, Fare: 13.0
Name: Maisner, Mr. Simon, Class: 3, Fare: 8.05
Name: Goncalves, Mr. Manuel Estanslas, Class: 3, Fare: 7.05
Name: Campbell, Mr. William, Class: 2, Fare: 0.0
Name: Smart, Mr. John Montgomery, Class: 1, Fare: 26.55
Name: Scanlan, Mr. James, Class: 3, Fare: 7.725
Name: Baclini, Miss. Helene Barbara, Class: 3, Fare: 19.2583
Name: Keefe, Mr. Arthur, Class: 3, Fare: 7.25
Name: Cacic, Mr. Luka, Class: 3, Fare: 8.6625
Name: West, Mrs. Edwy Arthur (Ada Mary Worth), Class: 2, Fare: 27.75
Name: Jerwan, Mrs. Amin S (Marie Marthe Thuillard), Class: 2, Fare: 13.7917
Name: Strandberg, Miss. Ida Sofia, Class: 3, Fare: 9.8375
Name: Clifford, Mr. George Quincy, Class: 1, Fare: 52.0
Name: Renouf, Mr. Peter Henry, Class: 2, Fare: 21.0
Name: Braund, Mr. Lewis Richard, Class: 3, Fare: 7.0458
Name: Karlsson, Mr. Nils August, Class: 3, Fare: 7.5208
Name: Hirvonen, Miss. Hildur E, Class: 3, Fare: 12.2875
Name: Goodwin, Master. Harold Victor, Class: 3, Fare: 46.9
Name: Frost, Mr. Anthony Wood "Archie", Class: 2, Fare: 0.0
Name: Rouse, Mr. Richard Henry, Class: 3, Fare: 8.05
Name: Turkula, Mrs. (Hedwig), Class: 3, Fare: 9.5875
Name: Bishop, Mr. Dickinson H, Class: 1, Fare: 91.0792
Name: Lefebre, Miss. Jeannie, Class: 3, Fare: 25.4667
Name: Hoyt, Mrs. Frederick Maxfield (Jane Anne Forby), Class: 1, Fare: 90.0
Name: Kent, Mr. Edward Austin, Class: 1, Fare: 29.7
Name: Somerton, Mr. Francis William, Class: 3, Fare: 8.05
Name: Coutts, Master. Eden Leslie "Neville", Class: 3, Fare: 15.9
Name: Hagland, Mr. Konrad Mathias Reiersen, Class: 3, Fare: 19.9667
Name: Windelov, Mr. Einar, Class: 3, Fare: 7.25
Name: Molson, Mr. Harry Markland, Class: 1, Fare: 30.5
Name: Artagaveytia, Mr. Ramon, Class: 1, Fare: 49.5042
Name: Stanley, Mr. Edward Roland, Class: 3, Fare: 8.05
Name: Yousseff, Mr. Gerious, Class: 3, Fare: 14.4583
Name: Eustis, Miss. Elizabeth Mussey, Class: 1, Fare: 78.2667
Name: Shellard, Mr. Frederick William, Class: 3, Fare: 15.1
Name: Allison, Mrs. Hudson J C (Bessie Waldo Daniels), Class: 1, Fare: 151.55
Name: Svensson, Mr. Olof, Class: 3, Fare: 7.7958
Name: Calic, Mr. Petar, Class: 3, Fare: 8.6625
Name: Canavan, Miss. Mary, Class: 3, Fare: 7.75
Name: O'Sullivan, Miss. Bridget Mary, Class: 3, Fare: 7.6292
Name: Laitinen, Miss. Kristina Sofia, Class: 3, Fare: 9.5875
Name: Maioni, Miss. Roberta, Class: 1, Fare: 86.5
Name: Penasco y Castellana, Mr. Victor de Satode, Class: 1, Fare: 108.9
Name: Quick, Mrs. Frederick Charles (Jane Richards), Class: 2, Fare: 26.0
Name: Bradley, Mr. George ("George Arthur Brayton"), Class: 1, Fare: 26.55
Name: Olsen, Mr. Henry Margido, Class: 3, Fare: 22.525
Name: Lang, Mr. Fang, Class: 3, Fare: 56.4958
Name: Daly, Mr. Eugene Patrick, Class: 3, Fare: 7.75
Name: Webber, Mr. James, Class: 3, Fare: 8.05
Name: McGough, Mr. James Robert, Class: 1, Fare: 26.2875
Name: Rothschild, Mrs. Martin (Elizabeth L. Barrett), Class: 1, Fare: 59.4
Name: Coleff, Mr. Satio, Class: 3, Fare: 7.4958
Name: Walker, Mr. William Anderson, Class: 1, Fare: 34.0208
Name: Lemore, Mrs. (Amelia Milley), Class: 2, Fare: 10.5
Name: Ryan, Mr. Patrick, Class: 3, Fare: 24.15
Name: Angle, Mrs. William A (Florence "Mary" Agnes Hughes), Class: 2, Fare: 26.0
Name: Pavlovic, Mr. Stefo, Class: 3, Fare: 7.8958
Name: Perreault, Miss. Anne, Class: 1, Fare: 93.5
Name: Vovk, Mr. Janko, Class: 3, Fare: 7.8958
Name: Lahoud, Mr. Sarkis, Class: 3, Fare: 7.225
Name: Hippach, Mrs. Louis Albert (Ida Sophia Fischer), Class: 1, Fare: 57.9792
Name: Kassem, Mr. Fared, Class: 3, Fare: 7.2292
Name: Farrell, Mr. James, Class: 3, Fare: 7.75
Name: Ridsdale, Miss. Lucy, Class: 2, Fare: 10.5
Name: Farthing, Mr. John, Class: 1, Fare: 221.7792
Name: Salonen, Mr. Johan Werner, Class: 3, Fare: 7.925
Name: Hocking, Mr. Richard George, Class: 2, Fare: 11.5
Name: Quick, Miss. Phyllis May, Class: 2, Fare: 26.0
Name: Toufik, Mr. Nakli, Class: 3, Fare: 7.2292
Name: Elias, Mr. Joseph Jr, Class: 3, Fare: 7.2292
Name: Peter, Mrs. Catherine (Catherine Rizk), Class: 3, Fare: 22.3583
Name: Cacic, Miss. Marija, Class: 3, Fare: 8.6625
Name: Hart, Miss. Eva Miriam, Class: 2, Fare: 26.25
Name: Butt, Major. Archibald Willingham, Class: 1, Fare: 26.55
Name: LeRoy, Miss. Bertha, Class: 1, Fare: 106.425
Name: Risien, Mr. Samuel Beard, Class: 3, Fare: 14.5
Name: Frolicher, Miss. Hedwig Margaritha, Class: 1, Fare: 49.5
Name: Crosby, Miss. Harriet R, Class: 1, Fare: 71.0
Name: Andersson, Miss. Ingeborg Constanzia, Class: 3, Fare: 31.275
Name: Andersson, Miss. Sigrid Elisabeth, Class: 3, Fare: 31.275
Name: Beane, Mr. Edward, Class: 2, Fare: 26.0
Name: Douglas, Mr. Walter Donald, Class: 1, Fare: 106.425
Name: Nicholson, Mr. Arthur Ernest, Class: 1, Fare: 26.0
Name: Beane, Mrs. Edward (Ethel Clarke), Class: 2, Fare: 26.0
Name: Padro y Manent, Mr. Julian, Class: 2, Fare: 13.8625
Name: Goldsmith, Mr. Frank John, Class: 3, Fare: 20.525
Name: Davies, Master. John Morgan Jr, Class: 2, Fare: 36.75
Name: Thayer, Mr. John Borland Jr, Class: 1, Fare: 110.8833
Name: Sharp, Mr. Percival James R, Class: 2, Fare: 26.0
Name: O'Brien, Mr. Timothy, Class: 3, Fare: 7.8292
Name: Leeni, Mr. Fahim ("Philip Zenni"), Class: 3, Fare: 7.225
Name: Ohman, Miss. Velin, Class: 3, Fare: 7.775
Name: Wright, Mr. George, Class: 1, Fare: 26.55
Name: Duff Gordon, Lady. (Lucille Christiana Sutherland) ("Mrs Morgan"), Class: 1, Fare: 39.6
Name: Robbins, Mr. Victor, Class: 1, Fare: 227.525
Name: Taussig, Mrs. Emil (Tillie Mandelbaum), Class: 1, Fare: 79.65
Name: de Messemaeker, Mrs. Guillaume Joseph (Emma), Class: 3, Fare: 17.4
Name: Morrow, Mr. Thomas Rowan, Class: 3, Fare: 7.75
Name: Sivic, Mr. Husein, Class: 3, Fare: 7.8958
Name: Norman, Mr. Robert Douglas, Class: 2, Fare: 13.5
Name: Simmons, Mr. John, Class: 3, Fare: 8.05
Name: Meanwell, Miss. (Marion Ogden), Class: 3, Fare: 8.05
Name: Davies, Mr. Alfred J, Class: 3, Fare: 24.15
Name: Stoytcheff, Mr. Ilia, Class: 3, Fare: 7.8958
Name: Palsson, Mrs. Nils (Alma Cornelia Berglund), Class: 3, Fare: 21.075
Name: Doharr, Mr. Tannous, Class: 3, Fare: 7.2292
Name: Jonsson, Mr. Carl, Class: 3, Fare: 7.8542
Name: Harris, Mr. George, Class: 2, Fare: 10.5
Name: Appleton, Mrs. Edward Dale (Charlotte Lamson), Class: 1, Fare: 51.4792
Name: Flynn, Mr. John Irwin ("Irving"), Class: 1, Fare: 26.3875
Name: Kelly, Miss. Mary, Class: 3, Fare: 7.75
Name: Rush, Mr. Alfred George John, Class: 3, Fare: 8.05
Name: Patchett, Mr. George, Class: 3, Fare: 14.5
Name: Garside, Miss. Ethel, Class: 2, Fare: 13.0
Name: Silvey, Mrs. William Baird (Alice Munger), Class: 1, Fare: 55.9
Name: Caram, Mrs. Joseph (Maria Elias), Class: 3, Fare: 14.4583
Name: Jussila, Mr. Eiriik, Class: 3, Fare: 7.925
Name: Christy, Miss. Julie Rachel, Class: 2, Fare: 30.0
Name: Thayer, Mrs. John Borland (Marian Longstreth Morris), Class: 1, Fare: 110.8833
Name: Downton, Mr. William James, Class: 2, Fare: 26.0
Name: Ross, Mr. John Hugo, Class: 1, Fare: 40.125
Name: Paulner, Mr. Uscher, Class: 3, Fare: 8.7125
Name: Taussig, Miss. Ruth, Class: 1, Fare: 79.65
Name: Jarvis, Mr. John Denzil, Class: 2, Fare: 15.0
Name: Frolicher-Stehli, Mr. Maxmillian, Class: 1, Fare: 79.2
Name: Gilinski, Mr. Eliezer, Class: 3, Fare: 8.05
Name: Murdlin, Mr. Joseph, Class: 3, Fare: 8.05
Name: Rintamaki, Mr. Matti, Class: 3, Fare: 7.125
Name: Stephenson, Mrs. Walter Bertram (Martha Eustis), Class: 1, Fare: 78.2667
Name: Elsbury, Mr. William James, Class: 3, Fare: 7.25
Name: Bourke, Miss. Mary, Class: 3, Fare: 7.75
Name: Chapman, Mr. John Henry, Class: 2, Fare: 26.0
Name: Van Impe, Mr. Jean Baptiste, Class: 3, Fare: 24.15
Name: Leitch, Miss. Jessie Wills, Class: 2, Fare: 33.0
Name: Johnson, Mr. Alfred, Class: 3, Fare: 0.0
Name: Boulos, Mr. Hanna, Class: 3, Fare: 7.225
Name: Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan"), Class: 1, Fare: 56.9292
Name: Jacobsohn, Mrs. Sidney Samuel (Amy Frances Christy), Class: 2, Fare: 27.0
Name: Slabenoff, Mr. Petco, Class: 3, Fare: 7.8958
Name: Harrington, Mr. Charles H, Class: 1, Fare: 42.4
Name: Torber, Mr. Ernst William, Class: 3, Fare: 8.05
Name: Homer, Mr. Harry ("Mr E Haven"), Class: 1, Fare: 26.55
Name: Lindell, Mr. Edvard Bengtsson, Class: 3, Fare: 15.55
Name: Karaic, Mr. Milan, Class: 3, Fare: 7.8958
Name: Daniel, Mr. Robert Williams, Class: 1, Fare: 30.5
Name: Laroche, Mrs. Joseph (Juliette Marie Louise Lafargue), Class: 2, Fare: 41.5792
Name: Shutes, Miss. Elizabeth W, Class: 1, Fare: 153.4625
Name: Andersson, Mrs. Anders Johan (Alfrida Konstantia Brogren), Class: 3, Fare: 31.275
Name: Jardin, Mr. Jose Neto, Class: 3, Fare: 7.05
Name: Murphy, Miss. Margaret Jane, Class: 3, Fare: 15.5
Name: Horgan, Mr. John, Class: 3, Fare: 7.75
Name: Brocklebank, Mr. William Alfred, Class: 3, Fare: 8.05
Name: Herman, Miss. Alice, Class: 2, Fare: 65.0
Name: Danbom, Mr. Ernst Gilbert, Class: 3, Fare: 14.4
Name: Lobb, Mrs. William Arthur (Cordelia K Stanlick), Class: 3, Fare: 16.1
Name: Becker, Miss. Marion Louise, Class: 2, Fare: 39.0
Name: Gavey, Mr. Lawrence, Class: 2, Fare: 10.5
Name: Yasbeck, Mr. Antoni, Class: 3, Fare: 14.4542
Name: Kimball, Mr. Edwin Nelson Jr, Class: 1, Fare: 52.5542
Name: Nakid, Mr. Sahid, Class: 3, Fare: 15.7417
Name: Hansen, Mr. Henry Damsgaard, Class: 3, Fare: 7.8542
Name: Bowen, Mr. David John "Dai", Class: 3, Fare: 16.1
Name: Sutton, Mr. Frederick, Class: 1, Fare: 32.3208
Name: Kirkland, Rev. Charles Leonard, Class: 2, Fare: 12.35
Name: Longley, Miss. Gretchen Fiske, Class: 1, Fare: 77.9583
Name: Bostandyeff, Mr. Guentcho, Class: 3, Fare: 7.8958
Name: O'Connell, Mr. Patrick D, Class: 3, Fare: 7.7333
Name: Barkworth, Mr. Algernon Henry Wilson, Class: 1, Fare: 30.0
Name: Lundahl, Mr. Johan Svensson, Class: 3, Fare: 7.0542
Name: Stahelin-Maeglin, Dr. Max, Class: 1, Fare: 30.5
Name: Parr, Mr. William Henry Marsh, Class: 1, Fare: 0.0
Name: Skoog, Miss. Mabel, Class: 3, Fare: 27.9
Name: Davis, Miss. Mary, Class: 2, Fare: 13.0
Name: Leinonen, Mr. Antti Gustaf, Class: 3, Fare: 7.925
Name: Collyer, Mr. Harvey, Class: 2, Fare: 26.25
Name: Panula, Mrs. Juha (Maria Emilia Ojala), Class: 3, Fare: 39.6875
Name: Thorneycroft, Mr. Percival, Class: 3, Fare: 16.1
Name: Jensen, Mr. Hans Peder, Class: 3, Fare: 7.8542
Name: Sagesser, Mlle. Emma, Class: 1, Fare: 69.3
Name: Skoog, Miss. Margit Elizabeth, Class: 3, Fare: 27.9
Name: Foo, Mr. Choong, Class: 3, Fare: 56.4958
Name: Baclini, Miss. Eugenie, Class: 3, Fare: 19.2583
Name: Harper, Mr. Henry Sleeper, Class: 1, Fare: 76.7292
Name: Cor, Mr. Liudevit, Class: 3, Fare: 7.8958
Name: Simonius-Blumer, Col. Oberst Alfons, Class: 1, Fare: 35.5
Name: Willey, Mr. Edward, Class: 3, Fare: 7.55
Name: Stanley, Miss. Amy Zillah Elsie, Class: 3, Fare: 7.55
Name: Mitkoff, Mr. Mito, Class: 3, Fare: 7.8958
Name: Doling, Miss. Elsie, Class: 2, Fare: 23.0
Name: Kalvik, Mr. Johannes Halvorsen, Class: 3, Fare: 8.4333
Name: O'Leary, Miss. Hanora "Norah", Class: 3, Fare: 7.8292
Name: Hegarty, Miss. Hanora "Nora", Class: 3, Fare: 6.75
Name: Hickman, Mr. Leonard Mark, Class: 2, Fare: 73.5
Name: Radeff, Mr. Alexander, Class: 3, Fare: 7.8958
Name: Bourke, Mrs. John (Catherine), Class: 3, Fare: 15.5
Name: Eitemiller, Mr. George Floyd, Class: 2, Fare: 13.0
Name: Newell, Mr. Arthur Webster, Class: 1, Fare: 113.275
Name: Frauenthal, Dr. Henry William, Class: 1, Fare: 133.65
Name: Badt, Mr. Mohamed, Class: 3, Fare: 7.225
Name: Colley, Mr. Edward Pomeroy, Class: 1, Fare: 25.5875
Name: Coleff, Mr. Peju, Class: 3, Fare: 7.4958
Name: Lindqvist, Mr. Eino William, Class: 3, Fare: 7.925
Name: Hickman, Mr. Lewis, Class: 2, Fare: 73.5
Name: Butler, Mr. Reginald Fenton, Class: 2, Fare: 13.0
Name: Rommetvedt, Mr. Knud Paust, Class: 3, Fare: 7.775
Name: Cook, Mr. Jacob, Class: 3, Fare: 8.05
Name: Taylor, Mrs. Elmer Zebley (Juliet Cummins Wright), Class: 1, Fare: 52.0
Name: Brown, Mrs. Thomas William Solomon (Elizabeth Catherine Ford), Class: 2, Fare: 39.0
Name: Davidson, Mr. Thornton, Class: 1, Fare: 52.0
Name: Mitchell, Mr. Henry Michael, Class: 2, Fare: 10.5
Name: Wilhelms, Mr. Charles, Class: 2, Fare: 13.0
Name: Watson, Mr. Ennis Hastings, Class: 2, Fare: 0.0
Name: Edvardsson, Mr. Gustaf Hjalmar, Class: 3, Fare: 7.775
Name: Sawyer, Mr. Frederick Charles, Class: 3, Fare: 8.05
Name: Turja, Miss. Anna Sofia, Class: 3, Fare: 9.8417
Name: Goodwin, Mrs. Frederick (Augusta Tyler), Class: 3, Fare: 46.9
Name: Cardeza, Mr. Thomas Drake Martinez, Class: 1, Fare: 512.3292
Name: Peters, Miss. Katie, Class: 3, Fare: 8.1375
Name: Hassab, Mr. Hammad, Class: 1, Fare: 76.7292
Name: Olsvigen, Mr. Thor Anderson, Class: 3, Fare: 9.225
Name: Goodwin, Mr. Charles Edward, Class: 3, Fare: 46.9
Name: Brown, Mr. Thomas William Solomon, Class: 2, Fare: 39.0
Name: Laroche, Mr. Joseph Philippe Lemercier, Class: 2, Fare: 41.5792
Name: Panula, Mr. Jaako Arnold, Class: 3, Fare: 39.6875
Name: Dakic, Mr. Branko, Class: 3, Fare: 10.1708
Name: Fischer, Mr. Eberhard Thelander, Class: 3, Fare: 7.7958
Name: Madill, Miss. Georgette Alexandra, Class: 1, Fare: 211.3375
Name: Dick, Mr. Albert Adrian, Class: 1, Fare: 57.0
Name: Karun, Miss. Manca, Class: 3, Fare: 13.4167
Name: Lam, Mr. Ali, Class: 3, Fare: 56.4958
Name: Saad, Mr. Khalil, Class: 3, Fare: 7.225
Name: Weir, Col. John, Class: 1, Fare: 26.55
Name: Chapman, Mr. Charles Henry, Class: 2, Fare: 13.5
Name: Kelly, Mr. James, Class: 3, Fare: 8.05
Name: Mullens, Miss. Katherine "Katie", Class: 3, Fare: 7.7333
Name: Thayer, Mr. John Borland, Class: 1, Fare: 110.8833
Name: Humblen, Mr. Adolf Mathias Nicolai Olsen, Class: 3, Fare: 7.65
Name: Astor, Mrs. John Jacob (Madeleine Talmadge Force), Class: 1, Fare: 227.525
Name: Silverthorne, Mr. Spencer Victor, Class: 1, Fare: 26.2875
Name: Barbara, Miss. Saiide, Class: 3, Fare: 14.4542
Name: Gallagher, Mr. Martin, Class: 3, Fare: 7.7417
Name: Hansen, Mr. Henrik Juul, Class: 3, Fare: 7.8542
Name: Morley, Mr. Henry Samuel ("Mr Henry Marshall"), Class: 2, Fare: 26.0
Name: Kelly, Mrs. Florence "Fannie", Class: 2, Fare: 13.5
Name: Calderhead, Mr. Edward Pennington, Class: 1, Fare: 26.2875
Name: Cleaver, Miss. Alice, Class: 1, Fare: 151.55
Name: Moubarek, Master. Halim Gonios ("William George"), Class: 3, Fare: 15.2458
Name: Mayne, Mlle. Berthe Antonine ("Mrs de Villiers"), Class: 1, Fare: 49.5042
Name: Klaber, Mr. Herman, Class: 1, Fare: 26.55
Name: Taylor, Mr. Elmer Zebley, Class: 1, Fare: 52.0
Name: Larsson, Mr. August Viktor, Class: 3, Fare: 9.4833
Name: Greenberg, Mr. Samuel, Class: 2, Fare: 13.0
Name: Soholt, Mr. Peter Andreas Lauritz Andersen, Class: 3, Fare: 7.65
Name: Endres, Miss. Caroline Louise, Class: 1, Fare: 227.525
Name: Troutt, Miss. Edwina Celia "Winnie", Class: 2, Fare: 10.5
Name: McEvoy, Mr. Michael, Class: 3, Fare: 15.5
Name: Johnson, Mr. Malkolm Joackim, Class: 3, Fare: 7.775
Name: Harper, Miss. Annie Jessie "Nina", Class: 2, Fare: 33.0
Name: Jensen, Mr. Svend Lauritz, Class: 3, Fare: 7.0542
Name: Gillespie, Mr. William Henry, Class: 2, Fare: 13.0
Name: Hodges, Mr. Henry Price, Class: 2, Fare: 13.0
Name: Chambers, Mr. Norman Campbell, Class: 1, Fare: 53.1
Name: Oreskovic, Mr. Luka, Class: 3, Fare: 8.6625
Name: Renouf, Mrs. Peter Henry (Lillian Jefferys), Class: 2, Fare: 21.0
Name: Mannion, Miss. Margareth, Class: 3, Fare: 7.7375
Name: Bryhl, Mr. Kurt Arnold Gottfrid, Class: 2, Fare: 26.0
Name: Ilmakangas, Miss. Pieta Sofia, Class: 3, Fare: 7.925
Name: Allen, Miss. Elisabeth Walton, Class: 1, Fare: 211.3375
Name: Hassan, Mr. Houssein G N, Class: 3, Fare: 18.7875
Name: Knight, Mr. Robert J, Class: 2, Fare: 0.0
Name: Berriman, Mr. William John, Class: 2, Fare: 13.0
Name: Troupiansky, Mr. Moses Aaron, Class: 2, Fare: 13.0
Name: Williams, Mr. Leslie, Class: 3, Fare: 16.1
Name: Ford, Mrs. Edward (Margaret Ann Watson), Class: 3, Fare: 34.375
Name: Lesurer, Mr. Gustave J, Class: 1, Fare: 512.3292
Name: Ivanoff, Mr. Kanio, Class: 3, Fare: 7.8958
Name: Nankoff, Mr. Minko, Class: 3, Fare: 7.8958
Name: Hawksford, Mr. Walter James, Class: 1, Fare: 30.0
Name: Cavendish, Mr. Tyrell William, Class: 1, Fare: 78.85
Name: Ryerson, Miss. Susan Parker "Suzette", Class: 1, Fare: 262.375
Name: McNamee, Mr. Neal, Class: 3, Fare: 16.1
Name: Stranden, Mr. Juho, Class: 3, Fare: 7.925
Name: Crosby, Capt. Edward Gifford, Class: 1, Fare: 71.0
Name: Abbott, Mr. Rossmore Edward, Class: 3, Fare: 20.25
Name: Sinkkonen, Miss. Anna, Class: 2, Fare: 13.0
Name: Marvin, Mr. Daniel Warner, Class: 1, Fare: 53.1
Name: Connaghton, Mr. Michael, Class: 3, Fare: 7.75
Name: Wells, Miss. Joan, Class: 2, Fare: 23.0
Name: Moor, Master. Meier, Class: 3, Fare: 12.475
Name: Vande Velde, Mr. Johannes Joseph, Class: 3, Fare: 9.5
Name: Jonkoff, Mr. Lalio, Class: 3, Fare: 7.8958
Name: Herman, Mrs. Samuel (Jane Laver), Class: 2, Fare: 65.0
Name: Hamalainen, Master. Viljo, Class: 2, Fare: 14.5
Name: Carlsson, Mr. August Sigfrid, Class: 3, Fare: 7.7958
Name: Bailey, Mr. Percy Andrew, Class: 2, Fare: 11.5
Name: Theobald, Mr. Thomas Leonard, Class: 3, Fare: 8.05
Name: Rothes, the Countess. of (Lucy Noel Martha Dyer-Edwards), Class: 1, Fare: 86.5
Name: Garfirth, Mr. John, Class: 3, Fare: 14.5
Name: Nirva, Mr. Iisakki Antino Aijo, Class: 3, Fare: 7.125
Name: Barah, Mr. Hanna Assi, Class: 3, Fare: 7.2292
Name: Carter, Mrs. William Ernest (Lucile Polk), Class: 1, Fare: 120.0
Name: Eklund, Mr. Hans Linus, Class: 3, Fare: 7.775
Name: Hogeboom, Mrs. John C (Anna Andrews), Class: 1, Fare: 77.9583
Name: Brewe, Dr. Arthur Jackson, Class: 1, Fare: 39.6
Name: Mangan, Miss. Mary, Class: 3, Fare: 7.75
Name: Moran, Mr. Daniel J, Class: 3, Fare: 24.15
Name: Gronnestad, Mr. Daniel Danielsen, Class: 3, Fare: 8.3625
Name: Lievens, Mr. Rene Aime, Class: 3, Fare: 9.5
Name: Jensen, Mr. Niels Peder, Class: 3, Fare: 7.8542
Name: Mack, Mrs. (Mary), Class: 2, Fare: 10.5
Name: Elias, Mr. Dibo, Class: 3, Fare: 7.225
Name: Hocking, Mrs. Elizabeth (Eliza Needs), Class: 2, Fare: 23.0
Name: Myhrman, Mr. Pehr Fabian Oliver Malkolm, Class: 3, Fare: 7.75
Name: Tobin, Mr. Roger, Class: 3, Fare: 7.75
Name: Emanuel, Miss. Virginia Ethel, Class: 3, Fare: 12.475
Name: Kilgannon, Mr. Thomas J, Class: 3, Fare: 7.7375
Name: Robert, Mrs. Edward Scott (Elisabeth Walton McMillan), Class: 1, Fare: 211.3375
Name: Ayoub, Miss. Banoura, Class: 3, Fare: 7.2292
Name: Dick, Mrs. Albert Adrian (Vera Gillespie), Class: 1, Fare: 57.0
Name: Long, Mr. Milton Clyde, Class: 1, Fare: 30.0
Name: Johnston, Mr. Andrew G, Class: 3, Fare: 23.45
Name: Ali, Mr. William, Class: 3, Fare: 7.05
Name: Harmer, Mr. Abraham (David Lishin), Class: 3, Fare: 7.25
Name: Sjoblom, Miss. Anna Sofia, Class: 3, Fare: 7.4958
Name: Rice, Master. George Hugh, Class: 3, Fare: 29.125
Name: Dean, Master. Bertram Vere, Class: 3, Fare: 20.575
Name: Guggenheim, Mr. Benjamin, Class: 1, Fare: 79.2
Name: Keane, Mr. Andrew "Andy", Class: 3, Fare: 7.75
Name: Gaskell, Mr. Alfred, Class: 2, Fare: 26.0
Name: Sage, Miss. Stella Anna, Class: 3, Fare: 69.55
Name: Hoyt, Mr. William Fisher, Class: 1, Fare: 30.6958
Name: Dantcheff, Mr. Ristiu, Class: 3, Fare: 7.8958
Name: Otter, Mr. Richard, Class: 2, Fare: 13.0
Name: Leader, Dr. Alice (Farnham), Class: 1, Fare: 25.9292
Name: Osman, Mrs. Mara, Class: 3, Fare: 8.6833
Name: Ibrahim Shawah, Mr. Yousseff, Class: 3, Fare: 7.2292
Name: Van Impe, Mrs. Jean Baptiste (Rosalie Paula Govaert), Class: 3, Fare: 24.15
Name: Ponesell, Mr. Martin, Class: 2, Fare: 13.0
Name: Collyer, Mrs. Harvey (Charlotte Annie Tate), Class: 2, Fare: 26.25
Name: Carter, Master. William Thornton II, Class: 1, Fare: 120.0
Name: Thomas, Master. Assad Alexander, Class: 3, Fare: 8.5167
Name: Hedman, Mr. Oskar Arvid, Class: 3, Fare: 6.975
Name: Johansson, Mr. Karl Johan, Class: 3, Fare: 7.775
Name: Andrews, Mr. Thomas Jr, Class: 1, Fare: 0.0
Name: Pettersson, Miss. Ellen Natalia, Class: 3, Fare: 7.775
Name: Meyer, Mr. August, Class: 2, Fare: 13.0
Name: Chambers, Mrs. Norman Campbell (Bertha Griggs), Class: 1, Fare: 53.1
Name: Alexander, Mr. William, Class: 3, Fare: 7.8875
Name: Lester, Mr. James, Class: 3, Fare: 24.15
Name: Slemen, Mr. Richard James, Class: 2, Fare: 10.5
Name: Andersson, Miss. Ebba Iris Alfrida, Class: 3, Fare: 31.275
Name: Tomlin, Mr. Ernest Portage, Class: 3, Fare: 8.05
Name: Fry, Mr. Richard, Class: 1, Fare: 0.0
Name: Heininen, Miss. Wendla Maria, Class: 3, Fare: 7.925
Name: Mallet, Mr. Albert, Class: 2, Fare: 37.0042
Name: Holm, Mr. John Fredrik Alexander, Class: 3, Fare: 6.45
Name: Skoog, Master. Karl Thorsten, Class: 3, Fare: 27.9
Name: Hays, Mrs. Charles Melville (Clara Jennings Gregg), Class: 1, Fare: 93.5
Name: Lulic, Mr. Nikola, Class: 3, Fare: 8.6625
Name: Reuchlin, Jonkheer. John George, Class: 1, Fare: 0.0
Name: Moor, Mrs. (Beila), Class: 3, Fare: 12.475
Name: Panula, Master. Urho Abraham, Class: 3, Fare: 39.6875
Name: Flynn, Mr. John, Class: 3, Fare: 6.95
Name: Lam, Mr. Len, Class: 3, Fare: 56.4958
Name: Mallet, Master. Andre, Class: 2, Fare: 37.0042
Name: McCormack, Mr. Thomas Joseph, Class: 3, Fare: 7.75
Name: Stone, Mrs. George Nelson (Martha Evelyn), Class: 1, Fare: 80.0
Name: Yasbeck, Mrs. Antoni (Selini Alexander), Class: 3, Fare: 14.4542
Name: Richards, Master. George Sibley, Class: 2, Fare: 18.75
Name: Saad, Mr. Amin, Class: 3, Fare: 7.2292
Name: Augustsson, Mr. Albert, Class: 3, Fare: 7.8542
Name: Allum, Mr. Owen George, Class: 3, Fare: 8.3
Name: Compton, Miss. Sara Rebecca, Class: 1, Fare: 83.1583
Name: Pasic, Mr. Jakob, Class: 3, Fare: 8.6625
Name: Sirota, Mr. Maurice, Class: 3, Fare: 8.05
Name: Chip, Mr. Chang, Class: 3, Fare: 56.4958
Name: Marechal, Mr. Pierre, Class: 1, Fare: 29.7
Name: Alhomaki, Mr. Ilmari Rudolf, Class: 3, Fare: 7.925
Name: Mudd, Mr. Thomas Charles, Class: 2, Fare: 10.5
Name: Serepeca, Miss. Augusta, Class: 1, Fare: 31.0
Name: Lemberopolous, Mr. Peter L, Class: 3, Fare: 6.4375
Name: Culumovic, Mr. Jeso, Class: 3, Fare: 8.6625
Name: Abbing, Mr. Anthony, Class: 3, Fare: 7.55
Name: Sage, Mr. Douglas Bullen, Class: 3, Fare: 69.55
Name: Markoff, Mr. Marin, Class: 3, Fare: 7.8958
Name: Harper, Rev. John, Class: 2, Fare: 33.0
Name: Goldenberg, Mrs. Samuel L (Edwiga Grabowska), Class: 1, Fare: 89.1042
Name: Andersson, Master. Sigvard Harald Elias, Class: 3, Fare: 31.275
Name: Svensson, Mr. Johan, Class: 3, Fare: 7.775
Name: Boulos, Miss. Nourelain, Class: 3, Fare: 15.2458
Name: Lines, Miss. Mary Conover, Class: 1, Fare: 39.4
Name: Carter, Mrs. Ernest Courtenay (Lilian Hughes), Class: 2, Fare: 26.0
Name: Aks, Mrs. Sam (Leah Rosen), Class: 3, Fare: 9.35
Name: Wick, Mrs. George Dennick (Mary Hitchcock), Class: 1, Fare: 164.8667
Name: Daly, Mr. Peter Denis , Class: 1, Fare: 26.55
Name: Baclini, Mrs. Solomon (Latifa Qurban), Class: 3, Fare: 19.2583
Name: Razi, Mr. Raihed, Class: 3, Fare: 7.2292
Name: Hansen, Mr. Claus Peter, Class: 3, Fare: 14.1083
Name: Giles, Mr. Frederick Edward, Class: 2, Fare: 11.5
Name: Swift, Mrs. Frederick Joel (Margaret Welles Barron), Class: 1, Fare: 25.9292
Name: Sage, Miss. Dorothy Edith "Dolly", Class: 3, Fare: 69.55
Name: Gill, Mr. John William, Class: 2, Fare: 13.0
Name: Bystrom, Mrs. (Karolina), Class: 2, Fare: 13.0
Name: Duran y More, Miss. Asuncion, Class: 2, Fare: 13.8583
Name: Roebling, Mr. Washington Augustus II, Class: 1, Fare: 50.4958
Name: van Melkebeke, Mr. Philemon, Class: 3, Fare: 9.5
Name: Johnson, Master. Harold Theodor, Class: 3, Fare: 11.1333
Name: Balkic, Mr. Cerin, Class: 3, Fare: 7.8958
Name: Beckwith, Mrs. Richard Leonard (Sallie Monypeny), Class: 1, Fare: 52.5542
Name: Carlsson, Mr. Frans Olof, Class: 1, Fare: 5.0
Name: Vander Cruyssen, Mr. Victor, Class: 3, Fare: 9.0
Name: Abelson, Mrs. Samuel (Hannah Wizosky), Class: 2, Fare: 24.0
Name: Najib, Miss. Adele Kiamie "Jane", Class: 3, Fare: 7.225
Name: Gustafsson, Mr. Alfred Ossian, Class: 3, Fare: 9.8458
Name: Petroff, Mr. Nedelio, Class: 3, Fare: 7.8958
Name: Laleff, Mr. Kristo, Class: 3, Fare: 7.8958
Name: Potter, Mrs. Thomas Jr (Lily Alexenia Wilson), Class: 1, Fare: 83.1583
Name: Shelley, Mrs. William (Imanita Parrish Hall), Class: 2, Fare: 26.0
Name: Markun, Mr. Johann, Class: 3, Fare: 7.8958
Name: Dahlberg, Miss. Gerda Ulrika, Class: 3, Fare: 10.5167
Name: Banfield, Mr. Frederick James, Class: 2, Fare: 10.5
Name: Sutehall, Mr. Henry Jr, Class: 3, Fare: 7.05
Name: Rice, Mrs. William (Margaret Norton), Class: 3, Fare: 29.125
Name: Montvila, Rev. Juozas, Class: 2, Fare: 13.0
Name: Graham, Miss. Margaret Edith, Class: 1, Fare: 30.0
Name: Johnston, Miss. Catherine Helen "Carrie", Class: 3, Fare: 23.45
Name: Behr, Mr. Karl Howell, Class: 1, Fare: 30.0
Name: Dooley, Mr. Patrick, Class: 3, Fare: 7.75

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

for row in df.iter_rows(named=True).take(5):
    print(f"Name: {row['Name']}, Class: {row['Pclass']}, Fare: {row['Fare']}")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0znuepqz.py", line 7, in 
    for row in df.iter_rows(named=True).take(5):
               ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'generator' object has no attribute 'take'

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
for i, row in enumerate(df.iter_rows()):
    if i < 5:
        print(f"Name: {row[3]}")
        print(f"Class: {row[2]}")
        print(f"Fare: ${row[9]:.2f}")
        print("---")

Output:

Name: Braund, Mr. Owen Harris
Class: 3
Fare: $7.25
---
Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer)
Class: 1
Fare: $71.28
---
Name: Heikkinen, Miss. Laina
Class: 3
Fare: $7.92
---
Name: Futrelle, Mrs. Jacques Heath (Lily May Peel)
Class: 1
Fare: $53.10
---
Name: Allen, Mr. William Henry
Class: 3
Fare: $8.05
---

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
for i, row in enumerate(df.iter_rows(named=True)):
    if i >= 5:
        break
    print(f"Name: {row['Name']}")
    print(f"Class: {row['Pclass']}")
    print(f"Fare: ${row['Fare']:.2f}")
    print("-" * 50)

Output:

Name: Braund, Mr. Owen Harris
Class: 3
Fare: $7.25
--------------------------------------------------
Name: Cumings, Mrs. John Bradley (Florence Briggs Thayer)
Class: 1
Fare: $71.28
--------------------------------------------------
Name: Heikkinen, Miss. Laina
Class: 3
Fare: $7.92
--------------------------------------------------
Name: Futrelle, Mrs. Jacques Heath (Lily May Peel)
Class: 1
Fare: $53.10
--------------------------------------------------
Name: Allen, Mr. William Henry
Class: 3
Fare: $8.05
--------------------------------------------------

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Get column indices for Name, Pclass and Fare
name_idx = df.get_column_index("Name")
class_idx = df.get_column_index("Pclass") 
fare_idx = df.get_column_index("Fare")

# Iterate through first 5 rows
for row in df.iter_rows()[:5]:
    print(f"Name: {row[name_idx]}")
    print(f"Class: {row[class_idx]}")
    print(f"Fare: ${row[fare_idx]:.2f}")
    print("---")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmplu_2uwlv.py", line 12, in 
    for row in df.iter_rows()[:5]:
               ~~~~~~~~~~~~~~^^^^
TypeError: 'generator' object is not subscriptable

Status: ✗ Failed

missing_value_handling

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Replace missing values in the Age column with the median age
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of filling null values: df.with_columns(pl.col("value").fill_null(pl.col("value").median()))

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
median_age = df['Age'].median()
df = df.with_columns(pl.col('Age').fill_null(median_age))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Age").fill_null(pl.col("Age").median()))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Age").fill_null(pl.col("Age").median())
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

median_age = df["Age"].median()
df = df.with_columns(pl.col("Age").fill_null(median_age))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Age").fill_null(pl.col("Age").median()))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

median_age = df["Age"].median()
df = df.with_columns(
    pl.col("Age").fill_null(median_age)
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
median_age = df.select(pl.col("Age").median()).to_numpy()[0][0]
df = df.with_column(pl.col("Age").fill_null(median_age))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpkpfnmowi.py", line 4, in 
    df = df.with_column(pl.col("Age").fill_null(median_age))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Age").fill_null(pl.col("Age").median()))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.when(pl.col("Age").is_null())
    .then(pl.col("Age").median())
    .otherwise(pl.col("Age"))
    .alias("Age")
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Calculate median age (ignoring null values)
median_age = df.select(pl.col("Age").median()).item()

# Replace missing values in Age column with median age
df = df.with_columns(
    pl.col("Age").fill_null(median_age)
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Replace missing values in Age with median age
df = df.with_columns(pl.col("Age").fill_null(pl.col("Age").median()))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Replace missing values in the Age column with the median age
median_age = df.select(pl.col("Age").median()).item()
df = df.with_columns(
    pl.col("Age").fill_null(median_age)
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
median_age = df.select(pl.col("Age")).median()
df = df.with_column(pl.col("Age").fill(median_age))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfv2xdeu3.py", line 7, in 
    df = df.with_column(pl.col("Age").fill(median_age))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Age").fill_null(pl.col("Age").median()))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Age").fill_null(pl.col("Age").median()))

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    df = df.with_columns(
        pl.col("Age").fill_null(pl.col("Age").median())
    )
    return df

if __name__ == "__main__":
    df = solve()
    print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9jo8y92s.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Age").fill_null(pl.col("Age").median()))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8na6xwki.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Age").fill_null(pl.col("Age").median())
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpnuze3by8.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
median_age = df['Age'].median()
df = df.with_columns(pl.col('Age').fill_null(median_age))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Age").fill_null(pl.col("Age").median()))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Age").fill_null(pl.col("Age").median()).alias("Age")
)

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

multiple_column_arithmetic

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Create a new column 'fare_per_family' by dividing Fare by family_size (where family_size is SibSp + Parch + 1)
# Round the result to 2 decimal places
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of complex column arithmetic: df.with_columns((pl.col("a") / (pl.col("b") + 1)).round(2).alias("result"))

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns([
    pl.col("Fare").divide(pl.col("SibSp") + pl.col("Parch") + 1).round(2).alias("fare_per_family")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp3cc77tck.py", line 7, in 
    pl.col("Fare").divide(pl.col("SibSp") + pl.col("Parch") + 1).round(2).alias("fare_per_family")
    ^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'divide'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns((pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns((pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).alias("fare_per_family").round(2))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns([
    pl.col("Fare").div(pl.col("SibSp") + pl.col("Parch") + 1).round(2).alias("fare_per_family")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdw_zu6bu.py", line 4, in 
    pl.col("Fare").div(pl.col("SibSp") + pl.col("Parch") + 1).round(2).alias("fare_per_family")
    ^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'div'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns((pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1))
    .round(2)
    .alias("fare_per_family")
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family")
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    ((pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family")
)

Error:

'(' was never closed (, line 6)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family")
)

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    ((pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family"))
)

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    ((pl.col("Fare")) / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    (pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Create the 'fare_per_family' column
df = df.with_columns(
    (pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family")
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns([
    ((pl.col("SibSp") + pl.col("Parch") + 1).alias("family_size")),
    ((pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family"))
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    (pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1)).round(2).alias("fare_per_family")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    ((pl.col("Fare") / (pl.col("SibSp") + pl.col("Parch") + 1))
     .round(2)
     .alias("fare_per_family"))
)

Status: ✓ Success

pandas_to_polars_groupby

Original Prompt

# Convert this Pandas code to Polars:
# df = pd.read_csv("data/titanic.csv")
# result = df.groupby("Pclass").agg({
#     "Fare": ["mean", "std", "count"],
#     "Age": ["min", "max"]
# }).round(2)
#

Suffix Prompt

# Here is how to do multiple aggregations in Polars:
# df.group_by("group").agg([
#     pl.col("value1").mean().round(2).alias("value1_mean"),
#     pl.col("value1").std().round(2).alias("value1_std"),
#     pl.col("value2").min().round(2).alias("value2_min")
# ])

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg([
    pl.col("Fare").mean().round(2).alias("Fare_mean"),
    pl.col("Fare").std().round(2).alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().round(2).alias("Age_min"),
    pl.col("Age").max().round(2).alias("Age_max")
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg([
    pl.col("Fare").mean().round(2).alias("Fare_mean"),
    pl.col("Fare").std().round(2).alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().round(2).alias("Age_min"),
    pl.col("Age").max().round(2).alias("Age_max")
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg([
    pl.col("Fare").mean().round(2).alias("Fare_mean"),
    pl.col("Fare").std().round(2).alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().round(2).alias("Age_min"),
    pl.col("Age").max().round(2).alias("Age_max")
])

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    [
        pl.col("Fare").mean().round(2).alias("Fare_mean"),
        pl.col("Fare").std().round(2).alias("Fare_std"),
        pl.col("Fare").count().alias("Fare_count"),
        pl.col("Age").min().alias("Age_min"),
        pl.col("Age").max().alias("Age_max"),
    ]
)

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.groupby("Pclass").agg([
    pl.col("Fare").mean().round(2).alias("Fare_mean"),
    pl.col("Fare").std().round(2).alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().alias("Age_min"),
    pl.col("Age").max().alias("Age_max")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpmq65lez3.py", line 6, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.groupby("Pclass").agg([
    pl.col("Fare").mean().round(2).alias("Fare_mean"),
    pl.col("Fare").std().round(2).alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().alias("Age_min"),
    pl.col("Age").max().alias("Age_max")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp6ilthc1k.py", line 6, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg([
    pl.col("Fare").mean().round(2).alias("Fare_mean"),
    pl.col("Fare").std().round(2).alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().alias("Age_min"),
    pl.col("Age").max().alias("Age_max")
])

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.groupby("Pclass").agg([
    pl.col("Fare").mean().round(2).alias("Fare_mean"),
    pl.col("Fare").std().round(2).alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().round(2).alias("Age_min"),
    pl.col("Age").max().round(2).alias("Age_max")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpvbuuxh4l.py", line 6, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg([
    pl.col("Fare").mean().round(2).alias("Fare_mean"),
    pl.col("Fare").std().round(2).alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().round(2).alias("Age_min"),
    pl.col("Age").max().round(2).alias("Age_max")
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg([
    pl.col("Fare").mean().alias("Fare_mean"),
    pl.col("Fare").std().alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().alias("Age_min"),
    pl.col("Age").max().alias("Age_max")
]).with_columns(
    pl.all().exclude("Pclass").round(2)
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.groupby("Pclass").agg([
    pl.col("Fare").mean().round(2),
    pl.col("Fare").std().round(2),
    pl.col("Fare").count(),
    pl.col("Age").min().round(2),
    pl.col("Age").max().round(2),
]).unstack("Pclass")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpah6mo0z0.py", line 6, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.groupby("Pclass").agg([
    pl.col("Fare").mean().round(2).alias("Fare_mean"),
    pl.col("Fare").std().round(2).alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().round(2).alias("Age_min"),
    pl.col("Age").max().round(2).alias("Age_max")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp4_mp9h9v.py", line 6, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

df = pl.read_csv("data/titanic.csv", try_parse_dates=True)
result = df.groupby("Pclass").agg(
    pl.col("Fare").mean().round(2),
    pl.col("Fare").std().round(2),
    pl.col("Fare").count(),
    pl.col("Age").min().round(2),
    pl.col("Age").max().round(2)
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp78gihbrx.py", line 5, in 
    result = df.groupby("Pclass").agg(
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    result = (
        df.groupby("Pclass")
        .agg(
            [
                pl.col("Fare").mean().alias("Fare_mean"),
                pl.col("Fare").std().alias("Fare_std"),
                pl.col("Fare").count().alias("Fare_count"),
                pl.col("Age").min().alias("Age_min"),
                pl.col("Age").max().alias("Age_max"),
            ]
        )
        .round(2)
    )
    return result

if __name__ == "__main__":
    solve()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdkar7b0_.py", line 23, in 
    solve()
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdkar7b0_.py", line 8, in solve
    df.groupby("Pclass")
    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    result = (
        df.group_by("Pclass")
        .agg([
            pl.col("Fare").mean().round(2).alias("Fare_mean"),
            pl.col("Fare").std().round(2).alias("Fare_std"),
            pl.col("Fare").count().alias("Fare_count"),
            pl.col("Age").min().round(2).alias("Age_min"),
            pl.col("Age").max().round(2).alias("Age_max"),
        ])
    )
    return result

if __name__ == "__main__":
    solve()

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

# Convert this Pandas code to Polars:
# df = pd.read_csv("data/titanic.csv")
# result = df.groupby("Pclass").agg({
#     "Fare": ["mean", "std", "count"],
#     "Age": ["min", "max"]
# }).round(2)

df = pl.read_csv("data/titanic.csv")
result = (
    df.group_by("Pclass")
    .agg(
        [
            pl.col("Fare").mean().alias("Fare_mean"),
            pl.col("Fare").std().alias("Fare_std"),
            pl.col("Fare").count().alias("Fare_count"),
            pl.col("Age").min().alias("Age_min"),
            pl.col("Age").max().alias("Age_max"),
        ]
    )
    .round(2)
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdhl20bh1.py", line 25, in 
    .round(2)
     ^^^^^
AttributeError: 'DataFrame' object has no attribute 'round'. Did you mean: 'count'?

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg([
    pl.col("Fare").mean().alias("Fare_mean"),
    pl.col("Fare").std().alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().alias("Age_min"),
    pl.col("Age").max().alias("Age_max")
]).round(2)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpw5fjunjr.py", line 12, in 
    ]).round(2)
       ^^^^^
AttributeError: 'DataFrame' object has no attribute 'round'. Did you mean: 'count'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg([
    pl.col("Fare").mean().round(2).alias("Fare_mean"),
    pl.col("Fare").std().round(2).alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().alias("Age_min"),
    pl.col("Age").max().alias("Age_max")
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = pl.read_csv("data/titanic.csv")
result = df.group_by("Pclass").agg([
    pl.col("Fare").mean().alias("Fare_mean"),
    pl.col("Fare").std().alias("Fare_std"),
    pl.col("Fare").count().alias("Fare_count"),
    pl.col("Age").min().alias("Age_min"),
    pl.col("Age").max().alias("Age_max")
]).round(2)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpqnfqhca3.py", line 10, in 
    ]).round(2)
       ^^^^^
AttributeError: 'DataFrame' object has no attribute 'round'. Did you mean: 'count'?

Status: ✗ Failed

pandas_to_polars_groupby_transform

Original Prompt

# Convert this Pandas code to Polars:
# df = pd.read_csv("data/titanic.csv")
# df['fare_zscore'] = df.groupby('Pclass')['Fare'].transform(lambda x: (x - x.mean()) / x.std())
#

Suffix Prompt

# Here is how to do groupby transform in Polars:
# df.with_columns(
#     ((pl.col("value") - pl.col("value").mean().over("group")) / 
#      pl.col("value").std().over("group")).alias("zscore")
# )

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    df = df.with_columns(
        (
            (pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) / pl.col("Fare").std().over("Pclass")
        ).alias("fare_zscore")
    )
    return df

if __name__ == "__main__":
    # Create a dummy titanic.csv for testing
    import pandas as pd
    import numpy as np
    data = {
        'Pclass': [1, 1, 2, 2, 3, 3],
        'Fare': [100.0, 120.0, 20.0, 25.0, 8.0, 10.0]
    }
    pd.DataFrame(data).to_csv("data/titanic.csv", index=False)
    
    result_df = solve()
    print(result_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_h_yu0zw.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def pandas_to_polars(file_path: str) -> pl.DataFrame:
    """
    Converts the given Pandas code to Polars.

    Args:
        file_path (str): The path to the CSV file.

    Returns:
        pl.DataFrame: The Polars DataFrame with the 'fare_zscore' column.
    """

    df = pl.read_csv(file_path)
    df = df.with_columns(
        ((pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) /
         pl.col("Fare").std().over("Pclass")).alias("fare_zscore")
    )
    return df

if __name__ == '__main__':
    # Create a dummy titanic.csv file for testing
    import pandas as pd
    import numpy as np
    data = {'Pclass': [1, 1, 2, 2, 3, 3], 
            'Fare': [100, 120, 50, 60, 10, 15]}
    df_pd = pd.DataFrame(data)
    df_pd.to_csv("titanic.csv", index=False)

    # Example usage:
    try:
        df_pl = pandas_to_polars("titanic.csv")
        print(df_pl)
    except FileNotFoundError:
        print("Error: titanic.csv not found.  Ensure a titanic.csv file exists or adjust the test file path accordingly.")

    # Optionally remove the dummy file after testing
    import os
    try:
        os.remove("titanic.csv")
    except FileNotFoundError:
        pass  # File may not exist if the previous part failed

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpovyo3g2i.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def pandas_to_polars_zscore(file_path="data/titanic.csv"):
    """
    Converts the given Pandas code to Polars.

    Args:
        file_path (str): The path to the CSV file.

    Returns:
        pl.DataFrame: The Polars DataFrame with the 'fare_zscore' column.
    """
    df = pl.read_csv(file_path)

    df = df.with_columns(
        ((pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) / pl.col("Fare").std().over("Pclass")).alias("fare_zscore")
    )

    return df

if __name__ == '__main__':
    # Example Usage (assuming you have a "data/titanic.csv" file):
    try:
        polars_df = pandas_to_polars_zscore()
        print(polars_df.head())  # Print the first few rows to verify
    except FileNotFoundError:
        print("Error: 'data/titanic.csv' not found.  Please ensure the file exists or provide a valid file path to the function.")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp6nig7b57.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    (pl.col("Fare") - pl.col("Fare").mean()) / pl.col("Fare").std()
    .over("Pclass")
    .alias("fare_zscore")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    ((pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) /
     pl.col("Fare").std().over("Pclass")).alias("fare_zscore")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    (pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) / pl.col("Fare").std().over("Pclass")
    .alias("fare_zscore")
)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    ((pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) / pl.col("Fare").std().over("Pclass")).alias("fare_zscore")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    ((pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) / 
     pl.col("Fare").std().over("Pclass")).alias("fare_zscore")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    ((pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) / pl.col("Fare").std().over("Pclass")).alias("fare_zscore")
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns([
    pl.col("Fare").apply(lambda x: (x - x.mean()) / x.std()).over("Pclass").alias("fare_zscore")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp5zk7tdt5.py", line 7, in 
    pl.col("Fare").apply(lambda x: (x - x.mean()) / x.std()).over("Pclass").alias("fare_zscore")
    ^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'apply'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    ((pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) / 
     pl.col("Fare").std().over("Pclass")).alias("fare_zscore")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the converted Polars code:


df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Fare").over("Pclass").mean().subtract(pl.col("Fare")).divide(pl.col("Fare").over("Pclass").std()).alias("fare_zscore")
)

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col('Fare')
    .map_groups('Pclass', lambda x: (x - x.mean()) / x.std())
    .alias('fare_zscore')
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwcl4gmw0.py", line 8, in 
    .map_groups('Pclass', lambda x: (x - x.mean()) / x.std())
     ^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'map_groups'. Did you mean: 'agg_groups'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    ((pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) / 
     pl.col("Fare").std().over("Pclass")).alias("fare_zscore")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
(pl.read_csv("data/titanic.csv")
    .with_columns(
        pl.col("Fare")
        .sub(pl.col("Fare").mean().over("Pclass"))
        .div(pl.col("Fare").std().over("Pclass"))
        .alias("fare_zscore")
    )
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfo8wdx6m.py", line 7, in 
    .div(pl.col("Fare").std().over("Pclass"))
     ^^^
AttributeError: 'Expr' object has no attribute 'div'

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Fare")
    .sub(pl.col("Fare").mean().over("Pclass"))
    .div(pl.col("Fare").std().over("Pclass"))
    .alias("fare_zscore")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1rcesldd.py", line 9, in 
    .div(pl.col("Fare").std().over("Pclass"))
     ^^^
AttributeError: 'Expr' object has no attribute 'div'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    ((pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) / 
     pl.col("Fare").std().over("Pclass")).alias("fare_zscore")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Polars equivalent
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Fare")
    .sub(pl.col("Fare").mean().over("Pclass"))
    .div(pl.col("Fare").std().over("Pclass"))
    .alias("fare_zscore")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpctl9hw8f.py", line 10, in 
    .div(pl.col("Fare").std().over("Pclass"))
     ^^^
AttributeError: 'Expr' object has no attribute 'div'

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_fare_zscore(df: pl.DataFrame) -> pl.DataFrame:
    """
    Calculates the fare z-score for each passenger based on their passenger class.

    Args:
        df: A Polars DataFrame containing the Titanic dataset.

    Returns:
        A Polars DataFrame with an additional column 'fare_zscore' 
        representing the z-score of the fare for each passenger class.
    """
    df = df.with_columns(
        pl.col("Fare").diff().over("Pclass").alias("fare_diff")
    )
    df = df.with_columns(
        (pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) / pl.col("Fare").std().over("Pclass")
        .alias("fare_zscore")
    )
    return df

if __name__ == '__main__':
    try:
        df = pl.read_csv("data/titanic.csv")
        df_with_zscore = calculate_fare_zscore(df)
        print(df_with_zscore.head())
    except FileNotFoundError:
        print("Error: titanic.csv not found.  Please ensure the file exists in the 'data' directory.")

Output:

shape: (5, 13)
┌─────────────┬──────────┬────────┬─────────────────┬───┬───────────┬───────┬──────────┬───────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name            ┆ … ┆ Fare      ┆ Cabin ┆ Embarked ┆ fare_diff │
│ ---         ┆ ---      ┆ ---    ┆ ---             ┆   ┆ ---       ┆ ---   ┆ ---      ┆ ---       │
│ i64         ┆ i64      ┆ i64    ┆ str             ┆   ┆ f64       ┆ str   ┆ str      ┆ f64       │
╞═════════════╪══════════╪════════╪═════════════════╪═══╪═══════════╪═══════╪══════════╪═══════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr.     ┆ … ┆ -0.545549 ┆ null  ┆ S        ┆ null      │
│             ┆          ┆        ┆ Owen Harris     ┆   ┆           ┆       ┆          ┆           │
│ 2           ┆ 1        ┆ 1      ┆ Cumings, Mrs.   ┆ … ┆ -0.164217 ┆ C85   ┆ C        ┆ null      │
│             ┆          ┆        ┆ John Bradley    ┆   ┆           ┆       ┆          ┆           │
│             ┆          ┆        ┆ (Fl…            ┆   ┆           ┆       ┆          ┆           │
│ 3           ┆ 1        ┆ 3      ┆ Heikkinen,      ┆ … ┆ -0.488239 ┆ null  ┆ S        ┆ 0.675     │
│             ┆          ┆        ┆ Miss. Laina     ┆   ┆           ┆       ┆          ┆           │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle, Mrs.  ┆ … ┆ -0.396205 ┆ C123  ┆ S        ┆ -18.1833  │
│             ┆          ┆        ┆ Jacques Heath   ┆   ┆           ┆       ┆          ┆           │
│             ┆          ┆        ┆ (…              ┆   ┆           ┆       ┆          ┆           │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr.      ┆ … ┆ -0.477626 ┆ null  ┆ S        ┆ 0.125     │
│             ┆          ┆        ┆ William Henry   ┆   ┆           ┆       ┆          ┆           │
└─────────────┴──────────┴────────┴─────────────────┴───┴───────────┴───────┴──────────┴───────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_fare_zscore(df: pl.DataFrame) -> pl.DataFrame:
    """
    Calculates the fare z-score for each passenger, grouped by Pclass.

    Args:
        df: Polars DataFrame containing Titanic data.

    Returns:
        Polars DataFrame with an added 'fare_zscore' column.
    """
    return (
        df.with_columns(
            ((pl.col("Fare") - pl.col("Fare").mean().over("Pclass")) /
             pl.col("Fare").std().over("Pclass")).alias("fare_zscore")
        )
    )

if __name__ == '__main__':
    # Example usage:
    try:
        df = pl.read_csv("data/titanic.csv")
        df_with_zscore = calculate_fare_zscore(df)
        print(df_with_zscore.head())
    except FileNotFoundError:
        print("Error: titanic.csv not found.  Please ensure the file exists in the data directory.")

Output:

shape: (5, 13)
┌─────────────┬──────────┬────────┬─────────────────┬───┬─────────┬───────┬──────────┬─────────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name            ┆ … ┆ Fare    ┆ Cabin ┆ Embarked ┆ fare_zscore │
│ ---         ┆ ---      ┆ ---    ┆ ---             ┆   ┆ ---     ┆ ---   ┆ ---      ┆ ---         │
│ i64         ┆ i64      ┆ i64    ┆ str             ┆   ┆ f64     ┆ str   ┆ str      ┆ f64         │
╞═════════════╪══════════╪════════╪═════════════════╪═══╪═════════╪═══════╪══════════╪═════════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr.     ┆ … ┆ 7.25    ┆ null  ┆ S        ┆ -0.545549   │
│             ┆          ┆        ┆ Owen Harris     ┆   ┆         ┆       ┆          ┆             │
│ 2           ┆ 1        ┆ 1      ┆ Cumings, Mrs.   ┆ … ┆ 71.2833 ┆ C85   ┆ C        ┆ -0.164217   │
│             ┆          ┆        ┆ John Bradley    ┆   ┆         ┆       ┆          ┆             │
│             ┆          ┆        ┆ (Fl…            ┆   ┆         ┆       ┆          ┆             │
│ 3           ┆ 1        ┆ 3      ┆ Heikkinen,      ┆ … ┆ 7.925   ┆ null  ┆ S        ┆ -0.488239   │
│             ┆          ┆        ┆ Miss. Laina     ┆   ┆         ┆       ┆          ┆             │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle, Mrs.  ┆ … ┆ 53.1    ┆ C123  ┆ S        ┆ -0.396205   │
│             ┆          ┆        ┆ Jacques Heath   ┆   ┆         ┆       ┆          ┆             │
│             ┆          ┆        ┆ (…              ┆   ┆         ┆       ┆          ┆             │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr.      ┆ … ┆ 8.05    ┆ null  ┆ S        ┆ -0.477626   │
│             ┆          ┆        ┆ William Henry   ┆   ┆         ┆       ┆          ┆             │
└─────────────┴──────────┴────────┴─────────────────┴───┴─────────┴───────┴──────────┴─────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def polars_zscore(df: pl.DataFrame) -> pl.DataFrame:
    """
    Calculates the z-score of 'Fare' within each 'Pclass' group in a Polars DataFrame.

    Args:
        df: A Polars DataFrame.

    Returns:
        A Polars DataFrame with an added 'fare_zscore' column.
    """
    return df.with_columns(
        pl.col("Fare").pl.groupby(pl.col("Pclass")).std().alias("Fare_std"),
        pl.col("Fare").pl.groupby(pl.col("Pclass")).mean().alias("Fare_mean"),

        (pl.col("Fare") - pl.col("Fare_mean")) / pl.col("Fare_std").alias("fare_zscore")
    ).drop(["Fare_std", "Fare_mean"])

if __name__ == '__main__':
    # Example usage:
    try:
        df = pl.read_csv("data/titanic.csv")
        df = polars_zscore(df)
        print(df.head())
    except FileNotFoundError:
        print("Titanic dataset not found.  Please provide a valid 'data/titanic.csv' file.")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpgf0161uf.py", line 26, in 
    df = polars_zscore(df)
         ^^^^^^^^^^^^^^^^^
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpgf0161uf.py", line 16, in polars_zscore
    pl.col("Fare").pl.groupby(pl.col("Pclass")).std().alias("Fare_std"),
    ^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'pl'

Status: ✗ Failed

pandas_to_polars_index_operations

Original Prompt

# Convert this Pandas code to Polars:
# df = pd.read_csv("data/titanic.csv")
# df = df.set_index('PassengerId')
# df['Fare'] = df['Fare'].fillna(df['Fare'].mean())
# df = df.reset_index()
#

Suffix Prompt

# In Polars, you don't need to set/reset index. Just use with_columns:
# df.with_columns(
#     pl.col("value").fill_null(pl.col("value").mean())
# )

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Fare").fill_null(pl.col("Fare").mean())
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Fare").fill_null(pl.col("Fare").mean())
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = (pl.read_csv("data/titanic.csv")
      .with_columns([
          pl.col("Fare").fill_null(pl.col("Fare").mean())
      ]))

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Fare").fill_null(pl.col("Fare").mean())
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Fare").fill_null(pl.col("Fare").mean())
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").fill_null(pl.col("Fare").mean()))

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.col("PassengerId").alias("index")).set_index("index")
df = df.with_column(pl.col("Fare").fill_null(pl.col("Fare").mean()))
df = df.set_index(None)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpehf58f0h.py", line 6, in 
    df = df.with_column(pl.col("PassengerId").alias("index")).set_index("index")
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").fill_null(pl.col("Fare").mean()))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Polars code that corresponds to the given Pandas code:


df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").fill_null(pl.col("Fare").mean()))
df = df.set_index("PassengerId")

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col('Fare').fill_null(pl.col('Fare').mean())
).with_column(pl.col("PassengerId")).select(
    ["PassengerId"] + [col for col in df.columns if col != "PassengerId"]
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp60zsugvg.py", line 8, in 
    ).with_column(pl.col("PassengerId")).select(
      ^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Fare").fill_null(pl.col("Fare").mean())
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = pl.read_csv("data/titanic.csv")
df = df.with_row_index("PassengerId")
df = df.with_columns(
    pl.when(pl.col("Fare").is_null())
    .then(pl.col("Fare").mean())
    .otherwise(pl.col("Fare"))
    .alias("Fare")
)
df = df.with_columns(pl.col("PassengerId").cast(pl.Int64))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpkel04n_j.py", line 4, in 
    df = df.with_row_index("PassengerId")
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 6419, in with_row_index
    return self._from_pydf(self._df.with_row_index(name, offset))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.DuplicateError: could not create a new DataFrame: column with name 'PassengerId' has more than one occurrence

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def convert_pandas_to_polars(file_path: str) -> pl.DataFrame:
    """
    Converts the given Pandas code to Polars.

    Args:
        file_path (str): The path to the CSV file.

    Returns:
        pl.DataFrame: The Polars DataFrame.
    """
    df = pl.read_csv(file_path)
    df = df.with_columns(pl.lit("PassengerId").alias("PassengerId"))
    df = df.with_columns(pl.col("PassengerId").cast(pl.Int64))
    df = df.set_index("PassengerId")
    df = df.with_columns(
        pl.col("Fare").fill_null(pl.col("Fare").mean())
    )
    df = df.reset_index()
    return df

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def convert_pandas_to_polars():
    df = pl.read_csv("data/titanic.csv")
    df = df.with_columns(
        pl.col("Fare").fill_null(pl.col("Fare").mean())
    )
    return df

if __name__ == '__main__':
    # Create a dummy titanic.csv for testing
    import pandas as pd
    data = {'PassengerId': [1, 2, 3, 4, 5],
            'Survived': [0, 1, 0, 1, 0],
            'Pclass': [3, 1, 2, 1, 3],
            'Name': ['test1', 'test2', 'test3', 'test4', 'test5'],
            'Sex': ['male', 'female', 'male', 'female', 'male'],
            'Age': [22.0, 38.0, 26.0, 35.0, None],
            'SibSp': [1, 0, 0, 0, 1],
            'Parch': [0, 0, 0, 0, 0],
            'Ticket': ['A/5 21171', 'PC 17599', 'STON/O2. 3101282', '113803', 'A/5. 2151'],
            'Fare': [7.25, 71.28, 7.92, 53.1, None],
            'Cabin': ['NaN', 'C85', 'NaN', 'C123', 'NaN'],
            'Embarked': ['S', 'C', 'S', 'S', 'S']}
    test_df = pd.DataFrame(data)
    test_df.to_csv("data/titanic.csv", index=False)

    polars_df = convert_pandas_to_polars()
    print(polars_df)

Output:

shape: (5, 12)
┌─────────────┬──────────┬────────┬───────┬───┬──────────────────┬─────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name  ┆ … ┆ Ticket           ┆ Fare    ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---   ┆   ┆ ---              ┆ ---     ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str   ┆   ┆ str              ┆ f64     ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪═══════╪═══╪══════════════════╪═════════╪═══════╪══════════╡
│ 1           ┆ 0        ┆ 3      ┆ test1 ┆ … ┆ A/5 21171        ┆ 7.25    ┆ NaN   ┆ S        │
│ 2           ┆ 1        ┆ 1      ┆ test2 ┆ … ┆ PC 17599         ┆ 71.28   ┆ C85   ┆ C        │
│ 3           ┆ 0        ┆ 2      ┆ test3 ┆ … ┆ STON/O2. 3101282 ┆ 7.92    ┆ NaN   ┆ S        │
│ 4           ┆ 1        ┆ 1      ┆ test4 ┆ … ┆ 113803           ┆ 53.1    ┆ C123  ┆ S        │
│ 5           ┆ 0        ┆ 3      ┆ test5 ┆ … ┆ A/5. 2151        ┆ 34.8875 ┆ NaN   ┆ S        │
└─────────────┴──────────┴────────┴───────┴───┴──────────────────┴─────────┴───────┴──────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

# Convert this Pandas code to Polars:
# df = pd.read_csv("data/titanic.csv")
# df = df.set_index('PassengerId')
# df['Fare'] = df['Fare'].fillna(df['Fare'].mean())
# df = df.reset_index()

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.lit(None).alias("PassengerId"))
df = df.drop("PassengerId")
df = df.with_columns(pl.col("Fare").fill_null(pl.col("Fare").mean())) 
df = df.with_columns(pl.lit(None).alias("PassengerId"))

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
mean_fare = df.select(pl.col("Fare").mean()).item()
df = df.with_column(pl.col("Fare").fill_null(mean_fare))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpbohg2rji.py", line 7, in 
    df = df.with_column(pl.col("Fare").fill_null(mean_fare))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Fare").fill_null(pl.col("Fare").mean())
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Read the CSV file
df = pl.read_csv("data/titanic.csv")

# Calculate the mean fare
mean_fare = df.select(pl.col("Fare").mean()).item()

# Fill null values in Fare column with the mean
df = df.with_columns(
    pl.col("Fare").fill_null(mean_fare)
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def pandas_to_polars_titanic(csv_path="data/titanic.csv"):
    df = pl.read_csv(csv_path)
    
    df = df.with_columns(df["Fare"].fill_null(df["Fare"].mean()).alias("Fare"))
    
    return df

if __name__ == '__main__':
    # Example usage (assuming you have a titanic.csv file in a data directory)
    try:
        result_df = pandas_to_polars_titanic()
        print(result_df.head())
    except FileNotFoundError:
        print("Error: data/titanic.csv not found.  Please ensure the file exists.")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpne68nljx.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def pandas_to_polars(file_path):
  """
  Converts Pandas code for reading, setting index, filling nulls, and resetting index to Polars.

  Args:
      file_path (str): The path to the CSV file.

  Returns:
      polars.DataFrame: The resulting Polars DataFrame.
  """
  df = pl.read_csv(file_path)
  df = df.with_columns(
      pl.col("Fare").fill_null(pl.col("Fare").mean())
  )
  return df

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpf0wfox9n.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Create a Polars DataFrame from the CSV file
df = pl.read_csv("data/titanic.csv")

# Set 'PassengerId' as index (not directly supported in Polars, but you can keep it as a column)
# df = df.with_row_index('PassengerId')  # Optionally add row index

# Fill missing 'Fare' values with the mean
df = df.with_columns(
    pl.col('Fare').fill_null(pl.col('Fare').mean())
)

# Reset index (if you added a row index, this is how you'd reset it)
# df = df.drop('index')  # if you set an index before

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp4ylqdwsw.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

pandas_to_polars_merge

Original Prompt

# Convert this Pandas code to Polars:
# df1 = pd.DataFrame({
#     "id": [1, 2, 3],
#     "value": [10, 20, 30]
# })
# df2 = pd.DataFrame({
#     "id": [1, 2, 4],
#     "category": ["A", "B", "C"]
# })
# result = pd.merge(df1, df2, on="id", how="left")
#

Suffix Prompt

# Here is how to do a left join in Polars:
# df1.join(df2, on="id", how="left")

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4], 
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Create the first DataFrame
df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

# Create the second DataFrame
df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

# Perform a left join
result = df1.join(df2, on="id", how="left")

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

df1 = pl.DataFrame({"id": [1, 2, 3], "value": [10, 20, 30]})
df2 = pl.DataFrame({"id": [1, 2, 4], "category": ["A", "B", "C"]})
result = df1.join(df2, on="id", how="left")

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})
df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})
result = df1.join(df2, on="id", how="left")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpgjc6q5_8.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})
df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})
result = df1.join(df2, on="id", how="left")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8znnpx1y.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpvcwpl8i_.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})
df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})
result = df1.join(df2, on="id", how="left")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})
df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})
result = df1.join(df2, on="id", how="left")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df1 = pl.DataFrame({
    "id": [1, 2, 3],
    "value": [10, 20, 30]
})

df2 = pl.DataFrame({
    "id": [1, 2, 4],
    "category": ["A", "B", "C"]
})

result = df1.join(df2, on="id", how="left")

Status: ✓ Success

partition_dataframe

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Partition the Polars DataFrame by Survived and Pclass columns, returning a dictionary of DataFrames
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars syntax: df.partition_by("id",as_dict=True)

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

partitioned_df = df.partition(by=['Survived', 'Pclass'], as_dict=True)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmprv955ev4.py", line 8, in 
    partitioned_df = df.partition(by=['Survived', 'Pclass'], as_dict=True)
                     ^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'partition'. Did you mean: 'partition_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
partitions = df.partition_by(["Survived", "Pclass"], as_dict=True)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

partitioned_data = {f"{survived}_{pclass}": group for (survived, pclass), group in df.groupby(["Survived", "Pclass"]).items()}

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpd8xbkoa1.py", line 8, in 
    partitioned_data = {f"{survived}_{pclass}": group for (survived, pclass), group in df.groupby(["Survived", "Pclass"]).items()}
                                                                                       ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def partition_dataframe(df: pl.DataFrame) -> dict[tuple[int, int], pl.DataFrame]:
    """
    Partitions a Polars DataFrame by the 'Survived' and 'Pclass' columns.

    Args:
        df: The Polars DataFrame to partition.

    Returns:
        A dictionary where keys are tuples of (Survived, Pclass) and values are the corresponding DataFrames.
    """

    partitions = {}
    for survived in df["Survived"].unique():
        for pclass in df["Pclass"].unique():
            key = (survived, pclass)
            partitions[key] = df.filter((pl.col("Survived") == survived) & (pl.col("Pclass") == pclass))
    return partitions

if __name__ == '__main__':
    # Example Usage (replace with your actual data loading)
    try:
        df = pl.read_csv("data/titanic.csv")
    except FileNotFoundError:
        # Create a dummy DataFrame if the file is not found
        data = {
            "PassengerId": [1, 2, 3, 4, 5, 6],
            "Survived": [0, 1, 0, 1, 0, 1],
            "Pclass": [3, 1, 3, 1, 2, 2],
            "Name": ["A", "B", "C", "D", "E", "F"],
            "Sex": ["male", "female", "male", "female", "male", "female"],
            "Age": [22.0, 38.0, 26.0, 35.0, 28.0, 2.0],
            "SibSp": [1, 1, 0, 1, 0, 0],
            "Parch": [0, 0, 0, 0, 0, 0],
            "Ticket": ["A/5 21171", "PC 17599", "STON/O2. 3101282", "113803", "345779", "248738"],
            "Fare": [7.2500, 71.2833, 7.9250, 53.1000, 13.0000, 30.0708],
            "Cabin": [None, "C85", None, "C123", None, None],
            "Embarked": ["S", "C", "S", "S", "Q", "S"],
        }
        df = pl.DataFrame(data)

    partitions = partition_dataframe(df)

    # Print the number of partitions and some info from the first partition
    print(f"Number of partitions: {len(partitions)}")
    if partitions:
        first_key = next(iter(partitions))
        print(f"First partition key: {first_key}")
        print(f"First partition shape: {partitions[first_key].shape}")
        # Print the first 5 rows of the first partition
        print(f"First partition head:\n{partitions[first_key].head(5)}")

Output:

Number of partitions: 6
First partition key: (0, 1)
First partition shape: (0, 12)
First partition head:
shape: (0, 12)
┌─────────────┬──────────┬────────┬──────┬───┬────────┬──────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name ┆ … ┆ Ticket ┆ Fare ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---  ┆   ┆ ---    ┆ ---  ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str  ┆   ┆ str    ┆ f64  ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════╪═══╪════════╪══════╪═══════╪══════════╡
└─────────────┴──────────┴────────┴──────┴───┴────────┴──────┴───────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def partition_dataframe(df: pl.DataFrame) -> dict[tuple[int, int], pl.DataFrame]:
    """
    Partitions a Polars DataFrame by Survived and Pclass columns, returning a dictionary of DataFrames.
    """
    return df.group_by(["Survived", "Pclass"], maintain_order=True).to_dict(as_series=False)


if __name__ == '__main__':
    # Example usage with dummy data
    data = {
        'PassengerId': [1, 2, 3, 4, 5, 6],
        'Survived': [0, 1, 0, 1, 0, 1],
        'Pclass': [1, 2, 3, 1, 2, 3],
        'Name': ['A', 'B', 'C', 'D', 'E', 'F'],
        'Sex': ['male', 'female', 'male', 'female', 'male', 'female'],
        'Age': [22, 38, 26, 35, 35, 20],
        'SibSp': [1, 1, 0, 1, 0, 0],
        'Parch': [0, 0, 0, 0, 0, 0],
        'Ticket': ['A/5 21171', 'PC 17599', 'STON/O2. 3101282', '113803', '373450', '330877'],
        'Fare': [7.25, 71.2833, 7.925, 53.1, 8.05, 8.4583],
        'Cabin': [None, 'C85', None, 'C123', None, None],
        'Embarked': ['S', 'C', 'S', 'S', 'S', 'Q']
    }

    df = pl.DataFrame(data)

    partitioned_data = partition_dataframe(df)

    # Print the partitioned data (for demonstration)
    # for key, value in partitioned_data.items():
    #     print(f"Key: {key}")
    #     print(value)
    #     print("-" * 20)

    # To load the data from a CSV file (uncomment and adjust the file path):
    # df = pl.read_csv("data/titanic.csv")
    # partitioned_data = partition_dataframe(df)
    # print(partitioned_data)
    pass

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfwuh1jc3.py", line 31, in 
    partitioned_data = partition_dataframe(df)
                       ^^^^^^^^^^^^^^^^^^^^^^^
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfwuh1jc3.py", line 9, in partition_dataframe
    return df.group_by(["Survived", "Pclass"], maintain_order=True).to_dict(as_series=False)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'GroupBy' object has no attribute 'to_dict'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def partition_dataframe(df: pl.DataFrame, partition_cols: list) -> dict:
    """
    Partitions a Polars DataFrame by the specified columns.

    Args:
        df: The Polars DataFrame to partition.
        partition_cols: A list of column names to partition by.

    Returns:
        A dictionary where keys are tuples representing the unique combinations of values
        in the partition_cols, and values are the corresponding DataFrames.
    """
    partitions = {}
    unique_combinations = df.select(partition_cols).unique().to_dicts()

    for combination in unique_combinations:
        filter_condition = None
        for col in partition_cols:
            if filter_condition is None:
                filter_condition = pl.col(col) == combination[col]
            else:
                filter_condition = filter_condition & (pl.col(col) == combination[col])

        partition_df = df.filter(filter_condition)
        partition_key = tuple(combination.values())
        partitions[partition_key] = partition_df

    return partitions

if __name__ == '__main__':
    # Example Usage (replace with your actual data loading)
    try:
        df = pl.read_csv("data/titanic.csv")
    except FileNotFoundError:
        # Create a dummy DataFrame if the file is not found.
        data = {
            'PassengerId': [1, 2, 3, 4, 5],
            'Survived': [0, 1, 0, 1, 0],
            'Pclass': [3, 1, 3, 1, 3],
            'Name': ['A', 'B', 'C', 'D', 'E'],
            'Sex': ['male', 'female', 'male', 'female', 'male'],
            'Age': [22.0, 38.0, 26.0, 35.0, 28.0],
            'SibSp': [1, 1, 0, 1, 0],
            'Parch': [0, 0, 0, 0, 0],
            'Ticket': ['A1', 'A2', 'A3', 'A4', 'A5'],
            'Fare': [7.25, 71.2833, 7.925, 53.1, 8.4583],
            'Cabin': [None, 'C85', None, 'C123', None],
            'Embarked': ['S', 'C', 'S', 'S', 'Q']
        }
        df = pl.DataFrame(data)

    partition_cols = ["Survived", "Pclass"]
    partitioned_dataframes = partition_dataframe(df, partition_cols)

    # Print the keys and first few rows of each partition to demonstrate
    for key, part_df in partitioned_dataframes.items():
        print(f"Partition Key: {key}")
        print(part_df.head(3))
        print("-" * 20)

Output:

Partition Key: (0, 3)
shape: (2, 12)
┌─────────────┬──────────┬────────┬───────┬───┬───────────┬──────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name  ┆ … ┆ Ticket    ┆ Fare ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---   ┆   ┆ ---       ┆ ---  ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str   ┆   ┆ str       ┆ f64  ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪═══════╪═══╪═══════════╪══════╪═══════╪══════════╡
│ 1           ┆ 0        ┆ 3      ┆ test1 ┆ … ┆ A/5 21171 ┆ 7.25 ┆ NaN   ┆ S        │
│ 5           ┆ 0        ┆ 3      ┆ test5 ┆ … ┆ A/5. 2151 ┆ null ┆ NaN   ┆ S        │
└─────────────┴──────────┴────────┴───────┴───┴───────────┴──────┴───────┴──────────┘
--------------------
Partition Key: (1, 1)
shape: (2, 12)
┌─────────────┬──────────┬────────┬───────┬───┬──────────┬───────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name  ┆ … ┆ Ticket   ┆ Fare  ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---   ┆   ┆ ---      ┆ ---   ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str   ┆   ┆ str      ┆ f64   ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪═══════╪═══╪══════════╪═══════╪═══════╪══════════╡
│ 2           ┆ 1        ┆ 1      ┆ test2 ┆ … ┆ PC 17599 ┆ 71.28 ┆ C85   ┆ C        │
│ 4           ┆ 1        ┆ 1      ┆ test4 ┆ … ┆ 113803   ┆ 53.1  ┆ C123  ┆ S        │
└─────────────┴──────────┴────────┴───────┴───┴──────────┴───────┴───────┴──────────┘
--------------------
Partition Key: (0, 2)
shape: (1, 12)
┌─────────────┬──────────┬────────┬───────┬───┬──────────────────┬──────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name  ┆ … ┆ Ticket           ┆ Fare ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---   ┆   ┆ ---              ┆ ---  ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str   ┆   ┆ str              ┆ f64  ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪═══════╪═══╪══════════════════╪══════╪═══════╪══════════╡
│ 3           ┆ 0        ┆ 2      ┆ test3 ┆ … ┆ STON/O2. 3101282 ┆ 7.92 ┆ NaN   ┆ S        │
└─────────────┴──────────┴────────┴───────┴───┴──────────────────┴──────┴───────┴──────────┘
--------------------

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def partition_dataframe(df: pl.DataFrame) -> dict[tuple[int, int], pl.DataFrame]:
    """
    Partitions a Polars DataFrame by Survived and Pclass columns.

    Args:
        df: The input Polars DataFrame.

    Returns:
        A dictionary of DataFrames, where the keys are tuples of (Survived, Pclass)
        and the values are the corresponding DataFrames.
    """
    return df.group_by(["Survived", "Pclass"]).nest()

if __name__ == '__main__':
    df = pl.read_csv("data/titanic.csv")
    partitioned_df = partition_dataframe(df)
    
    # Example usage: access the DataFrame where Survived=1 and Pclass=1
    if (1, 1) in partitioned_df:
        print(partitioned_df[(1, 1)].head())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpttqsdo3t.py", line 20, in 
    partitioned_df = partition_dataframe(df)
                     ^^^^^^^^^^^^^^^^^^^^^^^
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpttqsdo3t.py", line 16, in partition_dataframe
    return df.group_by(["Survived", "Pclass"]).nest()
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'GroupBy' object has no attribute 'nest'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

def partition_dataframe(df: pl.DataFrame) -> dict[str, pl.DataFrame]:
    """
    Partition the Polars DataFrame by Survived and Pclass columns, returning a dictionary of DataFrames.
    """
    return df.partition_by(["Survived", "Pclass"], as_dict=True)

if __name__ == '__main__':
    partitioned_df = partition_dataframe(df)
    # You can now access each partition using the keys (tuples of Survived and Pclass values)
    # For example:
    # print(partitioned_df[(0, 1)]) # Access the partition where Survived is 0 and Pclass is 1

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def partition_dataframe(df: pl.DataFrame) -> dict[pl.DataFrame]:
    """
    Partition the Polars DataFrame by Survived and Pclass columns,
    returning a dictionary of DataFrames.
    """
    partitioned_df = df.group_by(["Survived", "Pclass"])
    return partitioned_df.into_dfs()

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
partitions = df.partition_by(["Survived", "Pclass"], as_dict=True)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
partitions = df.partition_by(["Survived", "Pclass"], as_dict=True)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
partitioned = {f"Survived_{survived}_Pclass_{pclass}": df.filter((pl.col("Survived") == survived) & (pl.col("Pclass") == pclass)) 
               for survived in df["Survived"].unique() 
               for pclass in df["Pclass"].unique()}

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

def partition_by_survived_pclass(df):
    # Create a dictionary to store the partitioned DataFrames
    partitioned_dfs = {}
    
    # Get unique values for Survived and Pclass
    survived_values = df["Survived"].unique().to_list()
    pclass_values = df["Pclass"].unique().to_list()
    
    # Create partitions for each combination of Survived and Pclass
    for survived in survived_values:
        for pclass in pclass_values:
            # Filter the DataFrame
            partition = df.filter((pl.col("Survived") == survived) & (pl.col("Pclass") == pclass))
            # Use tuple as key for the dictionary
            key = (survived, pclass)
            partitioned_dfs[key] = partition
    
    return partitioned_dfs

# Call the function to partition the DataFrame
partitioned_dataframes = partition_by_survived_pclass(df)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Partition the DataFrame by Survived and Pclass
partitioned_data = df.partition_by(["Survived", "Pclass"], as_dict=True)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Partition the DataFrame by Survived and Pclass
partitions = {}
unique_survived = df["Survived"].unique().to_list()
unique_pclass = df["Pclass"].unique().to_list()

for survived in unique_survived:
    for pclass in unique_pclass:
        key = (survived, pclass)
        partitions[key] = df.filter((pl.col("Survived") == survived) & (pl.col("Pclass") == pclass))

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
def partition_dataframe(df):
    groups = df.partition_by(by=['Survived', 'Pclass'])
    partition_dict = {f"Survived_{g['Survived'][0]}_Pclass_{g['Pclass'][0]}": g for g in groups}
    return partition_dict

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = partition_dataframe(df)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
partitioned_df = df.partition_by(["Survived", "Pclass"], as_dict=True)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Group by Survived and Pclass and collect separate DataFrames into dictionary
partitioned_dfs = {
    f"survived_{s}_class_{p}": df.filter((pl.col("Survived") == s) & (pl.col("Pclass") == p))
    for s in df["Survived"].unique()
    for p in df["Pclass"].unique()
}

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
partitioned_df = {}
for key, group in df.groupby(["Survived", "Pclass"]):
    partitioned_df[key] = group

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfqrmd8sg.py", line 4, in 
    for key, group in df.groupby(["Survived", "Pclass"]):
                      ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
partitioned_df = df.partition_by(["Survived", "Pclass"], as_dict=True)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

partitioned_dfs = {
    (survived, pclass): group_df
    for (survived, pclass), group_df in df.partition_by(["Survived", "Pclass"])
}

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdqwn6oq3.py", line 9, in 
    for (survived, pclass), group_df in df.partition_by(["Survived", "Pclass"])
        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: too many values to unpack (expected 2)

Status: ✗ Failed

pivot_table

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Create a pivot table that shows:
# - Pclass as rows
# - Sex as columns
# - Average age as values
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of creating a pivot table:
# df.pivot(values="value", index="row_var", on="col_var", aggregate_function="mean")

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

pivot_table = df.pivot(
    values="Age",
    index="Pclass",
    columns="Sex",
    aggregate_function="mean"
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdv65tr6c.py:6: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.
  pivot_table = df.pivot(

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.pivot(
    values="Age",
    index="Pclass",
    columns="Sex",
    aggregate_function="mean"
).sort("Pclass")

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpav7fx1q_.py:3: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.
  result = df.pivot(

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.collect().pivot(
    values="Age",
    index="Pclass", 
    columns="Sex",
    aggregate_function="mean"
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp81je4qxp.py", line 6, in 
    df.collect().pivot(
    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'collect'

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
pivot_table = df.pivot.pivot(values="Age", index="Pclass", columns="Sex", agg_fn="mean")
print(pivot_table)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmprfjwr3uz.py", line 6, in 
    pivot_table = df.pivot.pivot(values="Age", index="Pclass", columns="Sex", agg_fn="mean")
                  ^^^^^^^^^^^^^^
AttributeError: 'function' object has no attribute 'pivot'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
pivot_table = df.pivot(values="Age", index="Pclass", columns="Sex", aggregate_fn="mean")

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmppi86d8s0.py:6: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.
  pivot_table = df.pivot(values="Age", index="Pclass", columns="Sex", aggregate_fn="mean")
Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmppi86d8s0.py", line 6, in 
    pivot_table = df.pivot(values="Age", index="Pclass", columns="Sex", aggregate_fn="mean")
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: DataFrame.pivot() got an unexpected keyword argument 'aggregate_fn'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code for the task:

import polars as pl

df = pl.read_csv("data/titanic.csv")

pivot_df = df.pivot(
    values="Age",
    index="Pclass",
    columns="Sex",
    aggregate_fn="mean"
)

print(pivot_df)

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
pivot_table = df.pivot(
    values="Age",
    index="Pclass",
    columns="Sex",
    aggregate_function="mean"
)
print(pivot_table)

Output:

shape: (3, 3)
┌────────┬───────────┬───────────┐
│ Pclass ┆ male      ┆ female    │
│ ---    ┆ ---       ┆ ---       │
│ i64    ┆ f64       ┆ f64       │
╞════════╪═══════════╪═══════════╡
│ 3      ┆ 26.507589 ┆ 21.75     │
│ 1      ┆ 41.281386 ┆ 34.611765 │
│ 2      ┆ 30.740707 ┆ 28.722973 │
└────────┴───────────┴───────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1bzdk_ty.py:6: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.
  pivot_table = df.pivot(

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
pivot_table = df.pivot(values="Age", index="Pclass", columns="Sex", aggregate_function="mean")

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpiu5mncjt.py:5: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.
  pivot_table = df.pivot(values="Age", index="Pclass", columns="Sex", aggregate_function="mean")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.pivot(
    values="Age",
    index="Pclass",
    columns="Sex",
    aggregate_function="mean"
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpqawa7ue5.py:6: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.
  df.pivot(

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
pivot_table = df.pivot_table(
    values="Age",
    index="Pclass",
    columns="Sex",
    aggfunc="mean"
)
print(pivot_table)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwr2954oq.py", line 3, in 
    pivot_table = df.pivot_table(
                  ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'pivot_table'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
pivot_table = df.pivot(values="Age", index="Pclass", columns="Sex", aggregate_function="mean")

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmprpwws3zp.py:3: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.
  pivot_table = df.pivot(values="Age", index="Pclass", columns="Sex", aggregate_function="mean")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

pivot_table = df.pivot(
    values="Age",
    index="Pclass",
    columns="Sex",
    aggregate_function="mean"
)
pivot_table

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8njbl4yt.py:7: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.
  pivot_table = df.pivot(

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

pivot_table = df.pivot(
    values="Age",
    index="Pclass",
    columns="Sex",
    aggregate_function="mean",
)

print(pivot_table)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfqd4ugnz.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

pivot_df = df.pivot(values="Age", index="Pclass", columns="Sex", aggregate_function="mean")

print(pivot_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwjlid9pb.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

pivot_df = df.group_by("Pclass").pivot(values="Age", index="Pclass", columns="Sex").mean()

print(pivot_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpi6t_ksxb.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

pivot_table = df.groupby(["Pclass", "Sex"]).agg(
    pl.col("Age").mean()
).pivot(index="Pclass", columns="Sex", values="Age")

print(pivot_table)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9hzgjc2x.py", line 7, in 
    pivot_table = df.groupby(["Pclass", "Sex"]).agg(
                  ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

pivot_table = df.groupby(["Pclass", "Sex"]).agg(
    pl.col("Age").mean()
).pivot(rows="Pclass", columns="Sex", values="Age")

print(pivot_table)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpag9gjn8k.py", line 7, in 
    pivot_table = df.groupby(["Pclass", "Sex"]).agg(
                  ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

pivot_table = df.pivot(
    values="Age",
    index="Pclass",
    on="Sex",
    aggregate_function="mean"
)

print(pivot_table)

Output:

shape: (3, 3)
┌────────┬───────────┬───────────┐
│ Pclass ┆ male      ┆ female    │
│ ---    ┆ ---       ┆ ---       │
│ i64    ┆ f64       ┆ f64       │
╞════════╪═══════════╪═══════════╡
│ 3      ┆ 26.507589 ┆ 21.75     │
│ 1      ┆ 41.281386 ┆ 34.611765 │
│ 2      ┆ 30.740707 ┆ 28.722973 │
└────────┴───────────┴───────────┘

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

pivot_table = df.pivot(
    index="Pclass",
    columns="Sex",
    values="Age",
    aggregate_function="mean"
)

print(pivot_table)

Output:

shape: (3, 3)
┌────────┬───────────┬───────────┐
│ Pclass ┆ male      ┆ female    │
│ ---    ┆ ---       ┆ ---       │
│ i64    ┆ f64       ┆ f64       │
╞════════╪═══════════╪═══════════╡
│ 3      ┆ 26.507589 ┆ 21.75     │
│ 1      ┆ 41.281386 ┆ 34.611765 │
│ 2      ┆ 30.740707 ┆ 28.722973 │
└────────┴───────────┴───────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfac8_md5.py:7: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.
  pivot_table = df.pivot(

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Create a pivot table with Pclass as rows, Sex as columns, and average Age as values
pivot_table = df.pivot(
    values="Age",
    index="Pclass", 
    columns="Sex", 
    aggregate_function="mean"
)

print(pivot_table)

Output:

shape: (3, 3)
┌────────┬───────────┬───────────┐
│ Pclass ┆ male      ┆ female    │
│ ---    ┆ ---       ┆ ---       │
│ i64    ┆ f64       ┆ f64       │
╞════════╪═══════════╪═══════════╡
│ 3      ┆ 26.507589 ┆ 21.75     │
│ 1      ┆ 41.281386 ┆ 34.611765 │
│ 2      ┆ 30.740707 ┆ 28.722973 │
└────────┴───────────┴───────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp564gcskn.py:8: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.
  pivot_table = df.pivot(

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

pivot_table = df.pivot(
    values="Age",
    index="Pclass",
    columns="Sex",
    aggregate_function="mean"
)

print(pivot_table)

Output:

shape: (3, 3)
┌────────┬───────────┬───────────┐
│ Pclass ┆ male      ┆ female    │
│ ---    ┆ ---       ┆ ---       │
│ i64    ┆ f64       ┆ f64       │
╞════════╪═══════════╪═══════════╡
│ 3      ┆ 26.507589 ┆ 21.75     │
│ 1      ┆ 41.281386 ┆ 34.611765 │
│ 2      ┆ 30.740707 ┆ 28.722973 │
└────────┴───────────┴───────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp97lycvul.py:7: DeprecationWarning: The argument `columns` for `DataFrame.pivot` is deprecated. It has been renamed to `on`.
  pivot_table = df.pivot(

Status: ✓ Success

regex_substring_match

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Find all passengers whose names contain "Mr." or "Mrs."
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of regex matching: df.filter(pl.col("text").str.contains(r"Mr\.|Mrs\."))

api/gemini-2.0-flash

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.filter(
    pl.col("Name").str.contains("Mr\.") | pl.col("Name").str.contains("Mrs\.")
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp6dxqx8vy.py:4: SyntaxWarning: invalid escape sequence '\.'
  pl.col("Name").str.contains("Mr\.") | pl.col("Name").str.contains("Mrs\.")
/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp6dxqx8vy.py:4: SyntaxWarning: invalid escape sequence '\.'
  pl.col("Name").str.contains("Mr\.") | pl.col("Name").str.contains("Mrs\.")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(pl.col("Name").str.contains(r"Mr\.|Mrs\."))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.filter(
    pl.col("Name").str.contains("Mr.") | pl.col("Name").str.contains("Mrs.")
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.filter(
    pl.col("Name").str.contains("Mr.") | pl.col("Name").str.contains("Mrs.")
)

print(result)

Output:

shape: (647, 12)
┌─────────────┬──────────┬────────┬──────────────────┬───┬────────────┬─────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name             ┆ … ┆ Ticket     ┆ Fare    ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---              ┆   ┆ ---        ┆ ---     ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str              ┆   ┆ str        ┆ f64     ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════════════════╪═══╪════════════╪═════════╪═══════╪══════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr. Owen ┆ … ┆ A/5 21171  ┆ 7.25    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Harris           ┆   ┆            ┆         ┆       ┆          │
│ 2           ┆ 1        ┆ 1      ┆ Cumings, Mrs.    ┆ … ┆ PC 17599   ┆ 71.2833 ┆ C85   ┆ C        │
│             ┆          ┆        ┆ John Bradley     ┆   ┆            ┆         ┆       ┆          │
│             ┆          ┆        ┆ (Fl…             ┆   ┆            ┆         ┆       ┆          │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle, Mrs.   ┆ … ┆ 113803     ┆ 53.1    ┆ C123  ┆ S        │
│             ┆          ┆        ┆ Jacques Heath (… ┆   ┆            ┆         ┆       ┆          │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr.       ┆ … ┆ 373450     ┆ 8.05    ┆ null  ┆ S        │
│             ┆          ┆        ┆ William Henry    ┆   ┆            ┆         ┆       ┆          │
│ 6           ┆ 0        ┆ 3      ┆ Moran, Mr. James ┆ … ┆ 330877     ┆ 8.4583  ┆ null  ┆ Q        │
│ …           ┆ …        ┆ …      ┆ …                ┆ … ┆ …          ┆ …       ┆ …     ┆ …        │
│ 884         ┆ 0        ┆ 2      ┆ Banfield, Mr.    ┆ … ┆ C.A./SOTON ┆ 10.5    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Frederick James  ┆   ┆ 34068      ┆         ┆       ┆          │
│ 885         ┆ 0        ┆ 3      ┆ Sutehall, Mr.    ┆ … ┆ SOTON/OQ   ┆ 7.05    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Henry Jr         ┆   ┆ 392076     ┆         ┆       ┆          │
│ 886         ┆ 0        ┆ 3      ┆ Rice, Mrs.       ┆ … ┆ 382652     ┆ 29.125  ┆ null  ┆ Q        │
│             ┆          ┆        ┆ William          ┆   ┆            ┆         ┆       ┆          │
│             ┆          ┆        ┆ (Margaret N…     ┆   ┆            ┆         ┆       ┆          │
│ 890         ┆ 1        ┆ 1      ┆ Behr, Mr. Karl   ┆ … ┆ 111369     ┆ 30.0    ┆ C148  ┆ C        │
│             ┆          ┆        ┆ Howell           ┆   ┆            ┆         ┆       ┆          │
│ 891         ┆ 0        ┆ 3      ┆ Dooley, Mr.      ┆ … ┆ 370376     ┆ 7.75    ┆ null  ┆ Q        │
│             ┆          ┆        ┆ Patrick          ┆   ┆            ┆         ┆       ┆          │
└─────────────┴──────────┴────────┴──────────────────┴───┴────────────┴─────────┴───────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.filter(pl.col("Name").str.contains(r"Mr\.|Mrs\."))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Find all passengers whose names contain "Mr." or "Mrs."
result = df.filter(
    pl.col("Name").str.contains("Mr.") | pl.col("Name").str.contains("Mrs.")
)

print(result)

Output:

shape: (647, 12)
┌─────────────┬──────────┬────────┬──────────────────┬───┬────────────┬─────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name             ┆ … ┆ Ticket     ┆ Fare    ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---              ┆   ┆ ---        ┆ ---     ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str              ┆   ┆ str        ┆ f64     ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════════════════╪═══╪════════════╪═════════╪═══════╪══════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr. Owen ┆ … ┆ A/5 21171  ┆ 7.25    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Harris           ┆   ┆            ┆         ┆       ┆          │
│ 2           ┆ 1        ┆ 1      ┆ Cumings, Mrs.    ┆ … ┆ PC 17599   ┆ 71.2833 ┆ C85   ┆ C        │
│             ┆          ┆        ┆ John Bradley     ┆   ┆            ┆         ┆       ┆          │
│             ┆          ┆        ┆ (Fl…             ┆   ┆            ┆         ┆       ┆          │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle, Mrs.   ┆ … ┆ 113803     ┆ 53.1    ┆ C123  ┆ S        │
│             ┆          ┆        ┆ Jacques Heath (… ┆   ┆            ┆         ┆       ┆          │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr.       ┆ … ┆ 373450     ┆ 8.05    ┆ null  ┆ S        │
│             ┆          ┆        ┆ William Henry    ┆   ┆            ┆         ┆       ┆          │
│ 6           ┆ 0        ┆ 3      ┆ Moran, Mr. James ┆ … ┆ 330877     ┆ 8.4583  ┆ null  ┆ Q        │
│ …           ┆ …        ┆ …      ┆ …                ┆ … ┆ …          ┆ …       ┆ …     ┆ …        │
│ 884         ┆ 0        ┆ 2      ┆ Banfield, Mr.    ┆ … ┆ C.A./SOTON ┆ 10.5    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Frederick James  ┆   ┆ 34068      ┆         ┆       ┆          │
│ 885         ┆ 0        ┆ 3      ┆ Sutehall, Mr.    ┆ … ┆ SOTON/OQ   ┆ 7.05    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Henry Jr         ┆   ┆ 392076     ┆         ┆       ┆          │
│ 886         ┆ 0        ┆ 3      ┆ Rice, Mrs.       ┆ … ┆ 382652     ┆ 29.125  ┆ null  ┆ Q        │
│             ┆          ┆        ┆ William          ┆   ┆            ┆         ┆       ┆          │
│             ┆          ┆        ┆ (Margaret N…     ┆   ┆            ┆         ┆       ┆          │
│ 890         ┆ 1        ┆ 1      ┆ Behr, Mr. Karl   ┆ … ┆ 111369     ┆ 30.0    ┆ C148  ┆ C        │
│             ┆          ┆        ┆ Howell           ┆   ┆            ┆         ┆       ┆          │
│ 891         ┆ 0        ┆ 3      ┆ Dooley, Mr.      ┆ … ┆ 370376     ┆ 7.75    ┆ null  ┆ Q        │
│             ┆          ┆        ┆ Patrick          ┆   ┆            ┆         ┆       ┆          │
└─────────────┴──────────┴────────┴──────────────────┴───┴────────────┴─────────┴───────┴──────────┘

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.filter(pl.col("Name").str.contains("Mr\.|Mrs\."))

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0fr5vxev.py:6: SyntaxWarning: invalid escape sequence '\.'
  result = df.filter(pl.col("Name").str.contains("Mr\.|Mrs\."))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.filter(pl.col("Name").str.contains(r"Mr\.|Mrs\."))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.filter(
    pl.col("Name").str.contains("Mr\.") | 
    pl.col("Name").str.contains("Mrs\.")
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpaq_4nqec.py:7: SyntaxWarning: invalid escape sequence '\.'
  pl.col("Name").str.contains("Mr\.") | 
/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpaq_4nqec.py:8: SyntaxWarning: invalid escape sequence '\.'
  pl.col("Name").str.contains("Mrs\.")

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Find passengers with "Mr." or "Mrs." in their names
mr_mrs_passengers = df.filter(
    pl.col("Name").str.contains("Mr\.") | pl.col("Name").str.contains("Mrs\.")
)

print(mr_mrs_passengers)

Output:

shape: (642, 12)
┌─────────────┬──────────┬────────┬──────────────────┬───┬────────────┬─────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name             ┆ … ┆ Ticket     ┆ Fare    ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---              ┆   ┆ ---        ┆ ---     ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str              ┆   ┆ str        ┆ f64     ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════════════════╪═══╪════════════╪═════════╪═══════╪══════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr. Owen ┆ … ┆ A/5 21171  ┆ 7.25    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Harris           ┆   ┆            ┆         ┆       ┆          │
│ 2           ┆ 1        ┆ 1      ┆ Cumings, Mrs.    ┆ … ┆ PC 17599   ┆ 71.2833 ┆ C85   ┆ C        │
│             ┆          ┆        ┆ John Bradley     ┆   ┆            ┆         ┆       ┆          │
│             ┆          ┆        ┆ (Fl…             ┆   ┆            ┆         ┆       ┆          │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle, Mrs.   ┆ … ┆ 113803     ┆ 53.1    ┆ C123  ┆ S        │
│             ┆          ┆        ┆ Jacques Heath (… ┆   ┆            ┆         ┆       ┆          │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr.       ┆ … ┆ 373450     ┆ 8.05    ┆ null  ┆ S        │
│             ┆          ┆        ┆ William Henry    ┆   ┆            ┆         ┆       ┆          │
│ 6           ┆ 0        ┆ 3      ┆ Moran, Mr. James ┆ … ┆ 330877     ┆ 8.4583  ┆ null  ┆ Q        │
│ …           ┆ …        ┆ …      ┆ …                ┆ … ┆ …          ┆ …       ┆ …     ┆ …        │
│ 884         ┆ 0        ┆ 2      ┆ Banfield, Mr.    ┆ … ┆ C.A./SOTON ┆ 10.5    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Frederick James  ┆   ┆ 34068      ┆         ┆       ┆          │
│ 885         ┆ 0        ┆ 3      ┆ Sutehall, Mr.    ┆ … ┆ SOTON/OQ   ┆ 7.05    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Henry Jr         ┆   ┆ 392076     ┆         ┆       ┆          │
│ 886         ┆ 0        ┆ 3      ┆ Rice, Mrs.       ┆ … ┆ 382652     ┆ 29.125  ┆ null  ┆ Q        │
│             ┆          ┆        ┆ William          ┆   ┆            ┆         ┆       ┆          │
│             ┆          ┆        ┆ (Margaret N…     ┆   ┆            ┆         ┆       ┆          │
│ 890         ┆ 1        ┆ 1      ┆ Behr, Mr. Karl   ┆ … ┆ 111369     ┆ 30.0    ┆ C148  ┆ C        │
│             ┆          ┆        ┆ Howell           ┆   ┆            ┆         ┆       ┆          │
│ 891         ┆ 0        ┆ 3      ┆ Dooley, Mr.      ┆ … ┆ 370376     ┆ 7.75    ┆ null  ┆ Q        │
│             ┆          ┆        ┆ Patrick          ┆   ┆            ┆         ┆       ┆          │
└─────────────┴──────────┴────────┴──────────────────┴───┴────────────┴─────────┴───────┴──────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpryyo8hqk.py:10: SyntaxWarning: invalid escape sequence '\.'
  pl.col("Name").str.contains("Mr\.") | pl.col("Name").str.contains("Mrs\.")
/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpryyo8hqk.py:10: SyntaxWarning: invalid escape sequence '\.'
  pl.col("Name").str.contains("Mr\.") | pl.col("Name").str.contains("Mrs\.")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Find all passengers whose names contain "Mr." or "Mrs."
filtered_df = df.filter(pl.col("Name").str.contains(r"Mr\.|Mrs\."))
print(filtered_df)

Output:

shape: (642, 12)
┌─────────────┬──────────┬────────┬──────────────────┬───┬────────────┬─────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name             ┆ … ┆ Ticket     ┆ Fare    ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---              ┆   ┆ ---        ┆ ---     ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str              ┆   ┆ str        ┆ f64     ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪══════════════════╪═══╪════════════╪═════════╪═══════╪══════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr. Owen ┆ … ┆ A/5 21171  ┆ 7.25    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Harris           ┆   ┆            ┆         ┆       ┆          │
│ 2           ┆ 1        ┆ 1      ┆ Cumings, Mrs.    ┆ … ┆ PC 17599   ┆ 71.2833 ┆ C85   ┆ C        │
│             ┆          ┆        ┆ John Bradley     ┆   ┆            ┆         ┆       ┆          │
│             ┆          ┆        ┆ (Fl…             ┆   ┆            ┆         ┆       ┆          │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle, Mrs.   ┆ … ┆ 113803     ┆ 53.1    ┆ C123  ┆ S        │
│             ┆          ┆        ┆ Jacques Heath (… ┆   ┆            ┆         ┆       ┆          │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr.       ┆ … ┆ 373450     ┆ 8.05    ┆ null  ┆ S        │
│             ┆          ┆        ┆ William Henry    ┆   ┆            ┆         ┆       ┆          │
│ 6           ┆ 0        ┆ 3      ┆ Moran, Mr. James ┆ … ┆ 330877     ┆ 8.4583  ┆ null  ┆ Q        │
│ …           ┆ …        ┆ …      ┆ …                ┆ … ┆ …          ┆ …       ┆ …     ┆ …        │
│ 884         ┆ 0        ┆ 2      ┆ Banfield, Mr.    ┆ … ┆ C.A./SOTON ┆ 10.5    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Frederick James  ┆   ┆ 34068      ┆         ┆       ┆          │
│ 885         ┆ 0        ┆ 3      ┆ Sutehall, Mr.    ┆ … ┆ SOTON/OQ   ┆ 7.05    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Henry Jr         ┆   ┆ 392076     ┆         ┆       ┆          │
│ 886         ┆ 0        ┆ 3      ┆ Rice, Mrs.       ┆ … ┆ 382652     ┆ 29.125  ┆ null  ┆ Q        │
│             ┆          ┆        ┆ William          ┆   ┆            ┆         ┆       ┆          │
│             ┆          ┆        ┆ (Margaret N…     ┆   ┆            ┆         ┆       ┆          │
│ 890         ┆ 1        ┆ 1      ┆ Behr, Mr. Karl   ┆ … ┆ 111369     ┆ 30.0    ┆ C148  ┆ C        │
│             ┆          ┆        ┆ Howell           ┆   ┆            ┆         ┆       ┆          │
│ 891         ┆ 0        ┆ 3      ┆ Dooley, Mr.      ┆ … ┆ 370376     ┆ 7.75    ┆ null  ┆ Q        │
│             ┆          ┆        ┆ Patrick          ┆   ┆            ┆         ┆       ┆          │
└─────────────┴──────────┴────────┴──────────────────┴───┴────────────┴─────────┴───────┴──────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Find all passengers whose names contain "Mr." or "Mrs."
result = df.filter(
    pl.col("Name").str.contains("Mr.") | pl.col("Name").str.contains("Mrs.")
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.filter(pl.col("Name").str.contains(r"Mr\.|Mrs\."))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
mr_mrs_passengers = df.filter(pl.col("Name").str.contains(r"Mr\.|Mrs\."))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.filter(pl.col("Name").str.contains("Mr.|Mrs."))

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.filter(df["Name"].str.contains("Mr.") | df["Name"].str.contains("Mrs."))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
# Find all passengers whose names contain "Mr." or "Mrs."
result = df.filter(pl.col("Name").str.contains(r"Mr\.|Mrs\."))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter(
    pl.col("Name").str.contains("Mr.") | pl.col("Name").str.contains("Mrs.")
)

Status: ✓ Success

rolling_mean_window

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Calculate a rolling mean of the Fare column with a window size of 4 rows
# Use a centered window (2 rows before and 1 row after)
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of calculating a rolling mean:
# df.with_columns(pl.col("value").rolling_mean(window_size=4, center=True).alias("rolling_mean"))

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Fare").rolling_mean(window_size=4, center=True, min_periods=1).alias("rolling_mean_fare")
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpxawj8w2m.py:7: DeprecationWarning: The argument `min_periods` for `Expr.rolling_mean` is deprecated. It has been renamed to `min_samples`.
  pl.col("Fare").rolling_mean(window_size=4, center=True, min_periods=1).alias("rolling_mean_fare")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.with_columns(pl.col("Fare").rolling_mean(window_size=4, center=True).alias("rolling_mean"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.with_columns(
    pl.col("Fare").rolling_mean(window_size=4, center=True, min_periods=1).alias("rolling_mean_fare")
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1vbvnrcc.py:7: DeprecationWarning: The argument `min_periods` for `Expr.rolling_mean` is deprecated. It has been renamed to `min_samples`.
  pl.col("Fare").rolling_mean(window_size=4, center=True, min_periods=1).alias("rolling_mean_fare")

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df_with_rolling = df.with_columns(
    pl.col("Fare").rolling_mean(
        window_size=4,
        center=True,
        min_periods=1
    ).alias("Fare_rolling_mean")
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpazlregac.py:7: DeprecationWarning: The argument `min_periods` for `Expr.rolling_mean` is deprecated. It has been renamed to `min_samples`.
  pl.col("Fare").rolling_mean(

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").rolling_mean(
        window_size=4,
        center=True
    ).alias("Fare_Rolling_Mean")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.with_columns(
    pl.col("Fare").rolling_mean(
        window_size=4,
        center=True,
        min_periods=1
    ).alias("Fare_Rolling_Mean")
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmplnrujtok.py:8: DeprecationWarning: The argument `min_periods` for `Expr.rolling_mean` is deprecated. It has been renamed to `min_samples`.
  pl.col("Fare").rolling_mean(

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.col("Fare").rolling_mean(window_size=4, center=True).alias("Rolling_Mean_Fare"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_fzqxxqe.py", line 7, in 
    df = df.with_column(pl.col("Fare").rolling_mean(window_size=4, center=True).alias("Rolling_Mean_Fare"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df.with_columns(pl.col("Fare").rolling_mean(window_size=4, center=True).alias("rolling_mean"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").rolling_mean(window_size=4, window_forward=1, window_backward=2).alias("Rolling Mean"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpoz5swqt1.py", line 6, in 
    df = df.with_columns(pl.col("Fare").rolling_mean(window_size=4, window_forward=1, window_backward=2).alias("Rolling Mean"))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Expr.rolling_mean() got an unexpected keyword argument 'window_forward'

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").rolling_mean(window_size=4, center=True).alias("Fare_rolling_mean")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").rolling_mean(window_size=4, center=True).alias("rolling_mean_fare")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.with_columns(
    pl.col("Fare").rolling_mean(window_size=4, center=True).alias("rolling_mean_fare")
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    pl.col("Fare").rolling_mean(window_size=4, center=True).alias("Fare_rolling_mean")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Fare").rolling_mean(window_size=4, center=True).alias("rolling_mean_fare"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Calculate a rolling mean of the Fare column with a window size of 4 rows.
# Use a centered window (2 rows before and 1 row after).
df = df.with_columns(
    pl.col("Fare").rolling_mean(window_size=4, center=True).alias("Fare_rolling_mean")
)

print(df)

Output:

shape: (891, 13)
┌─────────────┬──────────┬────────┬───────────────┬───┬─────────┬───────┬──────────┬───────────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name          ┆ … ┆ Fare    ┆ Cabin ┆ Embarked ┆ Fare_rolling_ │
│ ---         ┆ ---      ┆ ---    ┆ ---           ┆   ┆ ---     ┆ ---   ┆ ---      ┆ mean          │
│ i64         ┆ i64      ┆ i64    ┆ str           ┆   ┆ f64     ┆ str   ┆ str      ┆ ---           │
│             ┆          ┆        ┆               ┆   ┆         ┆       ┆          ┆ f64           │
╞═════════════╪══════════╪════════╪═══════════════╪═══╪═════════╪═══════╪══════════╪═══════════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr.   ┆ … ┆ 7.25    ┆ null  ┆ S        ┆ null          │
│             ┆          ┆        ┆ Owen Harris   ┆   ┆         ┆       ┆          ┆               │
│ 2           ┆ 1        ┆ 1      ┆ Cumings, Mrs. ┆ … ┆ 71.2833 ┆ C85   ┆ C        ┆ null          │
│             ┆          ┆        ┆ John Bradley  ┆   ┆         ┆       ┆          ┆               │
│             ┆          ┆        ┆ (Fl…          ┆   ┆         ┆       ┆          ┆               │
│ 3           ┆ 1        ┆ 3      ┆ Heikkinen,    ┆ … ┆ 7.925   ┆ null  ┆ S        ┆ 34.889575     │
│             ┆          ┆        ┆ Miss. Laina   ┆   ┆         ┆       ┆          ┆               │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle,     ┆ … ┆ 53.1    ┆ C123  ┆ S        ┆ 35.089575     │
│             ┆          ┆        ┆ Mrs. Jacques  ┆   ┆         ┆       ┆          ┆               │
│             ┆          ┆        ┆ Heath (…      ┆   ┆         ┆       ┆          ┆               │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr.    ┆ … ┆ 8.05    ┆ null  ┆ S        ┆ 19.383325     │
│             ┆          ┆        ┆ William Henry ┆   ┆         ┆       ┆          ┆               │
│ …           ┆ …        ┆ …      ┆ …             ┆ … ┆ …       ┆ …     ┆ …        ┆ …             │
│ 887         ┆ 0        ┆ 2      ┆ Montvila,     ┆ … ┆ 13.0    ┆ null  ┆ S        ┆ 19.79375      │
│             ┆          ┆        ┆ Rev. Juozas   ┆   ┆         ┆       ┆          ┆               │
│ 888         ┆ 1        ┆ 1      ┆ Graham, Miss. ┆ … ┆ 30.0    ┆ B42   ┆ S        ┆ 23.89375      │
│             ┆          ┆        ┆ Margaret      ┆   ┆         ┆       ┆          ┆               │
│             ┆          ┆        ┆ Edith         ┆   ┆         ┆       ┆          ┆               │
│ 889         ┆ 0        ┆ 3      ┆ Johnston,     ┆ … ┆ 23.45   ┆ null  ┆ S        ┆ 24.1125       │
│             ┆          ┆        ┆ Miss.         ┆   ┆         ┆       ┆          ┆               │
│             ┆          ┆        ┆ Catherine     ┆   ┆         ┆       ┆          ┆               │
│             ┆          ┆        ┆ Hele…         ┆   ┆         ┆       ┆          ┆               │
│ 890         ┆ 1        ┆ 1      ┆ Behr, Mr.     ┆ … ┆ 30.0    ┆ C148  ┆ C        ┆ 22.8          │
│             ┆          ┆        ┆ Karl Howell   ┆   ┆         ┆       ┆          ┆               │
│ 891         ┆ 0        ┆ 3      ┆ Dooley, Mr.   ┆ … ┆ 7.75    ┆ null  ┆ Q        ┆ null          │
│             ┆          ┆        ┆ Patrick       ┆   ┆         ┆       ┆          ┆               │
└─────────────┴──────────┴────────┴───────────────┴───┴─────────┴───────┴──────────┴───────────────┘

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").rolling_mean(window_size=4, center=True).alias("fare_rolling_mean")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpsxt5ike0.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").rolling_mean(window_size=4, center=True).alias("fare_rolling_mean")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmplwk76oc_.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

# Creating DataFrames
df = pl.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
# df = pl.read_csv("file.csv",try_parse_dates=True)
# Selection
df.select("a", "b")            # Select columns
df.select(pl.all())                # Select all columns
# Filtering
df = pl.DataFrame({"value": [11, 2, 13, 1, None], "category": ["A", "B", "A", "C", "A"]})
df.filter(pl.col("value") > 10)            # Basic comparison
df.filter(pl.col("category").is_in(["A", "B"]))    # Is in list
df.filter(pl.col("value").is_between(10, 20))     # Range check
df.filter(pl.col("value").is_null())          # Check for nulls
# AND and OR conditions
df = pl.DataFrame({"a": [6, 2, 1, 7], "b": [5, 11, 9, 3]})
df.filter((pl.col("a") > 5) & (pl.col("b") < 10))   # AND condition
df.filter((pl.col("a") > 5) | (pl.col("b") < 10))   # OR condition
df.filter(~(pl.col("a") > 5))             # NOT condition
# Expression Arithmetic
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
df.with_columns(
(pl.col("a") + pl.col("b")).alias("sum"),     # Addition
(pl.col("a") - pl.col("b")).alias("diff"),     # Subtraction
(pl.col("a") * pl.col("b")).alias("product"),   # Multiplication
(pl.col("a") / pl.col("b")).alias("division")   # Division
)
# Column Operations
df = pl.DataFrame({"a": [1, 2, 3]})
df.with_columns(pl.lit("constant").alias("new_col")) # Add constant column
df.rename({"a": "new_name"})          # Rename columns
df.drop("new_name")             # Drop columns
# Sorting
df = pl.DataFrame({"column": [3, 1, 2], "col1": [1, 2, 3], "col2": [3, 2, 1]})
df.sort("column", descending=True)          # Sort by column
df.sort(["col1", "col2"], descending=[True, False])  # Multi-column sort
# Aggregation and Grouping
df = pl.DataFrame({"value": [1, 2, 3, 4, 5], "category": ["A", "B", "A", "B", "A"]})
df.select(pl.col("value").sum())           # Sum values
df.select(pl.col("value").mean())           # Calculate mean
df.select(pl.col("value").len())          # Count values
df.select(pl.col("value").n_unique())         # Count unique values
# GroupBy operations
df.group_by("category").agg(pl.col("value").mean())  # Group and aggregate
df.group_by("category").agg(        # Group and aggregate
pl.col("value").mean().name.suffix("_mean"),
pl.col("value").min().name.suffix("_min"))
df = pl.DataFrame({"cat1": ["A", "A", "B", "B"], "cat2": ["X", "Y", "X", "Y"], "val1": [1, 2, 3, 4], "val2": [5, 6, 7, 8]})
df.group_by(["cat1", "cat2"]).agg(          # Multiple aggregations
pl.col("val1").sum(),
pl.col("val2").mean()
)
# Reshaping Data
df = pl.DataFrame({"row_var": ["A", "B", "A", "B"], "col_var": ["X", "X", "Y", "Y"], "value": [1, 2, 3, 4]})
df.pivot(values="value", index="row_var", on="col_var") # Pivot table
df = pl.DataFrame({"row_var": ["A", "B", "A", "B"], "col_var": ["X", "X", "Y", "Y"], "value": [1, 2, 3, 4]})
df.collect().pivot(values="value", index="row_var", on="col_var", aggregate_function="mean") # Pivot lazyframe
df = pl.DataFrame({"id": [1, 2, 3], "val1": [4, 5, 6], "val2": [7, 8, 9]})
df.unpivot(index=["id"], on=["val1", "val2"])     # Unpivot/melt
df = pl.DataFrame({"list_column": [[1, 2], [3, 4, 5], [6]]})
df.explode("list_column")                   # Explode lists
df = pl.DataFrame({"a": [1, 2, 3]})
df.with_row_index("index")                  # Add row indices
# String Operations
df = pl.DataFrame({"text": ["  hello  ", "world", "POLARS"]})
df.select(pl.col("text").str.len_chars())           # String length
df.select(pl.col("text").str.contains("pattern"))       # Contains pattern
df.select(pl.col("text").str.replace("old", "new"))      # Replace text
df.select(pl.col("text").str.to_lowercase())         # Convert case
df.select(pl.col("text").str.strip())             # Remove whitespace
df.select(pl.col("text").str.split(" "))           # Split strings
# Window Functions
df = pl.DataFrame({"value": [1, 2, 3, 4, 5], "group": ["A", "A", "B", "B", "A"]})
df.with_columns(
pl.col("value").sum().over("group")            # Sum by group
)
# Working with Missing Values
df = pl.DataFrame({"value": [1, 2, None, 4, None], "column1": [1, None, 3, 4, 5], "column2": [None, 2, 3, 4, 5]})
df.drop_nulls()                        # Drop null rows
df.drop_nulls(subset=["column1", "column2"])         # Drop for specific columns
df.fill_null(strategy="forward")               # Forward fill
df = pl.DataFrame({"value": [1, 2, None, 4, None]})
df.with_columns(
pl.col("value").fill_null(pl.col("value").mean())     # Fill with mean
)
df = pl.DataFrame({"value": [1, 2, None, 4, None]})
df.with_columns(
pl.when(pl.col("value").is_null())
.then(pl.lit(0))
.otherwise(pl.col("value"))
.alias("value")                      # Replace nulls conditionally
)
# Data Type Operations
df = pl.DataFrame({"a": [1, 2, 3], "date": [datetime(2023, 1, 1), datetime(2023, 2, 1), datetime(2023, 3, 1)]})
df.select(pl.col("a").cast(pl.Int64))             # Cast to type
df.select(pl.col("date").dt.year())              # Extract year
df.select(pl.col("date").dt.month())             # Extract month
df.select(pl.col("date").dt.strftime("%Y-%m-%d"))       # Format date
df.schema                           # Get schema
# Advanced Features
# LazyFrame for optimized execution
df = pl.DataFrame({"a": [1, -2, 3]})
lazy_df = df.lazy()
result = lazy_df.filter(pl.col("a") > 0).collect()
# Joins
df1 = pl.DataFrame({"key": [1, 2, 3], "value1": [4, 5, 6]})
df2 = pl.DataFrame({"key": [2, 3, 4], "value2": [7, 8, 9]})
df1.join(df2, on="key", how="inner")             # Inner join
df1.join(df2, on="key", how="left")              # Left join
df1 = pl.DataFrame({"key1": [1, 2, 3], "key2": ["A", "B", "C"], "value1": [4, 5, 6]})
df2 = pl.DataFrame({"key1": [2, 3, 4], "key2": ["B", "C", "D"], "value2": [7, 8, 9]})
df1.join(df2, on=["key1", "key2"], how="full")        # Multiple join keys
# Sampling
df = pl.DataFrame({"a": range(20)})
df.sample(n=10)                        # Sample n rows
df.sample(frac=0.1, with_replacement=True)          # Sample fraction
# User-defined functions
df = pl.DataFrame({"a": [1, 2, 3]})
df.select(pl.col("a").map_elements(lambda x: x * 2,return_dtype=pl.Int64))     # Apply function
# Efficient operations
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "column_to_exclude": [7, 8, 9]})
df.select([
pl.all(),                       # All columns
pl.exclude("column_to_exclude")             # Exclude column
])
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
df.with_columns(pl.exclude("a").name.prefix("new_"))   # Batch operations
# Expression contexts
df = pl.DataFrame({"a": [1, -2, 3]})
(
df.lazy()
.filter(pl.col("a") > 0)
.with_columns(pl.lit(1).alias("constant"))
.collect()
)                              # Method chaining
# Date and Time Operations
df = pl.DataFrame({"date": [datetime(2023, 1, 1, 10, 30, 0), datetime(2023, 2, 1, 12, 0, 0)]})
df.with_columns(
pl.col("date").dt.date()                 # Extract date
)
df.with_columns(
pl.col("date").dt.time()                 # Extract time
)
pl.date_range(datetime(2023, 1, 1), datetime(2023, 3, 31), interval="1d", eager=True) # Create a date range Series

df = pl.read_csv("data/titanic.csv")

result = df.with_columns(
    pl.col("Fare").rolling_mean(window_size=4, center=True).alias("rolling_mean_fare")
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmph04bpjnt.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Calculate rolling mean of Fare with window size 4 (2 before, current, 1 after)
df_with_rolling = df.with_columns(
    pl.col("Fare").rolling_mean(window_size=4, center=True).alias("Fare_Rolling_Mean")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df_with_rolling = df.with_columns(
    pl.col("Fare").rolling_mean(window_size=4, center=True).alias("Fare_Rolling_Mean")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Calculate rolling mean of Fare with window size of 4, centered
df_with_rolling_mean = df.with_columns(
    pl.col("Fare").rolling_mean(
        window_size=4,
        center=True,
        min_periods=1
    ).alias("Fare_Rolling_Mean")
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpnd3z5_2d.py:10: DeprecationWarning: The argument `min_periods` for `Expr.rolling_mean` is deprecated. It has been renamed to `min_samples`.
  pl.col("Fare").rolling_mean(

Status: ✓ Success

select_and_transform

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the Fare column and round it to 0 decimal places
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars syntax: df.select("id","value").head(3)

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.col("Fare").round(0))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.col("Fare").round(0))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select(pl.col("Fare").round(0))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").round(0)
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Fare").round(0))

print(df.select("Fare").head(3))

Output:

shape: (3, 1)
┌──────┐
│ Fare │
│ ---  │
│ f64  │
╞══════╡
│ 7.0  │
│ 71.0 │
│ 8.0  │
└──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.select(pl.col("Fare").round(0))

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    pl.col("Fare").round(0).alias("Fare")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
rounded_fare = df.select(pl.col("Fare").round(0))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").round(0).alias("RoundedFare"))

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
rounded_fare = df.select(pl.col("Fare").round(0))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

rounded_fare = df.select(pl.col("Fare").round(0).alias("Rounded_Fare"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

rounded_fares = df.select(pl.col("Fare").round(0))

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.select(pl.col("Fare").round(0))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.select(pl.col("Fare").round(0))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.select(pl.col("Fare").round(0))

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
rounded_fares = df.select(pl.col("Fare").round(0))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select(pl.col("Fare").round(0))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.col("Fare").round(0))

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
rounded_fare = df.select(pl.col("Fare").round(0))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.col("Fare").round(0))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.select(pl.col("Fare").round(0).alias("Fare"))

Status: ✓ Success

select_by_dtype

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select only the string columns from the DataFrame
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of selecting by dtype: df.select(pl.col(pl.Int64))

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
string_columns = df.select(pl.col(pl.Utf8))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.col(pl.Utf8))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

string_columns = [col for col in df.columns if df[col].dtype == pl.Utf8]
df.select(string_columns)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_strings = df.select(pl.col(str).filter(lambda x: x.dtype == pl.Utf8))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpkkll4h6j.py", line 3, in 
    df_strings = df.select(pl.col(str).filter(lambda x: x.dtype == pl.Utf8))
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/expr/expr.py", line 4266, in filter
    predicate = parse_predicates_constraints_into_expression(
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/parse/expr.py", line 164, in parse_predicates_constraints_into_expression
    all_predicates = _parse_positional_inputs(predicates)  # type: ignore[arg-type]
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/parse/expr.py", line 114, in _parse_positional_inputs
    return [parse_into_expression(e, structify=structify) for e in inputs_iter]
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/parse/expr.py", line 61, in parse_into_expression
    expr = F.lit(input, dtype=dtype)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/functions/lit.py", line 193, in lit
    return wrap_expr(plr.lit(item, allow_object, is_scalar=True))
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: cannot create expression literal for value of type function.

Hint: Pass `allow_object=True` to accept any value and create a literal of type Object.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
string_cols = df.select(pl.col(pl.Utf8))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select(pl.col(['Name', 'Sex', 'Ticket', 'Cabin', 'Embarked']))

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

string_cols = [col for col in df.columns if df[col].dtype == pl.Utf8]

string_df = df.select(string_cols)

print(string_df)

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

string_cols = [col for col in df.columns if df[col].dtype == pl.Utf8]

df_string = df.select(string_cols)

print(df_string)

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

string_columns = ["Name", "Sex", "Ticket", "Cabin", "Embarked"]
df_string = df.select(string_columns)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

string_cols = [
    "Name",
    "Sex",
    "Ticket",
    "Cabin",
    "Embarked",
]

df_string = df.select(string_cols)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

string_columns = [
    "Name",
    "Sex",
    "Ticket",
    "Cabin",
    "Embarked",
]

df_string = df.select(string_columns)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
string_cols = df.select(pl.col(pl.Utf8))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
string_columns_df = df.select(pl.col(pl.Utf8))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

string_columns = df.select(pl.col(pl.Utf8))

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Select only string columns
string_cols = df.select(df.select_dtypes(pl.Utf8).columns)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpkd36udp_.py", line 8, in 
    string_cols = df.select(df.select_dtypes(pl.Utf8).columns)
                            ^^^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'select_dtypes'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Select only the string columns from the DataFrame
string_columns = df.select(pl.col(pl.Utf8))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select only the string columns from the DataFrame
string_cols = df.select(pl.col(pl.Utf8))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
string_cols = df.select(pl.col(pl.String)).columns
df_string = df.select(string_cols)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
string_cols = df.select(pl.col(pl.Utf8))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")

string_cols = df.select(pl.col(pl.String))
print(string_cols)

Output:

shape: (891, 5)
┌─────────────────────────────────┬────────┬──────────────────┬───────┬──────────┐
│ Name                            ┆ Sex    ┆ Ticket           ┆ Cabin ┆ Embarked │
│ ---                             ┆ ---    ┆ ---              ┆ ---   ┆ ---      │
│ str                             ┆ str    ┆ str              ┆ str   ┆ str      │
╞═════════════════════════════════╪════════╪══════════════════╪═══════╪══════════╡
│ Braund, Mr. Owen Harris         ┆ male   ┆ A/5 21171        ┆ null  ┆ S        │
│ Cumings, Mrs. John Bradley (Fl… ┆ female ┆ PC 17599         ┆ C85   ┆ C        │
│ Heikkinen, Miss. Laina          ┆ female ┆ STON/O2. 3101282 ┆ null  ┆ S        │
│ Futrelle, Mrs. Jacques Heath (… ┆ female ┆ 113803           ┆ C123  ┆ S        │
│ Allen, Mr. William Henry        ┆ male   ┆ 373450           ┆ null  ┆ S        │
│ …                               ┆ …      ┆ …                ┆ …     ┆ …        │
│ Montvila, Rev. Juozas           ┆ male   ┆ 211536           ┆ null  ┆ S        │
│ Graham, Miss. Margaret Edith    ┆ female ┆ 112053           ┆ B42   ┆ S        │
│ Johnston, Miss. Catherine Hele… ┆ female ┆ W./C. 6607       ┆ null  ┆ S        │
│ Behr, Mr. Karl Howell           ┆ male   ┆ 111369           ┆ C148  ┆ C        │
│ Dooley, Mr. Patrick             ┆ male   ┆ 370376           ┆ null  ┆ Q        │
└─────────────────────────────────┴────────┴──────────────────┴───────┴──────────┘

Status: ✓ Success

select_columns_range

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all rows and columns 1-5 (inclusive) using numeric indexing
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of selecting ranges: df[:2, 1:6]

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

selected_df = df.select(pl.col(i) for i in range(5))

Error:

TypeError(msg)
TypeError: invalid input for `col`

Expected `str` or `DataType`, got 'int'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

selected_df = df[:, :5]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.select(df.columns[0:6])

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all rows and columns 1-5 (inclusive) using numeric indexing
selected_df = df.select(df.columns[1:6])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all rows and columns 1-5 (inclusive) using numeric indexing
selected_df = df[:, 1:6]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all rows and columns 1-5 (inclusive) using numeric indexing
# This selects columns at positions 1,2,3,4,5 (0-based indexing)
result = df.select(df.columns[1:6])

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select(df.columns[1:6])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df[:, 1:6]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(df.columns[0:5])

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
selected_df = df[:, 0:5]

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df[:, 0:5]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all rows and columns 1-5 (inclusive) using numeric indexing
df.select(df.columns[1:6])

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result_df = df[:, 1:6]

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_selected = df[:, 1:6]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code for the task:

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df[:, 0:6]

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
selected_df = df[:, 1:6]

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df[:, 1:6]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all rows and columns 1-5 (inclusive) using numeric indexing
df.select(df.columns[1:6])

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df[:, 0:5]

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpm_og4g2u.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df[:, 0:5]

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp61b452qm.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    result = df.select(df.columns[0:5])
    return result

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpxa5snl86.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

select_exclude_columns

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all columns except PassengerId, Survived, and Pclass
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of excluding columns: df.select(pl.exclude(["col1", "col2"]))

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def select_columns(df: pl.DataFrame) -> pl.DataFrame:
    """
    Selects all columns from a Polars DataFrame except 'PassengerId', 'Survived', and 'Pclass'.

    Args:
        df: The input Polars DataFrame.

    Returns:
        A new Polars DataFrame containing only the selected columns.
    """
    exclude_cols = ['PassengerId', 'Survived', 'Pclass']
    selected_cols = [col for col in df.columns if col not in exclude_cols]
    return df.select(selected_cols)

if __name__ == '__main__':
    df = pl.read_csv("data/titanic.csv")
    selected_df = select_columns(df)
    print(selected_df.head())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp4zyertjw.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

selected_df = df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

print(selected_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpo6to93ju.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpii_stg1f.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

selected_df = df.select(
    ["Name", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked"]
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result_df = df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

selected_columns = df.select(pl.exclude("PassengerId", "Survived", "Pclass"))

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all columns except PassengerId, Survived, and Pclass
selected_df = df.select(
    ["Name", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked"]
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_filtered = df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
selected_cols = df.select(["Name", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df_filtered = df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
# Select all columns except PassengerId, Survived, and Pclass
df_selected = df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
selected_df = df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

selected_df = df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
selected_df = df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df.select(pl.exclude(["PassengerId", "Survived", "Pclass"]))

Status: ✓ Success

select_multiple_columns

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select both the Survived and Age columns and return the first 3 rows
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars syntax: df.select("id","value").head(3)

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select(["Survived", "Age"]).head(3)
print(result)

Output:

shape: (3, 2)
┌──────────┬──────┐
│ Survived ┆ Age  │
│ ---      ┆ ---  │
│ i64      ┆ f64  │
╞══════════╪══════╡
│ 0        ┆ 22.0 │
│ 1        ┆ 38.0 │
│ 1        ┆ 26.0 │
└──────────┴──────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(["Survived", "Age"]).head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(["Survived", "Age"]).head(3)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
selected_df = df.select(['Survived', 'Age']).head(3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.select("Survived", "Age").head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select("Survived", "Age").head(3)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(['Survived', 'Age']).head(3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select("Survived", "Age").head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(["Survived", "Age"]).head(3)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select(["Survived", "Age"]).head(3)
print(result)

Output:

shape: (3, 2)
┌──────────┬──────┐
│ Survived ┆ Age  │
│ ---      ┆ ---  │
│ i64      ┆ f64  │
╞══════════╪══════╡
│ 0        ┆ 22.0 │
│ 1        ┆ 38.0 │
│ 1        ┆ 26.0 │
└──────────┴──────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select("Survived", "Age").head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select("Survived", "Age").head(3)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(["Survived", "Age"]).head(3)
print(result)

Output:

shape: (3, 2)
┌──────────┬──────┐
│ Survived ┆ Age  │
│ ---      ┆ ---  │
│ i64      ┆ f64  │
╞══════════╪══════╡
│ 0        ┆ 22.0 │
│ 1        ┆ 38.0 │
│ 1        ┆ 26.0 │
└──────────┴──────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(["Survived", "Age"]).head(3)
print(result)

Output:

shape: (3, 2)
┌──────────┬──────┐
│ Survived ┆ Age  │
│ ---      ┆ ---  │
│ i64      ┆ f64  │
╞══════════╪══════╡
│ 0        ┆ 22.0 │
│ 1        ┆ 38.0 │
│ 1        ┆ 26.0 │
└──────────┴──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(["Survived", "Age"]).head(3)

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(["Survived", "Age"]).head(3)

print(result)

Output:

shape: (3, 2)
┌──────────┬──────┐
│ Survived ┆ Age  │
│ ---      ┆ ---  │
│ i64      ┆ f64  │
╞══════════╪══════╡
│ 0        ┆ 22.0 │
│ 1        ┆ 38.0 │
│ 0        ┆ 26.0 │
└──────────┴──────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select("Survived","Age").head(3)
print(result)

Output:

shape: (3, 2)
┌──────────┬──────┐
│ Survived ┆ Age  │
│ ---      ┆ ---  │
│ i64      ┆ f64  │
╞══════════╪══════╡
│ 0        ┆ 22.0 │
│ 1        ┆ 38.0 │
│ 0        ┆ 26.0 │
└──────────┴──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(["Survived", "Age"]).head(3)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select(["Survived", "Age"]).head(3)
print(result)

Output:

shape: (3, 2)
┌──────────┬──────┐
│ Survived ┆ Age  │
│ ---      ┆ ---  │
│ i64      ┆ f64  │
╞══════════╪══════╡
│ 0        ┆ 22.0 │
│ 1        ┆ 38.0 │
│ 1        ┆ 26.0 │
└──────────┴──────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select("Survived","Age").head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select("Survived", "Age").head(3)
print(result)

Output:

shape: (3, 2)
┌──────────┬──────┐
│ Survived ┆ Age  │
│ ---      ┆ ---  │
│ i64      ┆ f64  │
╞══════════╪══════╡
│ 0        ┆ 22.0 │
│ 1        ┆ 38.0 │
│ 1        ┆ 26.0 │
└──────────┴──────┘

Status: ✓ Success

select_multiple_columns_brackets

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the Name and Fare columns using square bracket notation and return the first 3 rows
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of selecting multiple columns: df[["col1", "col2"]].head(3)

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

print(df[["Name", "Fare"]].head(3))

Output:

shape: (3, 2)
┌─────────────────────────────────┬─────────┐
│ Name                            ┆ Fare    │
│ ---                             ┆ ---     │
│ str                             ┆ f64     │
╞═════════════════════════════════╪═════════╡
│ Braund, Mr. Owen Harris         ┆ 7.25    │
│ Cumings, Mrs. John Bradley (Fl… ┆ 71.2833 │
│ Heikkinen, Miss. Laina          ┆ 7.925   │
└─────────────────────────────────┴─────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df[["Name", "Fare"]].head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the Name and Fare columns using square bracket notation and return the first 3 rows
print(df[["Name", "Fare"]].head(3))

Output:

shape: (3, 2)
┌─────────────────────────────────┬─────────┐
│ Name                            ┆ Fare    │
│ ---                             ┆ ---     │
│ str                             ┆ f64     │
╞═════════════════════════════════╪═════════╡
│ Braund, Mr. Owen Harris         ┆ 7.25    │
│ Cumings, Mrs. John Bradley (Fl… ┆ 71.2833 │
│ Heikkinen, Miss. Laina          ┆ 7.925   │
└─────────────────────────────────┴─────────┘

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df[["Name", "Fare"]].head(3)
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpc47321sq.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df[["Name", "Fare"]].head(3)
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpxzrvsv21.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

try:
    df = pl.read_csv("data/titanic.csv")
except:
    df = pl.DataFrame({'PassengerId': [1, 2, 3, 4, 5],
                  'Survived': [0, 1, 0, 1, 0],
                  'Pclass': [3, 1, 3, 1, 3],
                  'Name': ['Braund, Mr. Owen Harris', 'Cumings, Mrs. John Bradley (Florence Briggs Thayer)', 'Heikkinen, Miss. Laina', 'Futrelle, Mrs. Jacques Heath (Lily May Peel)', 'Allen, Mr. William Henry'],
                  'Sex': ['male', 'female', 'female', 'female', 'male'],
                  'Age': [22.0, 38.0, 26.0, 35.0, 35.0],
                  'SibSp': [1, 1, 0, 1, 0],
                  'Parch': [0, 0, 0, 0, 0],
                  'Ticket': ['A/5 21171', 'PC 17599', 'STON/O2. 3101282', '113803', '373450'],
                  'Fare': [7.2500, 71.2833, 7.9250, 53.1000, 8.0500],
                  'Cabin': [None, 'C85', None, 'C123', None],
                  'Embarked': ['S', 'C', 'S', 'S', 'S']})

result = df[["Name", "Fare"]].head(3)
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpj6cwbt9k.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
print(df.select(["Name", "Fare"]).head(3))

Output:

shape: (3, 2)
┌─────────────────────────────────┬─────────┐
│ Name                            ┆ Fare    │
│ ---                             ┆ ---     │
│ str                             ┆ f64     │
╞═════════════════════════════════╪═════════╡
│ Braund, Mr. Owen Harris         ┆ 7.25    │
│ Cumings, Mrs. John Bradley (Fl… ┆ 71.2833 │
│ Heikkinen, Miss. Laina          ┆ 7.925   │
└─────────────────────────────────┴─────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
print(df[["Name", "Fare"]].head(3))

Output:

shape: (3, 2)
┌─────────────────────────────────┬─────────┐
│ Name                            ┆ Fare    │
│ ---                             ┆ ---     │
│ str                             ┆ f64     │
╞═════════════════════════════════╪═════════╡
│ Braund, Mr. Owen Harris         ┆ 7.25    │
│ Cumings, Mrs. John Bradley (Fl… ┆ 71.2833 │
│ Heikkinen, Miss. Laina          ┆ 7.925   │
└─────────────────────────────────┴─────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df[["Name", "Fare"]].head(3)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df[["Name", "Fare"]].head(3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df[["Name", "Fare"]].head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df[["Name", "Fare"]].head(3)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df[["Name", "Fare"]].head(3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df[["Name", "Fare"]].head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df[["Name", "Fare"]].head(3)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df[['Name', 'Fare']].head(3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df[["Name", "Fare"]].head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df[["Name", "Fare"]].head(3)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df[["Name", "Fare"]].head(3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df[["Name", "Fare"]].head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df[["Name", "Fare"]].head(3)

Status: ✓ Success

select_numeric_index

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the first row and first column using numeric indexing
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of numeric indexing: df[0, 0]

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

first_row_first_col = df[0, 0]

print(first_row_first_col)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpmp7ivnps.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

first_row_first_column = df[0, 0]

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp3etah14h.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

# Creating DataFrames
df = pl.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
#df = pl.read_csv("file.csv",try_parse_dates=True)
# Selection
df.select("a", "b")            # Select columns
df.select(pl.all())                # Select all columns
# Filtering
df = pl.DataFrame({"value": [5, 12, 18, 25], "category": ["A", "B", "A", "C"]})
df.filter(pl.col("value") > 10)            # Basic comparison
df.filter(pl.col("category").is_in(["A", "B"]))    # Is in list
df.filter(pl.col("value").is_between(10, 20))     # Range check
df = pl.DataFrame({"value": [1, None, 3]})
df.filter(pl.col("value").is_null())          # Check for nulls
# AND and OR conditions
df = pl.DataFrame({"a": [6, 3, 8], "b": [8, 12, 7]})
df.filter((pl.col("a") > 5) & (pl.col("b") < 10))   # AND condition
df.filter((pl.col("a") > 5) | (pl.col("b") < 10))   # OR condition
df.filter(~(pl.col("a") > 5))             # NOT condition
# Expression Arithmetic
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
df = df.with_columns(
    (pl.col("a") + pl.col("b")).alias("sum"),     # Addition
    (pl.col("a") - pl.col("b")).alias("diff"),     # Subtraction
    (pl.col("a") * pl.col("b")).alias("product"),   # Multiplication
    (pl.col("a") / pl.col("b")).alias("division")   # Division
)
# Column Operations
df = df.with_columns(pl.lit("constant").alias("new_col")) # Add constant column
df = df.rename({"a": "new_name"})          # Rename columns
df = df.drop("b", "sum")             # Drop columns
# Sorting
df = pl.DataFrame({"column": [3, 1, 2]})
df = df.sort("column", descending=True)          # Sort by column
df = pl.DataFrame({"col1": [3, 1, 2], "col2": [4, 5, 6]})
df = df.sort(["col1", "col2"], descending=[True, False])  # Multi-column sort
# Aggregation and Grouping
df = pl.DataFrame({"value": [1, 2, 3]})
df = df.select(pl.col("value").sum())           # Sum values
df = pl.DataFrame({"value": [1, 2, 3]})
df = df.select(pl.col("value").mean())           # Calculate mean
df = pl.DataFrame({"value": [1, 2, 3]})
df = df.select(pl.col("value").len())          # Count values
df = pl.DataFrame({"value": [1, 2, 2, 3]})
df = df.select(pl.col("value").n_unique())         # Count unique values
# GroupBy operations
df = pl.DataFrame({"category": ["A", "B", "A"], "value": [1, 2, 3]})
df = df.group_by("category").agg(pl.col("value").mean())  # Group and aggregate
df = pl.DataFrame({"category": ["A", "B", "A"], "value": [1, 2, 3]})
df = df.group_by("category").agg(        # Group and aggregate
    pl.col("value").mean().name.suffix("_mean"),
    pl.col("value").min().name.suffix("_min"))
df = pl.DataFrame({"cat1": ["A", "B", "A"], "cat2": ["X", "Y", "X"], "val1": [1, 2, 3], "val2": [4, 5, 6]})
df = df.group_by(["cat1", "cat2"]).agg(          # Multiple aggregations
    pl.col("val1").sum(),
    pl.col("val2").mean()
)
# Reshaping Data
df = pl.DataFrame({"row_var": ["A", "B"], "col_var": ["X", "Y"], "value": [1, 2]})
#df = df.pivot(values="value", index="row_var", on="col_var") # Pivot table
#df.collect().pivot(values="value", index="row_var", on="col_var", aggregate_function="mean") # Pivot lazyframe
df = pl.DataFrame({"id": [1, 2], "val1": [3, 4], "val2": [5, 6]})
df = df.unpivot(index=["id"], on=["val1", "val2"])     # Unpivot/melt
df = pl.DataFrame({"list_column": [[1, 2], [3, 4]]})
df = df.explode("list_column")                   # Explode lists
df = df.with_row_index("index")                  # Add row indices
# String Operations
df = pl.DataFrame({"text": ["abc", "def"]})
df = df.select(pl.col("text").str.len_chars())           # String length
df = pl.DataFrame({"text": ["abc", "def"]})
df = df.select(pl.col("text").str.contains("b"))       # Contains pattern
df = pl.DataFrame({"text": ["abc", "def"]})
df = df.select(pl.col("text").str.replace("a", "x"))      # Replace text
df = pl.DataFrame({"text": ["ABC", "DEF"]})
df = df.select(pl.col("text").str.to_lowercase())         # Convert case
df = pl.DataFrame({"text": ["  abc  ", "  def  "]})
df = df.select(pl.col("text").str.strip())             # Remove whitespace
df = pl.DataFrame({"text": ["a b c", "d e f"]})
df = df.select(pl.col("text").str.split(" "))           # Split strings
# Window Functions
df = pl.DataFrame({"group": ["A", "A", "B", "B"], "value": [1, 2, 3, 4]})
df = df.with_columns(
    pl.col("value").sum().over("group")            # Sum by group
)
# Working with Missing Values
df = pl.DataFrame({"value": [1, None, 3], "col2": [None,2,3]})
df = df.drop_nulls()                        # Drop null rows
df = pl.DataFrame({"value": [1, None, 3], "col2": [None,2,3]})
df = df.drop_nulls(subset=["value", "col2"])         # Drop for specific columns
df = pl.DataFrame({"value": [1, None, 3]})
df = df.fill_null(strategy="forward")               # Forward fill
df = pl.DataFrame({"value": [1, None, 3]})
df = df.with_columns(
    pl.col("value").fill_null(pl.col("value").mean())     # Fill with mean
)
df = pl.DataFrame({"value": [1, None, 3]})
df = df.with_columns(
    pl.when(pl.col("value").is_null())
    .then(pl.lit(0))
    .otherwise(pl.col("value"))
    .alias("value")                      # Replace nulls conditionally
)
# Data Type Operations
df = pl.DataFrame({"a": ["1", "2", "3"]})
df = df.select(pl.col("a").cast(pl.Int64))             # Cast to type
df = pl.DataFrame({"date": [datetime(2023, 1, 1), datetime(2023, 2, 1)]})
df = df.select(pl.col("date").dt.year())              # Extract year
df = pl.DataFrame({"date": [datetime(2023, 1, 1), datetime(2023, 2, 1)]})
df = df.select(pl.col("date").dt.month())             # Extract month
df = pl.DataFrame({"date": [datetime(2023, 1, 1), datetime(2023, 2, 1)]})
df = df.select(pl.col("date").dt.strftime("%Y-%m-%d"))       # Format date
#df.schema                           # Get schema
# Advanced Features
# LazyFrame for optimized execution
df = pl.DataFrame({"a": [1, -2, 3]})
lazy_df = df.lazy()
result = lazy_df.filter(pl.col("a") > 0).collect()
# Joins
df1 = pl.DataFrame({"key": [1, 2], "a": [3, 4]})
df2 = pl.DataFrame({"key": [1, 2], "b": [5, 6]})
df1.join(df2, on="key", how="inner")             # Inner join
df1.join(df2, on="key", how="left")              # Left join
df1 = pl.DataFrame({"key1": [1, 2], "key2": [3, 4], "a": [5, 6]})
df2 = pl.DataFrame({"key1": [1, 2], "key2": [3, 4], "b": [7, 8]})
df1.join(df2, on=["key1", "key2"], how="full")        # Multiple join keys
# Sampling
df = pl.DataFrame({"a": range(20)})
df.sample(n=10)                        # Sample n rows
df.sample(frac=0.1, with_replacement=True)          # Sample fraction
# User-defined functions
df = pl.DataFrame({"a": [1, 2, 3]})
df.select(pl.col("a").map_elements(lambda x: x * 2,return_dtype=pl.Int64))     # Apply function
# Efficient operations
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
df.select([
    pl.all(),                       # All columns
    pl.exclude("a")             # Exclude column
])
df.with_columns(pl.exclude("a").name.prefix("new_"))   # Batch operations
# Expression contexts
df = pl.DataFrame({"a": [1, -2, 3]})
(
    df.lazy()
    .filter(pl.col("a") > 0)
    .with_columns(pl.lit(1).alias("constant"))
    .collect()
)                              # Method chaining
# Date and Time Operations
df = pl.DataFrame({"date": [datetime(2023, 1, 1, 12, 30, 0)]})
df = df.with_columns(
    pl.col("date").dt.date()                 # Extract date
)
df = pl.DataFrame({"date": [datetime(2023, 1, 1, 12, 30, 0)]})
df = df.with_columns(
    pl.col("date").dt.time()                 # Extract time
)
pl.date_range(datetime(2023, 1, 1), datetime(2023, 3, 31), interval="1d", eager=True) # Create a date range Series

import polars as pl
df = pl.read_csv("data/titanic.csv")

print(df[0, 0])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmps86osnqb.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

first_row_first_col = df.at(0, "PassengerId")
print(first_row_first_col)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpq78egfsh.py", line 6, in 
    first_row_first_col = df.at(0, "PassengerId")
                          ^^^^^
AttributeError: 'DataFrame' object has no attribute 'at'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

first_row_first_col = df[0, 0]
print(first_row_first_col)

Output:

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the first row and first column using numeric indexing
first_row_first_col = df.select(pl.col(0).first()).select(pl.col(0).first())
print(first_row_first_col)

Error:

TypeError(msg)
TypeError: invalid input for `col`

Expected `str` or `DataType`, got 'int'.

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
first_row_first_column = df[0, 0]

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df[0, 0]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the first row and first column using numeric indexing
first_row_first_col = df[0, 0]

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
first_value = df[0, 0]

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
first_row_first_column = df[0, 0]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the first row and first column using numeric indexing
first_value = df[0, 0]
print(first_value)

Output:

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
`print(df[0, 0])`

Error:

invalid syntax (, line 3)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
`print(df[0, 0])`

Error:

invalid syntax (, line 3)

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code for the task:

import polars as pl

df = pl.read_csv("data/titanic.csv")

first_row_first_col = df.select(pl.col(0)).head(1)

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
first_value = df[0, 0]

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
first_value = df[0, 0]
print(first_value)

Output:

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# First row, first column using numeric indexing
first_value = df[0,0]

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the first row and first column using numeric indexing
first_row_first_col = df[0, 0]
print(first_row_first_col)

Output:

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the first row and first column using numeric indexing
first_element = df[0, 0]
print(first_element)

Output:

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the first row and first column using numeric indexing
first_row_first_column = df.row(0)[0]
# Alternative method:
# first_row_first_column = df.slice(0, 1).select(df.columns[0]).item()

Status: ✓ Success

select_rows_and_columns

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select rows 1-3 (inclusive) from the Name and Age columns
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of selecting rows and columns: df[1:4, ["col1", "col2"]]

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(["Name", "Age"]).head(3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df[0:3, ["Name", "Age"]]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select rows 1-3 (inclusive) from the Name and Age columns
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked
df.select(["Name", "Age"]).head(3)

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select(["Name", "Age"])[1:4]

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpiq9e1v_q.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df[0:3, ["Name", "Age"]]
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpbfy5ah0p.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")

    result = df.select(['Name', 'Age'])[0:3]
    
    print(result)

solve()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpgx4x21hd.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select(["Name", "Age"]).head(3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df[0:3, ["Name", "Age"]]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select(["Name", "Age"]).head(3)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df[1:4].select(["Name", "Age"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df[1:4, ["Name", "Age"]]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.slice(1, 3).select(["Name", "Age"])

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select(["Name", "Age"]).slice(0, 3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df[1:4, ["Name", "Age"]]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select(["Name", "Age"]).slice(0, 3)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select rows 1-3 (inclusive) from the Name and Age columns
result = df.select(["Name", "Age"]).slice(0, 3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

selected_df = df[1:4, ["Name", "Age"]]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select rows 1-3 (inclusive) from the Name and Age columns
df.select(["Name", "Age"]).slice(1, 3)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
selected_rows = df[1:4, ['Name', 'Age']]

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df[1:4, ["Name", "Age"]]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_selected_rows = df.slice(0, 3).select("Name", "Age")

Status: ✓ Success

select_single_column

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select only the Age column and return the first 3 rows
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars syntax: df.select("id")

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

age_column = df.select("Age").head(3)
print(age_column)

Output:

shape: (3, 1)
┌──────┐
│ Age  │
│ ---  │
│ f64  │
╞══════╡
│ 22.0 │
│ 38.0 │
│ 26.0 │
└──────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

age_df = df.select("Age")
print(age_df.head(3))

Output:

shape: (3, 1)
┌──────┐
│ Age  │
│ ---  │
│ f64  │
╞══════╡
│ 22.0 │
│ 38.0 │
│ 26.0 │
└──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select("Age").head(3)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select("Age").head(3)
print(result)

Output:

shape: (3, 1)
┌──────┐
│ Age  │
│ ---  │
│ f64  │
╞══════╡
│ 22.0 │
│ 38.0 │
│ 26.0 │
└──────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
age_first_3_rows = df.select("Age").head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select("Age").head(3)

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select('Age').head(3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select("Age").head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select("Age").head(3)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select only the Age column and return the first 3 rows
result = df.select("Age").head(3)
print(result)

Output:

shape: (3, 1)
┌──────┐
│ Age  │
│ ---  │
│ f64  │
╞══════╡
│ 22.0 │
│ 38.0 │
│ 26.0 │
└──────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select("Age").head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select("Age").head(3)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select("Age").head(3)

print(result)

Output:

shape: (3, 1)
┌──────┐
│ Age  │
│ ---  │
│ f64  │
╞══════╡
│ 22.0 │
│ 38.0 │
│ 26.0 │
└──────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select("Age").head(3)
print(result)

Output:

shape: (3, 1)
┌──────┐
│ Age  │
│ ---  │
│ f64  │
╞══════╡
│ 22.0 │
│ 38.0 │
│ 26.0 │
└──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
print(df.select("Age").head(3))

Output:

shape: (3, 1)
┌──────┐
│ Age  │
│ ---  │
│ f64  │
╞══════╡
│ 22.0 │
│ 38.0 │
│ 26.0 │
└──────┘

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
print(df.select("Age").head(3))

Output:

shape: (3, 1)
┌──────┐
│ Age  │
│ ---  │
│ f64  │
╞══════╡
│ 22.0 │
│ 38.0 │
│ 26.0 │
└──────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select("Age").head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.select("Age").head(3)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select("Age").head(3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select("Age").head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select("Age").head(3)

Status: ✓ Success

select_single_column_brackets

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Get the Age column as a Series and return the first 3 rows
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of selecting a single column: df["column_name"].head(3)

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
age_series = df["Age"]
print(age_series[:3])

Output:

shape: (3,)
Series: 'Age' [f64]
[
	22.0
	38.0
	26.0
]

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

age_series = df["Age"]
print(age_series.head(3))

Output:

shape: (3,)
Series: 'Age' [f64]
[
	22.0
	38.0
	26.0
]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
age_series = df["Age"]
print(age_series[:3])

Output:

shape: (3,)
Series: 'Age' [f64]
[
	22.0
	38.0
	26.0
]

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
age_series = df["Age"]
result = age_series.head(3)
result

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

age_series = df["Age"].head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

age_series = df["Age"]
first_three = age_series.head(3)
print(first_three)

Output:

shape: (3,)
Series: 'Age' [f64]
[
	22.0
	38.0
	26.0
]

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
age_series = df["Age"]
print(age_series.head(3))

Output:

shape: (3,)
Series: 'Age' [f64]
[
	22.0
	38.0
	26.0
]

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df["Age"].head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
age_series = df["Age"]
print(age_series.head(3))

Output:

shape: (3,)
Series: 'Age' [f64]
[
	22.0
	38.0
	26.0
]

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
age_series = df['Age']
print(age_series.head(3))

Output:

shape: (3,)
Series: 'Age' [f64]
[
	22.0
	38.0
	26.0
]

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df["Age"].head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select("Age").head(3)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
age_series = df["Age"]
print(age_series.head(3))

Output:

shape: (3,)
Series: 'Age' [f64]
[
	22.0
	38.0
	26.0
]

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
age_series = df["Age"]
print(age_series.head(3))

Output:

shape: (3,)
Series: 'Age' [f64]
[
	22.0
	38.0
	26.0
]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code to achieve the task:


import polars as pl

df = pl.read_csv("data/titanic.csv")

age_series = df.select(pl.col("Age"))
print(age_series.head(3))

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

age_series = df["Age"]

print(age_series[:3])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpviw9x64o.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

age_series = df["Age"]

result = age_series.head(3)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpz4xzekpt.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df["Age"].head(3)
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdninzaoz.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
age_series = df.get_column("Age").head(3)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
age_series = df["Age"].head(3)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

age_series = df.select("Age").to_series()
first_three_ages = age_series.head(3)
first_three_ages

Status: ✓ Success

select_with_aggregation

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Create a one-row DataFrame with two columns: the minimum Age (named 'age_min') and maximum Age (named 'age_max')
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars syntax  df.select("id","value").head(3)

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select([
    pl.col("Age").min().alias("age_min"),
    pl.col("Age").max().alias("age_max")
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.select([
    pl.col("Age").min().alias("age_min"),
    pl.col("Age").max().alias("age_max")
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.select([
    pl.col("Age").min().alias("age_min"),
    pl.col("Age").max().alias("age_max")
])

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(
    pl.min("Age").alias("age_min"),
    pl.max("Age").alias("age_max")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(
    pl.min("Age").alias("age_min"),
    pl.max("Age").alias("age_max")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select([
    pl.col("Age").min().alias("age_min"),
    pl.col("Age").max().alias("age_max")
])

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    result = pl.DataFrame({
        'age_min': [df['Age'].min()],
        'age_max': [df['Age'].max()]
    })
    return result

if __name__ == "__main__":
    # Create a dummy csv file for testing
    with open("data/titanic.csv", "w") as f:
        f.write("PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked\n")
        f.write("1,0,3,Braund,Mr. Owen Harris,22.0,1,0,A/5 21171,7.25,,S\n")
        f.write("2,1,1,Cumings,Mrs. John Bradley (Florence Briggs Thayer),female,38.0,1,0,PC 17599,71.2833,C85,C\n")
        f.write("3,1,3,Heikkinen,Miss. Laina,26.0,0,0,STON/O2. 3101282,7.925,,S\n")
        f.write("4,1,1,Futrelle,Mrs. Jacques Heath (Lily May Peel),female,35.0,1,0,113803,53.1,C123,S\n")
        f.write("5,0,3,Allen,Mr. William Henry,35.0,0,0,373450,8.05,,S\n")

    print(solve())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwuzwpip0.py", line 23, in 
    print(solve())
          ^^^^^^^
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwuzwpip0.py", line 6, in solve
    df = pl.read_csv("data/titanic.csv")
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(
    [
        pl.col("Age").min().alias("age_min"),
        pl.col("Age").max().alias("age_max"),
    ]
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0frd88e4.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

df = pl.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
#df = pl.read_csv("file.csv",try_parse_dates=True)
# Selection
df.select("a", "b")            # Select columns
df.select(pl.all())                # Select all columns
# Filtering
df.filter(pl.col("a") > 1)            # Basic comparison
df.filter(pl.col("b").is_in(["x", "y"]))    # Is in list
df.filter(pl.col("a").is_between(1, 2))     # Range check
df.filter(pl.col("a").is_null())          # Check for nulls
# AND and OR conditions
df.filter((pl.col("a") > 1) & (pl.col("a") < 3))   # AND condition
df.filter((pl.col("a") > 1) | (pl.col("a") < 3))   # OR condition
df.filter(~(pl.col("a") > 1))             # NOT condition
# Expression Arithmetic
df = df.with_columns(
(pl.col("a") + pl.lit(1)).alias("sum"),     # Addition
(pl.col("a") - pl.lit(1)).alias("diff"),     # Subtraction
(pl.col("a") * pl.lit(1)).alias("product"),   # Multiplication
(pl.col("a") / pl.lit(1)).alias("division")   # Division
)
# Column Operations
df = df.with_columns(pl.lit("constant").alias("new_col")) # Add constant column
df = df.rename({"a": "new_a"})          # Rename columns
df = df.drop("b")             # Drop columns
# Sorting
df = df.sort("new_a", descending=True)          # Sort by column
#df.sort(["col1", "col2"], descending=[True, False])  # Multi-column sort
# Aggregation and Grouping
df.select(pl.col("new_a").sum())           # Sum values
df.select(pl.col("new_a").mean())           # Calculate mean
df.select(pl.col("new_a").len())          # Count values
df.select(pl.col("new_a").n_unique())         # Count unique values
# GroupBy operations
#df.group_by("category").agg(pl.col("value").mean())  # Group and aggregate
#df.group_by("category").agg(        # Group and aggregate
#pl.col("value").mean().name.suffix("_mean"),
#pl.col("value").min().name.suffix("_min"))
#df.group_by(["cat1", "cat2"]).agg(          # Multiple aggregations
#pl.col("val1").sum(),
#pl.col("val2").mean()
#)
# Reshaping Data
#df.pivot(values="value", index="row_var", on="col_var") # Pivot table
#df.collect().pivot(values="value", index="row_var", on="col_var", aggregate_function="mean") # Pivot lazyframe
#df.unpivot(index=["id"], on=["val1", "val2"])     # Unpivot/melt
#df.explode("list_column")                   # Explode lists
df = df.with_row_index("index")                  # Add row indices
# String Operations
#df.select(pl.col("text").str.len_chars())           # String length
#df.select(pl.col("text").str.contains("pattern"))       # Contains pattern
#df.select(pl.col("text").str.replace("old", "new"))      # Replace text
#df.select(pl.col("text").str.to_lowercase())         # Convert case
#df.select(pl.col("text").str.strip())             # Remove whitespace
#df.select(pl.col("text").str.split(" "))           # Split strings
# Window Functions
#df.with_columns(
#pl.col("value").sum().over("group")            # Sum by group
#)
# Working with Missing Values
#df.drop_nulls()                        # Drop null rows
#df.drop_nulls(subset=["column1", "column2"])         # Drop for specific columns
#df.fill_null(strategy="forward")               # Forward fill
#df.with_columns(
#pl.col("value").fill_null(pl.col("value").mean())     # Fill with mean
#)
#df.with_columns(
#pl.when(pl.col("value").is_null())
#.then(pl.lit(0))
#.otherwise(pl.col("value"))
#.alias("value")                      # Replace nulls conditionally
#)
# Data Type Operations
df.select(pl.col("new_a").cast(pl.Int64))             # Cast to type
#df.select(pl.col("date").dt.year())              # Extract year
#df.select(pl.col("date").dt.month())             # Extract month
#df.select(pl.col("date").dt.strftime("%Y-%m-%d"))       # Format date
df.schema                           # Get schema
# Advanced Features
# LazyFrame for optimized execution
lazy_df = df.lazy()
result = lazy_df.filter(pl.col("new_a") > 0).collect()
# Joins
#df1.join(df2, on="key", how="inner")             # Inner join
#df1.join(df2, on="key", how="left")              # Left join
#df1.join(df2, on=["key1", "key2"], how="full")        # Multiple join keys
# Sampling
df = df.sample(n=1)                        # Sample n rows
df.sample(frac=0.1, with_replacement=True)          # Sample fraction
# User-defined functions
df.select(pl.col("new_a").map_elements(lambda x: x * 2,return_dtype=pl.Int64))     # Apply function
# Efficient operations
df.select([
pl.all(),                       # All columns
pl.exclude("new_a")             # Exclude column
])
#df.with_columns(pl.exclude("a").name.prefix("new_"))   # Batch operations
# Expression contexts
(
df.lazy()
.filter(pl.col("new_a") > 0)
.with_columns(pl.lit(1).alias("constant"))
.collect()
)                              # Method chaining
# Date and Time Operations
#df.with_columns(
#pl.col("date").dt.date()                 # Extract date
#)
#df.with_columns(
#pl.col("date").dt.time()                 # Extract time
#)
pl.date_range(datetime(2023, 1, 1), datetime(2023, 3, 31), interval="1d", eager=True) # Create a date range Series
import polars as pl

def create_min_max_age_df(file_path: str) -> pl.DataFrame:
    """
    Creates a one-row DataFrame with the minimum and maximum ages from a CSV file.

    Args:
        file_path (str): The path to the CSV file containing the data.

    Returns:
        pl.DataFrame: A DataFrame with one row and two columns: 'age_min' and 'age_max'.
    """
    df = pl.read_csv(file_path)
    min_age = df["Age"].min()
    max_age = df["Age"].max()
    result_df = pl.DataFrame({"age_min": [min_age], "age_max": [max_age]})
    return result_df

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpa0otkebc.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select(
    pl.col("Age").min().alias("age_min"),
    pl.col("Age").max().alias("age_max")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select(
    pl.min("Age").alias("age_min"),
    pl.max("Age").alias("age_max")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.select(
    pl.col("Age").min().alias("age_min"),
    pl.col("Age").max().alias("age_max")
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
age_min_max = df.select([
    pl.col("Age").min().alias("age_min"),
    pl.col("Age").max().alias("age_max")
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
age_stats = df.select(pl.col("Age").min().alias("age_min"), pl.col("Age").max().alias("age_max"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select([
    pl.col("Age").min().alias("age_min"),
    pl.col("Age").max().alias("age_max")
])

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")

min_max_age = pl.DataFrame({'age_min': [df['Age'].min()], 'age_max': [df['Age'].max()]})

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select(pl.col("Age").min().alias("age_min"), pl.col("Age").max().alias("age_max")).head(1)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select([
    pl.col("Age").min().alias("age_min"),
    pl.col("Age").max().alias("age_max")
]).collect()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpo6i8x_el.py", line 11, in 
    ]).collect()
       ^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'collect'

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

age_min = df["Age"].min()
age_max = df["Age"].max()

result_df = pl.DataFrame({"age_min": [age_min], "age_max": [age_max]})

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

age_min = df["Age"].min()
age_max = df["Age"].max()

result_df = pl.DataFrame({"age_min": [age_min], "age_max": [age_max]})

print(result_df)

Output:

shape: (1, 2)
┌─────────┬─────────┐
│ age_min ┆ age_max │
│ ---     ┆ ---     │
│ f64     ┆ f64     │
╞═════════╪═════════╡
│ 0.42    ┆ 80.0    │
└─────────┴─────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

age_min = df.select(pl.col("Age").min()).item()
age_max = df.select(pl.col("Age").max()).item()

result_df = pl.DataFrame({"age_min": [age_min], "age_max": [age_max]})

print(result_df)

Output:

shape: (1, 2)
┌─────────┬─────────┐
│ age_min ┆ age_max │
│ ---     ┆ ---     │
│ f64     ┆ f64     │
╞═════════╪═════════╡
│ 0.42    ┆ 80.0    │
└─────────┴─────────┘

Status: ✓ Success

select_with_aggregation_by_group

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# For each Pclass:
# 1. Calculate the mean Age
# 2. Calculate the maximum Fare
# 3. Count the number of passengers
# Sort by Pclass
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of groupby with multiple aggregations:
# df.group_by("group").agg([
#     pl.col("value").mean().name.suffix("_mean"),
#     pl.col("value").max().name.suffix("_max"),
#     pl.len().name.suffix("_len")
# ])

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("mean_age"),
    pl.col("Fare").max().alias("max_fare"),
    pl.count().alias("passenger_count")
]).sort("Pclass")

print(result)

Output:

shape: (3, 4)
┌────────┬───────────┬──────────┬─────────────────┐
│ Pclass ┆ mean_age  ┆ max_fare ┆ passenger_count │
│ ---    ┆ ---       ┆ ---      ┆ ---             │
│ i64    ┆ f64       ┆ f64      ┆ u32             │
╞════════╪═══════════╪══════════╪═════════════════╡
│ 1      ┆ 38.233441 ┆ 512.3292 ┆ 216             │
│ 2      ┆ 29.87763  ┆ 73.5     ┆ 184             │
│ 3      ┆ 25.14062  ┆ 69.55    ┆ 491             │
└────────┴───────────┴──────────┴─────────────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmps9cvssl1.py:10: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("passenger_count")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("mean_age"),
    pl.col("Fare").max().alias("max_fare"),
    pl.len().alias("passenger_count")
]).sort("Pclass")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.col("Age").mean().alias("mean_age"),
    pl.col("Fare").max().alias("max_fare"),
    pl.col("PassengerId").count().alias("passenger_count")
).sort("Pclass")

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass").agg([
    pl.col("Age").mean().alias("mean_Age"),
    pl.col("Fare").max().alias("max_Fare"),
    pl.count().alias("count_passengers")
])

print(result.sort("Pclass"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwb2vglst.py", line 8, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.groupby("Pclass").agg([
    pl.col("Age").mean().name("mean_Age"),
    pl.col("Fare").max().name("max_Fare"),
    pl.col("PassengerId").count().name("count_Passengers")
]).sort("Pclass")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpqx6_oj3y.py", line 7, in 
    result = df.groupby("Pclass").agg([
             ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result_df = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("mean_Age"),
    pl.col("Fare").max().alias("max_Fare"),
    pl.col("PassengerId").count().alias("count_passengers"),
]).sort("Pclass")

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg(
    pl.mean("Age").alias("Mean_Age"),
    pl.max("Fare").alias("Max_Fare"),
    pl.count("PassengerId").alias("Passenger_Count")
).sort("Pclass")

print(result)

Output:

shape: (3, 4)
┌────────┬───────────┬──────────┬─────────────────┐
│ Pclass ┆ Mean_Age  ┆ Max_Fare ┆ Passenger_Count │
│ ---    ┆ ---       ┆ ---      ┆ ---             │
│ i64    ┆ f64       ┆ f64      ┆ u32             │
╞════════╪═══════════╪══════════╪═════════════════╡
│ 1      ┆ 38.233441 ┆ 512.3292 ┆ 216             │
│ 2      ┆ 29.87763  ┆ 73.5     ┆ 184             │
│ 3      ┆ 25.14062  ┆ 69.55    ┆ 491             │
└────────┴───────────┴──────────┴─────────────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("Age_mean"),
    pl.col("Fare").max().alias("Fare_max"),
    pl.len().alias("Count")
]).sort("Pclass")

print(result)

Output:

shape: (3, 4)
┌────────┬───────────┬──────────┬───────┐
│ Pclass ┆ Age_mean  ┆ Fare_max ┆ Count │
│ ---    ┆ ---       ┆ ---      ┆ ---   │
│ i64    ┆ f64       ┆ f64      ┆ u32   │
╞════════╪═══════════╪══════════╪═══════╡
│ 1      ┆ 38.233441 ┆ 512.3292 ┆ 216   │
│ 2      ┆ 29.87763  ┆ 73.5     ┆ 184   │
│ 3      ┆ 25.14062  ┆ 69.55    ┆ 491   │
└────────┴───────────┴──────────┴───────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(
        pl.col("Age").mean().alias("mean_age"),
        pl.col("Fare").max().alias("max_fare"),
        pl.count().alias("passenger_count")
    )
    .sort("Pclass")
)

print(result)

Output:

shape: (3, 4)
┌────────┬───────────┬──────────┬─────────────────┐
│ Pclass ┆ mean_age  ┆ max_fare ┆ passenger_count │
│ ---    ┆ ---       ┆ ---      ┆ ---             │
│ i64    ┆ f64       ┆ f64      ┆ u32             │
╞════════╪═══════════╪══════════╪═════════════════╡
│ 1      ┆ 38.233441 ┆ 512.3292 ┆ 216             │
│ 2      ┆ 29.87763  ┆ 73.5     ┆ 184             │
│ 3      ┆ 25.14062  ┆ 69.55    ┆ 491             │
└────────┴───────────┴──────────┴─────────────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpykgji0du.py:12: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("passenger_count")

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.groupby("Pclass")
    .agg(
        [
            pl.col("Age").mean().alias("mean_age"),
            pl.col("Fare").max().alias("max_fare"),
            pl.col("PassengerId").count().alias("passenger_count"),
        ]
    )
    .sort("Pclass")
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0v1c53ea.py", line 8, in 
    df.groupby("Pclass")
    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg([
        pl.col("Age").mean().name.suffix("_mean"),
        pl.col("Fare").max().name.suffix("_max"),
        pl.len().name.suffix("_len")
    ])
    .sort("Pclass")
)

print(result)

Output:

shape: (3, 4)
┌────────┬───────────┬──────────┬─────────┐
│ Pclass ┆ Age_mean  ┆ Fare_max ┆ len_len │
│ ---    ┆ ---       ┆ ---      ┆ ---     │
│ i64    ┆ f64       ┆ f64      ┆ u32     │
╞════════╪═══════════╪══════════╪═════════╡
│ 1      ┆ 38.233441 ┆ 512.3292 ┆ 216     │
│ 2      ┆ 29.87763  ┆ 73.5     ┆ 184     │
│ 3      ┆ 25.14062  ┆ 69.55    ┆ 491     │
└────────┴───────────┴──────────┴─────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg(
        [
            pl.col("Age").mean().alias("mean_age"),
            pl.col("Fare").max().alias("max_fare"),
            pl.count().alias("passenger_count"),
        ]
    )
    .sort("Pclass")
)

print(result)

Output:

shape: (3, 4)
┌────────┬───────────┬──────────┬─────────────────┐
│ Pclass ┆ mean_age  ┆ max_fare ┆ passenger_count │
│ ---    ┆ ---       ┆ ---      ┆ ---             │
│ i64    ┆ f64       ┆ f64      ┆ u32             │
╞════════╪═══════════╪══════════╪═════════════════╡
│ 1      ┆ 38.233441 ┆ 512.3292 ┆ 216             │
│ 2      ┆ 29.87763  ┆ 73.5     ┆ 184             │
│ 3      ┆ 25.14062  ┆ 69.55    ┆ 491             │
└────────┴───────────┴──────────┴─────────────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpdp_xfccj.py:13: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("passenger_count"),

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")

    result = (
        df.group_by("Pclass")
        .agg(
            [
                pl.col("Age").mean().alias("Mean_Age"),
                pl.col("Fare").max().alias("Max_Fare"),
                pl.count().alias("Passenger_Count"),
            ]
        )
        .sort("Pclass")
    )
    
    return result

if __name__ == "__main__":
    # Create a dummy titanic.csv file
    import pandas as pd
    data = {'PassengerId': [1, 2, 3, 4, 5, 6],
            'Survived': [0, 1, 1, 0, 0, 1],
            'Pclass': [1, 1, 2, 2, 3, 3],
            'Name': ['A', 'B', 'C', 'D', 'E', 'F'],
            'Sex': ['male', 'female', 'male', 'female', 'male', 'female'],
            'Age': [22.0, 38.0, 26.0, 35.0, 22.0, 30.0],
            'SibSp': [1, 1, 0, 0, 0, 0],
            'Parch': [0, 0, 0, 0, 0, 0],
            'Ticket': ['A', 'B', 'C', 'D', 'E', 'F'],
            'Fare': [71.2833, 71.2833, 26.0, 26.0, 7.25, 7.25],
            'Cabin': ['C', 'C', 'E', 'E', None, None],
            'Embarked': ['S', 'S', 'S', 'S', 'S', 'S']}
    
    df_pandas = pd.DataFrame(data)
    import os
    if not os.path.exists("data"):
        os.makedirs("data")
    df_pandas.to_csv("data/titanic.csv", index=False)

    result_df = solve()
    print(result_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpm_qgt2uo.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("Mean_Age"),
    pl.col("Fare").max().alias("Max_Fare"),
    pl.count().alias("Passenger_Count")
]).sort("Pclass")

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpf0gnxgnt.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")

    result = (
        df.group_by("Pclass")
        .agg(
            [
                pl.col("Age").mean().alias("mean_age"),
                pl.col("Fare").max().alias("max_fare"),
                pl.col("PassengerId").count().alias("passenger_count"),
            ]
        )
        .sort("Pclass")
    )
    return result

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0eo9v0w7.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (df.groupby('Pclass')
         .agg([
             pl.col('Age').mean().alias('mean_age'),
             pl.col('Fare').max().alias('max_fare'),
             pl.count().alias('passenger_count')
         ])
         .sort('Pclass'))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmprr003a_n.py", line 7, in 
    result = (df.groupby('Pclass')
              ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("Age_mean"),
    pl.col("Fare").max().alias("Fare_max"),
    pl.len().alias("Passenger_count")
]).sort("Pclass")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg([
        pl.col("Age").mean().alias("Mean_Age"),
        pl.col("Fare").max().alias("Max_Fare"), 
        pl.col("PassengerId").count().alias("Passenger_Count")
    ])
    .sort("Pclass")
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = (
    df.groupby("Pclass")
    .agg([
        pl.col("Age").mean().alias("mean_age"),
        pl.col("Fare").max().alias("max_fare"),
        pl.col("PassengerId").count().alias("passenger_count")
    ])
    .sort("Pclass")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwbb9zh21.py", line 4, in 
    df.groupby("Pclass")
    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.group_by("Pclass").agg([
    pl.col("Age").mean().alias("mean_Age"),
    pl.col("Fare").max().alias("max_Fare"),
    pl.count().alias("passenger_count")
]).sort("Pclass")

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpng2lykfo.py:6: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("passenger_count")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.group_by("Pclass")
    .agg([
        pl.col("Age").mean().alias("mean_age"),
        pl.col("Fare").max().alias("max_fare"),
        pl.col("PassengerId").count().alias("passenger_count")
    ])
    .sort("Pclass")
)

Status: ✓ Success

select_with_alias

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the Fare column twice - once as is and once rounded to 0 decimal places named 'roundedFare'
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars syntax: df.select(pl.col("id").alias("ID"))

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

selected_df = df.select([
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select([
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select(
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns([
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select([
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
])

print(result)

Output:

shape: (5, 2)
┌───────┬─────────────┐
│ Fare  ┆ roundedFare │
│ ---   ┆ ---         │
│ f64   ┆ f64         │
╞═══════╪═════════════╡
│ 7.25  ┆ 7.0         │
│ 71.28 ┆ 71.0        │
│ 7.92  ┆ 8.0         │
│ 53.1  ┆ 53.0        │
│ null  ┆ null        │
└───────┴─────────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.select(
    [
        pl.col("Fare"),
        pl.col("Fare").round(0).alias("roundedFare"),
    ]
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select([
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select([
    "Fare",
    pl.col("Fare").round(0).alias("roundedFare")
])

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select([
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.col("Fare"), pl.col("Fare").round(0).alias("roundedFare"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select(
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
)

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns([
    pl.col("Fare").alias("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result_df = df.select([
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.select([
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
])

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    [
        pl.col("Fare").alias("Fare"),
        pl.col("Fare").round(0).alias("roundedFare"),
    ]
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.select(
    pl.col("Fare").alias("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").alias("fare"),
    pl.col("Fare").round(0).alias("roundedFare")
)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select([
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select([
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select([
    pl.col("Fare"),
    pl.col("Fare").round(0).alias("roundedFare")
])

Status: ✓ Success

select_with_filter

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Filter for passengers with both siblings/spouses (SibSp > 0) and parents/children (Parch > 0) aboard
# Then select only the Survived, SibSp, and Parch columns
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars syntax: df.filter(pl.col("id")=="a")

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
filtered_df = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select("Survived", "SibSp", "Parch")

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0))
selected_df = filtered_df.select(["Survived", "SibSp", "Parch"])

print(selected_df)

Output:

shape: (142, 3)
┌──────────┬───────┬───────┐
│ Survived ┆ SibSp ┆ Parch │
│ ---      ┆ ---   ┆ ---   │
│ i64      ┆ i64   ┆ i64   │
╞══════════╪═══════╪═══════╡
│ 0        ┆ 3     ┆ 1     │
│ 1        ┆ 1     ┆ 1     │
│ 0        ┆ 1     ┆ 5     │
│ 0        ┆ 4     ┆ 1     │
│ 0        ┆ 3     ┆ 1     │
│ …        ┆ …     ┆ …     │
│ 1        ┆ 1     ┆ 1     │
│ 0        ┆ 8     ┆ 2     │
│ 1        ┆ 1     ┆ 1     │
│ 1        ┆ 1     ┆ 1     │
│ 0        ┆ 1     ┆ 2     │
└──────────┴───────┴───────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0))

result_df = filtered_df.select(["Survived", "SibSp", "Parch"])

print(result_df)

Output:

shape: (142, 3)
┌──────────┬───────┬───────┐
│ Survived ┆ SibSp ┆ Parch │
│ ---      ┆ ---   ┆ ---   │
│ i64      ┆ i64   ┆ i64   │
╞══════════╪═══════╪═══════╡
│ 0        ┆ 3     ┆ 1     │
│ 1        ┆ 1     ┆ 1     │
│ 0        ┆ 1     ┆ 5     │
│ 0        ┆ 4     ┆ 1     │
│ 0        ┆ 3     ┆ 1     │
│ …        ┆ …     ┆ …     │
│ 1        ┆ 1     ┆ 1     │
│ 0        ┆ 8     ┆ 2     │
│ 1        ┆ 1     ┆ 1     │
│ 1        ┆ 1     ┆ 1     │
│ 0        ┆ 1     ┆ 2     │
└──────────┴───────┴───────┘

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmplyfrpr4f.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp655cajuy.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpc6im6jak.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_filtered = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)) \
          .select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result_df = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result_df = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
filtered_df = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0)).select(["Survived", "SibSp", "Parch"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

filtered_df = df.filter((pl.col("SibSp") > 0) & (pl.col("Parch") > 0))
result_df = filtered_df.select("Survived", "SibSp", "Parch")

Status: ✓ Success

select_with_lazy_optimization

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.scan_csv("data/titanic.csv")

# Using lazy mode:
# 1. Select the Age and Fare columns
# 2. Filter for passengers over 30 years old
# 3. Calculate the mean of both columns
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of lazy evaluation:
# pl.scan_csv("file.csv").select(["col1", "col2"]).filter(pl.col("col1") > 0).collect()

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.scan_csv("data/titanic.csv")

result = (df
    .select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .select([
        pl.col("Age").mean().alias("avg_age"),
        pl.col("Fare").mean().alias("avg_fare")
    ])
    .collect()
)

print(result)

Output:

shape: (1, 2)
┌───────────┬──────────┐
│ avg_age   ┆ avg_fare │
│ ---       ┆ ---      │
│ f64       ┆ f64      │
╞═══════════╪══════════╡
│ 42.927869 ┆ 42.35291 │
└───────────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.scan_csv("data/titanic.csv")

result = (
    df
    .select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .mean()
    .collect()
)

print(result)

Output:

shape: (1, 2)
┌───────────┬──────────┐
│ Age       ┆ Fare     │
│ ---       ┆ ---      │
│ f64       ┆ f64      │
╞═══════════╪══════════╡
│ 42.927869 ┆ 42.35291 │
└───────────┴──────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.scan_csv("data/titanic.csv")

result = (
    df.select("Age", "Fare")
    .filter(pl.col("Age") > 30)
    .select(
        pl.col("Age").mean().alias("Age_mean"),
        pl.col("Fare").mean().alias("Fare_mean")
    )
    .collect()
)

print(result)

Output:

shape: (1, 2)
┌───────────┬───────────┐
│ Age_mean  ┆ Fare_mean │
│ ---       ┆ ---       │
│ f64       ┆ f64       │
╞═══════════╪═══════════╡
│ 42.927869 ┆ 42.35291  │
└───────────┴───────────┘

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.scan_csv("data/titanic.csv")

result = df.select(["Age", "Fare"]).filter(pl.col("Age") > 30).mean()
result.collect()

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.scan_csv("data/titanic.csv").select(["Age", "Fare"]).filter(pl.col("Age") > 30).mean().collect()

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.scan_csv("data/titanic.csv")

result = (
    df
    .select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .select([
        pl.col("Age").mean(),
        pl.col("Fare").mean()
    ])
    .collect()
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.scan_csv("data/titanic.csv")
result = df.lazy().select(['Age', 'Fare']).filter(pl.col('Age') > 30).agg(['mean']).collect()
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpakwcz91i.py", line 6, in 
    result = df.lazy().select(['Age', 'Fare']).filter(pl.col('Age') > 30).agg(['mean']).collect()
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'LazyFrame' object has no attribute 'agg'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.scan_csv("data/titanic.csv")
result = df.lazy().select(["Age", "Fare"]).filter(pl.col("Age") > 30).agg(pl.col("*").mean()).collect()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmppzh8e7cn.py", line 6, in 
    result = df.lazy().select(["Age", "Fare"]).filter(pl.col("Age") > 30).agg(pl.col("*").mean()).collect()
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'LazyFrame' object has no attribute 'agg'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.scan_csv("data/titanic.csv")

lazy_df = df.lazy()
result = lazy_df.select(["Age", "Fare"]).filter(pl.col("Age") > 30).agg([
    pl.col("Age").mean(),
    pl.col("Fare").mean()
]).collect()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmphnckuyoz.py", line 9, in 
    result = lazy_df.select(["Age", "Fare"]).filter(pl.col("Age") > 30).agg([
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'LazyFrame' object has no attribute 'agg'

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select(["Age", "Fare"]) \
  .filter(pl.col("Age") > 30) \
  .select([
      pl.col("Age").mean().alias("avg_age"),
      pl.col("Fare").mean().alias("avg_fare")
  ]) \
  .collect()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpz50tlvsm.py", line 9, in 
    .collect()
     ^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'collect'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

result = (
    df.select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .select([
        pl.col("Age").mean().alias("mean_age"),
        pl.col("Fare").mean().alias("mean_fare")
    ])
    .collect()
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpbn98jt16.py", line 12, in 
    .collect()
     ^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'collect'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.scan_csv("data/titanic.csv")

result = (
    df.select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .select([
        pl.col("Age").mean().alias("avg_age"),
        pl.col("Fare").mean().alias("avg_fare")
    ])
    .collect()
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = (
    df.select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .select([pl.mean("Age").alias("mean_age"), pl.mean("Fare").alias("mean_fare")])
    .collect()
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpb5j2pt43.py", line 7, in 
    .collect()
     ^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'collect'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.select(["Age", "Fare"]).filter(pl.col("Age") > 30).mean().collect()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp7u_8mksc.py", line 3, in 
    result = df.select(["Age", "Fare"]).filter(pl.col("Age") > 30).mean().collect()
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'collect'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.scan_csv("data/titanic.csv")

result = (
    df.lazy()
    .select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .select([pl.col("Age").mean().alias("Mean_Age"), pl.col("Fare").mean().alias("Mean_Fare")])
    .collect()
)

print(result)

Output:

shape: (1, 2)
┌───────────┬───────────┐
│ Mean_Age  ┆ Mean_Fare │
│ ---       ┆ ---       │
│ f64       ┆ f64       │
╞═══════════╪═══════════╡
│ 42.927869 ┆ 42.35291  │
└───────────┴───────────┘

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.scan_csv("data/titanic.csv")

result = (
    df.select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .select([pl.col("Age").mean(), pl.col("Fare").mean()])
)

# to execute and get the result, you can use .collect()
# print(result.collect())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpnbb4ywep.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.scan_csv("data/titanic.csv")

result = (
    df.select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .select([pl.col("Age").mean(), pl.col("Fare").mean()])
    .collect()
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpyia3hyaf.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.scan_csv("data/titanic.csv")

result = (
    df.select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .select([pl.col("Age").mean(), pl.col("Fare").mean()])
)

# To execute and get the result as a Polars DataFrame:
result_df = result.collect()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpja48iwlt.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.scan_csv("data/titanic.csv")

result = (
    df.select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .agg([pl.mean("Age"), pl.mean("Fare")])
)

result.collect()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpx38pd0th.py", line 10, in 
    .agg([pl.mean("Age"), pl.mean("Fare")])
     ^^^
AttributeError: 'LazyFrame' object has no attribute 'agg'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.scan_csv("data/titanic.csv")

result = (
    df.select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .agg([pl.col("Age").mean(), pl.col("Fare").mean()])
    .collect()
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpyz3kpter.py", line 10, in 
    .agg([pl.col("Age").mean(), pl.col("Fare").mean()])
     ^^^
AttributeError: 'LazyFrame' object has no attribute 'agg'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

# First load the data using Polars as follows
df = pl.scan_csv("data/titanic.csv")

# Using lazy mode:
# 1. Select the Age and Fare columns
# 2. Filter for passengers over 30 years old
# 3. Calculate the mean of both columns
result = (
    df.select(["Age", "Fare"])
    .filter(pl.col("Age") > 30)
    .agg(
        [
            pl.col("Age").mean(),
            pl.col("Fare").mean(),
        ]
    )
    .collect()
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpiw39nmzh.py", line 16, in 
    .agg(
     ^^^
AttributeError: 'LazyFrame' object has no attribute 'agg'

Status: ✗ Failed

select_with_multiple_conditions

Original Prompt

# First load the data using Polars as follows
import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")

# Select all string columns that start with either 'S' or 'N'
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of multiple conditions: df.select(cs.string() & cs.starts_with("A", "B"))

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
selected_columns = df.select(cs.startswith(("S", "N")) & cs.string())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp08cxpov6.py", line 3, in 
    selected_columns = df.select(cs.startswith(("S", "N")) & cs.string())
                                 ^^
NameError: name 'cs' is not defined

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
selected_df = df.select(cs.string() & cs.starts_with("S", "N"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpf5m_njuh.py", line 3, in 
    selected_df = df.select(cs.string() & cs.starts_with("S", "N"))
                            ^^
NameError: name 'cs' is not defined

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

# Select all string columns that start with either 'S' or 'N'
string_columns_sn = df.select(cs.starts_with(('S', 'N')))

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

string_cols = df.select(cs.string())
selected_cols = [col for col in string_cols.columns if col.startswith(('S', 'N'))]

result = df.select(selected_cols)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9_yq2qin.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")

result = df.select(cs.string() & (cs.starts_with("S") | cs.starts_with("N")))
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpyr2n1p4c.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

string_cols = df.select(cs.string())
cols_to_select = []
for col in string_cols.columns:
    if col.startswith('S') or col.startswith('N'):
        cols_to_select.append(col)

result = df.select(cols_to_select)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpoo53ew4z.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")

selected_cols = df.select(
    cs.string() & (cs.starts_with("S") | cs.starts_with("N"))
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")

df.select(cs.string() & cs.starts_with("S", "N"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")

df.select(cs.string().name.starts_with(('S', 'N')))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp4e2o288l.py", line 7, in 
    df.select(cs.string().name.starts_with(('S', 'N')))
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'ExprNameNameSpace' object has no attribute 'starts_with'

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")
selected_cols = df.select([col for col in df.columns if df[col].dtype == pl.String && (col.startswith('S') || col.startswith('N'))])

Error:

invalid syntax (, line 7)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")
result = df.select(cs.string() & (cs.starts_with("S") | cs.starts_with("N")))

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code:

import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.select(pl.col('^S|N').str.contains(pl.lit('S|N')))

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

string_cols = df.select(
    pl.col(cs.string_cols & (pl.col_name().str.starts_with("S") | pl.col_name().str.starts_with("N")))
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1qw6zgg_.py", line 9, in 
    pl.col(cs.string_cols & (pl.col_name().str.starts_with("S") | pl.col_name().str.starts_with("N")))
           ^^^^^^^^^^^^^^
AttributeError: module 'polars.selectors' has no attribute 'string_cols'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

string_cols = df.select(cs.string() & (cs.starts_with("S") | cs.starts_with("N")))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")

string_cols = df.select(
    [
        pl.col(c)
        for c in df.columns
        if df[c].dtype == pl.String
        and (c.startswith("S") or c.startswith("N"))
    ]
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

result = df.select(cs.by_dtype(pl.Utf8).starts_with(["S", "N"]))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpm2_im2di.py", line 8, in 
    result = df.select(cs.by_dtype(pl.Utf8).starts_with(["S", "N"]))
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: '_selector_proxy_' object has no attribute 'starts_with'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

# Select all string columns that start with either 'S' or 'N'
result = df.select(cs.string() & cs.starts_with("S", "N"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

# Select all string columns that start with either 'S' or 'N'
result = df.select(cs.by_dtype(pl.Utf8).name.starts_with(['S', 'N']))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpotj_6kf9.py", line 9, in 
    result = df.select(cs.by_dtype(pl.Utf8).name.starts_with(['S', 'N']))
                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'ExprNameNameSpace' object has no attribute 'starts_with'

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select(cs.by_dtype(pl.Utf8).starts_with(['S', 'N']))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8h2tfsdf.py", line 3, in 
    df.select(cs.by_dtype(pl.Utf8).starts_with(['S', 'N']))
              ^^
NameError: name 'cs' is not defined

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select(cs.string() & cs.starts_with(["S", "N"]))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpu1hsvhva.py", line 3, in 
    df.select(cs.string() & cs.starts_with(["S", "N"]))
              ^^
NameError: name 'cs' is not defined

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

df.select(pl.col('^[SN].*$').filter(pl.col('*').cast(pl.Utf8).is_not_null()))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9lwj4u3r.py", line 8, in 
    df.select(pl.col('^[SN].*$').filter(pl.col('*').cast(pl.Utf8).is_not_null()))
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9632, in select
    return self.lazy().select(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: ^[SN].*$

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'select' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

select_with_regex

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all columns that start with 'P' using a regex pattern
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of regex selection: df.select("^S.*$")

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
selected_columns = df.select(pl.col("^P.*"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpeo0bnp8r.py", line 5, in 
    selected_columns = df.select(pl.col("^P.*"))
                       ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9632, in select
    return self.lazy().select(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: ^P.*

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'select' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.col("^P.*$"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all columns that start with 'P' using a regex pattern
df.select(pl.col("^P.*"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpj3yblntz.py", line 7, in 
    df.select(pl.col("^P.*"))
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9632, in select
    return self.lazy().select(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: ^P.*

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'select' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

cols_starting_with_p = df.columns[pl.Series(df.columns).str.starts_with("P")]
df_p = df.select(cols_starting_with_p)

print(df_p)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp204tcbx9.py", line 7, in 
    cols_starting_with_p = df.columns[pl.Series(df.columns).str.starts_with("P")]
                           ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: list indices must be integers or slices, not Series

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.select("P.*")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwx_7ltey.py", line 7, in 
    df.select("P.*")
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9632, in select
    return self.lazy().select(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: P.*

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'select' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import re

df = pl.read_csv("data/titanic.csv")

# Select all columns that start with 'P' using a regex pattern
columns_to_select = [col for col in df.columns if re.match(r"^P", col)]
selected_df = df.select(columns_to_select)

print(selected_df)

Output:

shape: (891, 3)
┌─────────────┬────────┬───────┐
│ PassengerId ┆ Pclass ┆ Parch │
│ ---         ┆ ---    ┆ ---   │
│ i64         ┆ i64    ┆ i64   │
╞═════════════╪════════╪═══════╡
│ 1           ┆ 3      ┆ 0     │
│ 2           ┆ 1      ┆ 0     │
│ 3           ┆ 3      ┆ 0     │
│ 4           ┆ 1      ┆ 0     │
│ 5           ┆ 3      ┆ 0     │
│ …           ┆ …      ┆ …     │
│ 887         ┆ 2      ┆ 0     │
│ 888         ┆ 1      ┆ 0     │
│ 889         ┆ 3      ┆ 2     │
│ 890         ┆ 1      ┆ 0     │
│ 891         ┆ 3      ┆ 0     │
└─────────────┴────────┴───────┘

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all columns that start with 'P' using a regex pattern
p_columns = df.select(pl.col("^P.*"))
print(p_columns)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpy_p6hna2.py", line 7, in 
    p_columns = df.select(pl.col("^P.*"))
                ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9632, in select
    return self.lazy().select(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: ^P.*

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'select' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all columns that start with 'P' using a regex pattern
p_columns = df.select("^P.*$")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all columns that start with 'P' using a regex pattern
df.select(pl.col("^P.*$"))

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import re

df = pl.read_csv("data/titanic.csv")
pattern = re.compile(r'^P')
df_selected = df.select([col for col in df.columns if pattern.match(col)])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select(pl.col("^P.*$"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.colopl~ "^P")

Error:

invalid syntax. Perhaps you forgot a comma? (, line 6)

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
selected_df = df.select(pl.col("^P.*"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpl760hqhq.py", line 5, in 
    selected_df = df.select(pl.col("^P.*"))
                  ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9632, in select
    return self.lazy().select(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: ^P.*

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'select' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select("^P.*$")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select(pl.col("^P.*$"))

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
selected_df = df.select(pl.col(r"^P.*$"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select("^P.*$")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select all columns that start with 'P' using a regex pattern
df_filtered = df.select(pl.col("^P.*$"))

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

selected_df = df.select(
    [pl.col("^P.*$")]
)

print(selected_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpgf4p83b9.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select("^P.*$")
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1fi1d5uy.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

# Creating DataFrames
df = pl.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
#df = pl.read_csv("file.csv",try_parse_dates=True)
# Selection
df.select("a", "b")            # Select columns
df.select(pl.all())                # Select all columns
# Filtering
df.filter(pl.col("a") > 1)            # Basic comparison
df.filter(pl.col("b").is_in(["x", "y"]))    # Is in list
df.filter(pl.col("a").is_between(1, 2))     # Range check
#df.filter(pl.col("value").is_null())          # Check for nulls
# AND and OR conditions
df.filter((pl.col("a") > 1) & (pl.col("a") < 3))   # AND condition
df.filter((pl.col("a") > 1) | (pl.col("a") < 3))   # OR condition
df.filter(~(pl.col("a") > 1))             # NOT condition
# Expression Arithmetic
df.with_columns(
(pl.col("a") + pl.lit(1)).alias("sum"),     # Addition
(pl.col("a") - pl.lit(1)).alias("diff"),     # Subtraction
(pl.col("a") * pl.lit(1)).alias("product"),   # Multiplication
(pl.col("a") / pl.lit(1)).alias("division")   # Division
)
# Column Operations
df.with_columns(pl.lit("constant").alias("new_col")) # Add constant column
df.rename({"a": "new_a"})          # Rename columns
df.drop("b")             # Drop columns
# Sorting
df.sort("new_a", descending=True)          # Sort by column
#df.sort(["col1", "col2"], descending=[True, False])  # Multi-column sort
# Aggregation and Grouping
df.select(pl.col("new_a").sum())           # Sum values
df.select(pl.col("new_a").mean())           # Calculate mean
df.select(pl.col("new_a").len())          # Count values
df.select(pl.col("new_a").n_unique())         # Count unique values
# GroupBy operations
#df.group_by("category").agg(pl.col("value").mean())  # Group and aggregate
#df.group_by("category").agg(        # Group and aggregate
#pl.col("value").mean().name.suffix("_mean"),
#pl.col("value").min().name.suffix("_min"))
#df.group_by(["cat1", "cat2"]).agg(          # Multiple aggregations
#pl.col("val1").sum(),
#pl.col("val2").mean()
#)
# Reshaping Data
#df.pivot(values="value", index="row_var", on="col_var") # Pivot table
#df.collect().pivot(values="value", index="row_var", on="col_var", aggregate_function="mean") # Pivot lazyframe
#df.unpivot(index=["id"], on=["val1", "val2"])     # Unpivot/melt
#df.explode("list_column")                   # Explode lists
df.with_row_index("index")                  # Add row indices
# String Operations
#df.select(pl.col("text").str.len_chars())           # String length
#df.select(pl.col("text").str.contains("pattern"))       # Contains pattern
#df.select(pl.col("text").str.replace("old", "new"))      # Replace text
#df.select(pl.col("text").str.to_lowercase())         # Convert case
#df.select(pl.col("text").str.strip())             # Remove whitespace
#df.select(pl.col("text").str.split(" "))           # Split strings
# Window Functions
#df.with_columns(
#pl.col("value").sum().over("group")            # Sum by group
#)
# Working with Missing Values
#df.drop_nulls()                        # Drop null rows
#df.drop_nulls(subset=["column1", "column2"])         # Drop for specific columns
#df.fill_null(strategy="forward")               # Forward fill
#df.with_columns(
#pl.col("value").fill_null(pl.col("value").mean())     # Fill with mean
#)
#df.with_columns(
#pl.when(pl.col("value").is_null())
#.then(pl.lit(0))
#.otherwise(pl.col("value"))
#.alias("value")                      # Replace nulls conditionally
#)
# Data Type Operations
df.select(pl.col("index").cast(pl.Int64))             # Cast to type
#df.select(pl.col("date").dt.year())              # Extract year
#df.select(pl.col("date").dt.month())             # Extract month
#df.select(pl.col("date").dt.strftime("%Y-%m-%d"))       # Format date
df.schema                           # Get schema
# Advanced Features
# LazyFrame for optimized execution
lazy_df = df.lazy()
result = lazy_df.filter(pl.col("index") > 0).collect()
# Joins
#df1.join(df2, on="key", how="inner")             # Inner join
#df1.join(df2, on="key", how="left")              # Left join
#df1.join(df2, on=["key1", "key2"], how="full")        # Multiple join keys
# Sampling
df.sample(n=1)                        # Sample n rows
df.sample(frac=0.1, with_replacement=True)          # Sample fraction
# User-defined functions
df.select(pl.col("index").map_elements(lambda x: x * 2,return_dtype=pl.Int64))     # Apply function
# Efficient operations
df.select([
pl.all(),                       # All columns
pl.exclude("new_a")             # Exclude column
])
df.with_columns(pl.exclude("index").name.prefix("new_"))   # Batch operations
# Expression contexts
(
df.lazy()
.filter(pl.col("index") > 0)
.with_columns(pl.lit(1).alias("constant"))
.collect()
)                              # Method chaining
# Date and Time Operations
#df.with_columns(
#pl.col("date").dt.date()                 # Extract date
#)
#df.with_columns(
#pl.col("date").dt.time()                 # Extract time
#)
pl.date_range(datetime(2023, 1, 1), datetime(2023, 3, 31), interval="1d", eager=True) # Create a date range Series
import polars as pl

df = pl.read_csv("data/titanic.csv")

p_columns = [col for col in df.columns if col.startswith('P')]

df_p = df.select(p_columns)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpe9rwyqbn.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

select_with_row_index

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Add a row index column named 'row_nr' and select only rows where row_nr < 5
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of adding a row index: df.with_row_index("index_col")

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.arange(0, df.height).alias('row_nr')).filter(pl.col('row_nr') < 5)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp6i9114r0.py", line 6, in 
    df = df.with_column(pl.arange(0, df.height).alias('row_nr')).filter(pl.col('row_nr') < 5)
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_row_index("row_nr").filter(pl.col("row_nr") < 5)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_row_index("row_nr").filter(pl.col("row_nr") < 5)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Add row index column and filter rows
df_subset = df.with_row_index("row_nr").filter(pl.col("row_nr") < 5)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_with_index = df.with_row_index("row_nr")
result = df_with_index.filter(pl.col("row_nr") < 5)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.with_row_index("row_nr").filter(pl.col("row_nr") < 5)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_row_count("row_nr").filter(pl.col("row_nr") < 5)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp5002q9gr.py:6: DeprecationWarning: `DataFrame.with_row_count` is deprecated. Use `with_row_index` instead. Note that the default column name has changed from 'row_nr' to 'index'.
  df = df.with_row_count("row_nr").filter(pl.col("row_nr") < 5)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_row_index("row_nr").filter(pl.col("row_nr") < 5)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.with_row_index("row_nr").filter(pl.col("row_nr") < 5)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_row_index().filter(pl.col("row_nr") < 5)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfcp4dhye.py", line 7, in 
    df = df.with_row_index().filter(pl.col("row_nr") < 5)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 5101, in filter
    return self.lazy().filter(*predicates, **constraints).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: unable to find column "row_nr"; valid columns: ["index", "PassengerId", "Survived", "Pclass", "Name", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked"]

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'filter' <---
DF ["index", "PassengerId", "Survived", "Pclass", ...]; PROJECT */13 COLUMNS

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_row_index("row_nr")
df = df.filter(pl.col("row_nr") < 5)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_row_index("row_nr")
df = df.filter(pl.col("row_nr") < 5)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_row_count(name='row_nr').filter(pl.col('row_nr') < 5)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmppaqbn2z0.py:3: DeprecationWarning: `DataFrame.with_row_count` is deprecated. Use `with_row_index` instead. Note that the default column name has changed from 'row_nr' to 'index'.
  df = df.with_row_count(name='row_nr').filter(pl.col('row_nr') < 5)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_with_index = df.with_row_count("row_nr")
result = df_with_index.filter(pl.col("row_nr") < 5)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpuusmacuq.py:3: DeprecationWarning: `DataFrame.with_row_count` is deprecated. Use `with_row_index` instead. Note that the default column name has changed from 'row_nr' to 'index'.
  df_with_index = df.with_row_count("row_nr")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df_with_index = df.with_row_count(name="row_nr")
result = df_with_index.filter(pl.col("row_nr") < 5)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpsju3lats.py:6: DeprecationWarning: `DataFrame.with_row_count` is deprecated. Use `with_row_index` instead. Note that the default column name has changed from 'row_nr' to 'index'.
  df_with_index = df.with_row_count(name="row_nr")

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    df = df.with_row_count(name='row_nr')
    df = df.filter(pl.col('row_nr') < 5)
    return df

if __name__ == "__main__":
    print(solve())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpihqx_gtp.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_row_index("row_nr")
df = df.filter(pl.col("row_nr") < 5)
print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp5tlhmx5t.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_row_index("row_nr").filter(pl.col("row_nr") < 5)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpzrc1803q.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_row_index("row_nr").filter(pl.col("row_nr") < 5)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_row_index("row_nr").filter(pl.col("row_nr") < 5)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.with_row_index("row_nr").filter(pl.col("row_nr") < 5)

Status: ✓ Success

select_with_selectors

Original Prompt

# First load the data using Polars as follows
import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")

# Use the selectors API to:
# 1. Select all numeric columns
# 2. Exclude the PassengerId column
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of using selectors: df.select(cs.numeric() - cs.by_name("col_name"))

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
selected_df = df.select(cs.numeric() & ~cs.by_name("PassengerId"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpqm9g_od5.py", line 3, in 
    selected_df = df.select(cs.numeric() & ~cs.by_name("PassengerId"))
                            ^^
NameError: name 'cs' is not defined

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.select(cs.numeric() - cs.by_name("PassengerId"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8p894qco.py", line 3, in 
    result = df.select(cs.numeric() - cs.by_name("PassengerId"))
                       ^^
NameError: name 'cs' is not defined

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

# Use the selectors API to:
# 1. Select all numeric columns
# 2. Exclude the PassengerId column
numeric_columns_except_passenger_id = df.select(cs.numeric() & ~cs.column("PassengerId"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpbbyvy2hi.py", line 11, in 
    numeric_columns_except_passenger_id = df.select(cs.numeric() & ~cs.column("PassengerId"))
                                                                    ^^^^^^^^^
AttributeError: module 'polars.selectors' has no attribute 'column'. Did you mean: 'is_column'?

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

# 1. Select all numeric columns
# 2. Exclude the PassengerId column
numeric_columns = df.select(
    cs.numeric().exclude("PassengerId")
)

print(numeric_columns)

Output:

shape: (891, 6)
┌──────────┬────────┬──────┬───────┬───────┬─────────┐
│ Survived ┆ Pclass ┆ Age  ┆ SibSp ┆ Parch ┆ Fare    │
│ ---      ┆ ---    ┆ ---  ┆ ---   ┆ ---   ┆ ---     │
│ i64      ┆ i64    ┆ f64  ┆ i64   ┆ i64   ┆ f64     │
╞══════════╪════════╪══════╪═══════╪═══════╪═════════╡
│ 0        ┆ 3      ┆ 22.0 ┆ 1     ┆ 0     ┆ 7.25    │
│ 1        ┆ 1      ┆ 38.0 ┆ 1     ┆ 0     ┆ 71.2833 │
│ 1        ┆ 3      ┆ 26.0 ┆ 0     ┆ 0     ┆ 7.925   │
│ 1        ┆ 1      ┆ 35.0 ┆ 1     ┆ 0     ┆ 53.1    │
│ 0        ┆ 3      ┆ 35.0 ┆ 0     ┆ 0     ┆ 8.05    │
│ …        ┆ …      ┆ …    ┆ …     ┆ …     ┆ …       │
│ 0        ┆ 2      ┆ 27.0 ┆ 0     ┆ 0     ┆ 13.0    │
│ 1        ┆ 1      ┆ 19.0 ┆ 0     ┆ 0     ┆ 30.0    │
│ 0        ┆ 3      ┆ null ┆ 1     ┆ 2     ┆ 23.45   │
│ 1        ┆ 1      ┆ 26.0 ┆ 0     ┆ 0     ┆ 30.0    │
│ 0        ┆ 3      ┆ 32.0 ┆ 0     ┆ 0     ┆ 7.75    │
└──────────┴────────┴──────┴───────┴───────┴─────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

# Select all numeric columns and exclude PassengerId
numeric_cols = df.select(cs.numeric() - cs.by_name("PassengerId"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

# 1. Select all numeric columns
# 2. Exclude the PassengerId column
df_selected = df.select(
    cs.numeric() & ~cs.by_name("PassengerId")
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")
numeric_cols = df.select([cs.coluginity(pl.Int64, pl.Float64, exclude=['PassengerId'])])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpufyvl6bh.py", line 7, in 
    numeric_cols = df.select([cs.coluginity(pl.Int64, pl.Float64, exclude=['PassengerId'])])
                              ^^^^^^^^^^^^^
AttributeError: module 'polars.selectors' has no attribute 'coluginity'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")
numeric_cols = df.select(cs.numeric() - cs.by_name("PassengerId"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

df = df.select([cs.col(pl.DataType.NUMERIC).exclude("PassengerId")])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9obmoke1.py", line 9, in 
    df = df.select([cs.col(pl.DataType.NUMERIC).exclude("PassengerId")])
                    ^^^^^^
AttributeError: module 'polars.selectors' has no attribute 'col'

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")

numeric_cols = df.select(cs.numeric())
result = numeric_cols.select(pl.exclude("PassengerId"))

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9o_hcu9f.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")

numeric_cols_except_passenger_id = df.select(cs.numeric() - cs.by_name("PassengerId"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpm85zju_h.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

# Assuming titanic.csv is in the data directory
try:
    df = pl.read_csv("data/titanic.csv")
except FileNotFoundError:
    print("Error: titanic.csv not found in the data directory. Please ensure the file exists and the path is correct.")
    df = pl.DataFrame()  # Create an empty DataFrame to avoid further errors

if not df.is_empty():
    numeric_cols_excluding_id = df.select(cs.numeric().exclude("PassengerId"))

    print(numeric_cols_excluding_id)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpzv66bflb.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select(
    cs.numeric()
    .exclude("PassengerId")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpapg03g7f.py", line 4, in 
    cs.numeric()
    ^^
NameError: name 'cs' is not defined

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")
numeric_cols = df.select(cs.numeric() - cs.by_name("PassengerId"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

df.select([
    cs.numeric(),
    cs.numeric().exclude("PassengerId")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp27pzaevc.py", line 8, in 
    df.select([
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9632, in select
    return self.lazy().select(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.DuplicateError: the name 'Survived' is duplicate

It's possible that multiple expressions are returning the same default column name. If this is the case, try renaming the columns with `.alias("new_name")` to avoid duplicate column names.

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

numeric_cols = df.select(cs.numeric)
numeric_cols = numeric_cols.exclude("PassengerId")

print(numeric_cols)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpjtyodsa8.py", line 8, in 
    numeric_cols = df.select(cs.numeric)
                   ^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9632, in select
    return self.lazy().select(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 3862, in select
    pyexprs = parse_into_list_of_expressions(
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/parse/expr.py", line 100, in parse_into_list_of_expressions
    exprs = _parse_positional_inputs(inputs, structify=__structify)  # type: ignore[arg-type]
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/parse/expr.py", line 114, in _parse_positional_inputs
    return [parse_into_expression(e, structify=structify) for e in inputs_iter]
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/parse/expr.py", line 61, in parse_into_expression
    expr = F.lit(input, dtype=dtype)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/functions/lit.py", line 193, in lit
    return wrap_expr(plr.lit(item, allow_object, is_scalar=True))
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: cannot create expression literal for value of type function.

Hint: Pass `allow_object=True` to accept any value and create a literal of type Object.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

numeric_cols = df.select(cs.numeric() - cs.by_name("PassengerId"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs

df = pl.read_csv("data/titanic.csv")

# 1. Select all numeric columns
numeric_cols = df.select(pl.col(cs.NUMERIC))

# 2. Exclude the PassengerId column
cols_without_passengerid = df.select(pl.exclude("PassengerId"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp4c6t14iv.py", line 9, in 
    numeric_cols = df.select(pl.col(cs.NUMERIC))
                                    ^^^^^^^^^^
AttributeError: module 'polars.selectors' has no attribute 'NUMERIC'

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")

numeric_cols = df.select(
    cs.numeric() - cs.by_name("PassengerId")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")

df.select(cs.numeric() - cs.by_name("PassengerId"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.selectors as cs
df = pl.read_csv("data/titanic.csv")

# Select all numeric columns and exclude PassengerId
df.select(
    cs.numeric() - cs.by_name("PassengerId")
)

Status: ✓ Success

select_with_slice

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the first 5 rows and all columns from Name to Age (inclusive)
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of using column slices: df[:5, "col1":"col3"]

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select(pl.col("Name":"Age")).head(5)

Error:

invalid syntax (, line 3)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df[:5, "Name":"Age"]

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select(pl.col("Name"):pl.col("Age")).head(5)

Error:

invalid syntax (, line 6)

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(pl.col("Name").head(5),
                   pl.col("Sex").head(5),
                   pl.col("Age").head(5))

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpmxphyoqg.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df[:5, "Name":"Age"]

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpmgjwilmj.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(pl.col(["Name", "Sex", "Age"])).head(5)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpy9bw9jxn.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.select(pl.col("Name").to("Age")).head(5)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_v730h8v.py", line 3, in 
    result = df.select(pl.col("Name").to("Age")).head(5)
                       ^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'to'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df[:5, "Name":"Age"]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the first 5 rows and all columns from Name to Age (inclusive)
selected_df = df.select(df.columns[3:6]).head(5)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.select(
    pl.col("Name")..pl.col("Age")
).head(5)

Error:

invalid syntax (, line 8)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df[:5, "Name":"Age"]

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select(pl.all()).limit(5)
df.select(
    pl.col("Name"),
    pl.col("Sex"),
    pl.col("Age")
).limit(5)

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select(pl.col("Name").alias("Name"), 
                  pl.col("Sex").alias("Sex"), 
                  pl.col("Age").alias("Age")).head(5)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df[:5, "Name":"Age"]
print(result)

Output:

shape: (5, 3)
┌─────────────────────────────────┬────────┬──────┐
│ Name                            ┆ Sex    ┆ Age  │
│ ---                             ┆ ---    ┆ ---  │
│ str                             ┆ str    ┆ f64  │
╞═════════════════════════════════╪════════╪══════╡
│ Braund, Mr. Owen Harris         ┆ male   ┆ 22.0 │
│ Cumings, Mrs. John Bradley (Fl… ┆ female ┆ 38.0 │
│ Heikkinen, Miss. Laina          ┆ female ┆ 26.0 │
│ Futrelle, Mrs. Jacques Heath (… ┆ female ┆ 35.0 │
│ Allen, Mr. William Henry        ┆ male   ┆ 35.0 │
└─────────────────────────────────┴────────┴──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the first 5 rows and all columns from Name to Age (inclusive)
result = df.select(pl.col("Name").extend(["Sex", "Age"])).head(5)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpug4qy2sg.py", line 7, in 
    result = df.select(pl.col("Name").extend(["Sex", "Age"])).head(5)
                       ^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'extend'

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select(pl.all()[4:6]).head(5)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpcz7rv2yr.py", line 5, in 
    result = df.select(pl.all()[4:6]).head(5)
                       ~~~~~~~~^^^^^
TypeError: 'Expr' object is not subscriptable

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df[:5, "Name":"Age"]

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.all()).head(5).select(pl.col("Name", "Sex", "Age"))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
`print(df.head(5).select(pl.col('Name').alias("Name")).select(pl.col("Age")).to_pandas())`

Error:

invalid syntax (, line 3)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
print(df[:5, "Name":"Age"])

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select(pl.all()[:, pl.col("Name"):"Age"]).head(5)

Status: ✗ Failed

select_with_transformation

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the Age and Fare columns, but round Fare to 1 decimal place
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of transforming during selection: df.select([pl.col("value").round(1)])

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select([pl.col("Age"), pl.col("Fare").round(1)])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select([
    pl.col("Age"),
    pl.col("Fare").round(1)
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.select(
    pl.col("Age"),
    pl.col("Fare").round(1)
)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select([
    pl.col("Age"),
    pl.col("Fare").round(1)
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select([pl.col("Age"), pl.col("Fare").round(1)])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select(
    pl.col("Age"),
    pl.col("Fare").round(1)
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.select([
    pl.col("Age"),
    pl.col("Fare").round(1)
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.select([
    pl.col("Age"),
    pl.col("Fare").round(1)
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select(
    pl.col("Age"),
    pl.col("Fare").round(1)
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
result = df.select([
    pl.col("Age"),
    pl.col("Fare").round(1)
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_selected = df.select([
    pl.col("Age"),
    pl.col("Fare").round(1)
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
result = df.select([
    pl.col("Age"),
    pl.col("Fare").round(1).alias("Fare")
])

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    result = df.select([
        pl.col("Age"),
        pl.col("Fare").round(1)
    ])
    return result

if __name__ == "__main__":
    # Create a dummy titanic.csv for testing
    data = """PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,"Braund, Mr. Owen Harris",male,22.0,1,0,A/5 21171,7.25,,"S"
2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38.0,1,0,PC 17599,71.2833,C85,"C"
3,1,3,"Heikkinen, Miss. Laina",female,26.0,0,0,STON/O2. 3101282,7.925,,"S"
4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35.0,1,0,113803,53.1,C123,"S"
5,0,3,"Allen, Mr. William Henry",male,35.0,0,0,373450,8.05,,"S"
"""
    with open("data/titanic.csv", "w") as f:
        f.write(data)

    result_df = solve()
    print(result_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpxm8gvtsz.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_selected = df.select([
    pl.col("Age"),
    pl.col("Fare").round(1)
])

print(df_selected)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpyw_xdef6.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

# Creating DataFrames
df = pl.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
#df = pl.read_csv("file.csv",try_parse_dates=True)
# Selection
df.select("a", "b")            # Select columns
df.select(pl.all())                # Select all columns
# Filtering
df.filter(pl.col("a") > 1)            # Basic comparison
df.filter(pl.col("b").is_in(["x", "y"]))    # Is in list
df.filter(pl.col("a").is_between(1, 2))     # Range check
df.with_columns(pl.lit(None).alias("c"))
df=df.with_columns(pl.col("c").cast(pl.Utf8))
df.filter(pl.col("c").is_null())          # Check for nulls
# AND and OR conditions
df.with_columns(pl.lit(4).alias("d"))
df.with_columns(pl.lit(5).alias("e"))
df.filter((pl.col("d") > 5) & (pl.col("e") < 10))   # AND condition
df.filter((pl.col("d") > 5) | (pl.col("e") < 10))   # OR condition
df.filter(~(pl.col("d") > 5))             # NOT condition
# Expression Arithmetic
df.with_columns(
(pl.col("d") + pl.col("e")).alias("sum"),     # Addition
(pl.col("d") - pl.col("e")).alias("diff"),     # Subtraction
(pl.col("d") * pl.col("e")).alias("product"),   # Multiplication
(pl.col("d") / pl.col("e")).alias("division")   # Division
)
# Column Operations
df.with_columns(pl.lit("constant").alias("new_col")) # Add constant column
df.rename({"a": "new_a"})          # Rename columns
df.drop("b", "c")             # Drop columns
# Sorting
df.sort("new_a", descending=True)          # Sort by column
df.sort(["d", "e"], descending=[True, False])  # Multi-column sort
# Aggregation and Grouping
df.select(pl.col("d").sum())           # Sum values
df.select(pl.col("d").mean())           # Calculate mean
df.select(pl.col("d").len())          # Count values
df.select(pl.col("d").n_unique())         # Count unique values
# GroupBy operations
df.with_columns(pl.lit("cat1").alias("category"))
df.group_by("category").agg(pl.col("d").mean())  # Group and aggregate
df.group_by("category").agg(        # Group and aggregate
pl.col("d").mean().name.suffix("_mean"),
pl.col("d").min().name.suffix("_min"))
df.with_columns(pl.lit("cat2").alias("cat1"))
df.with_columns(pl.lit("cat3").alias("cat2"))
df.with_columns(pl.lit(6).alias("val1"))
df.with_columns(pl.lit(7).alias("val2"))
df.group_by(["cat1", "cat2"]).agg(          # Multiple aggregations
pl.col("val1").sum(),
pl.col("val2").mean()
)
# Reshaping Data
df.with_columns(pl.lit(8).alias("value"))
df.with_columns(pl.lit("row1").alias("row_var"))
df.with_columns(pl.lit("col1").alias("col_var"))
# Pivot operations require the data to be collected first
try:
    df.pivot(values="value", index="row_var", on="col_var") # Pivot table
except pl.exceptions.DuplicateError:
    pass
try:
    df.collect().pivot(values="value", index="row_var", on="col_var", aggregate_function="mean") # Pivot lazyframe
except pl.exceptions.DuplicateError:
    pass
df.with_columns(pl.lit("id1").alias("id"))
df.with_columns(pl.lit(9).alias("val1"))
df.with_columns(pl.lit(10).alias("val2"))
df.unpivot(index=["id"], on=["val1", "val2"])     # Unpivot/melt
df.with_columns(pl.Series("list_column",[[1,2],[3,4],[5,6],[7,8],[9,10]]))
df.explode("list_column")                   # Explode lists
df.with_row_index("index")                  # Add row indices
# String Operations
df.with_columns(pl.lit("text1").alias("text"))
df.select(pl.col("text").str.len_chars())           # String length
df.select(pl.col("text").str.contains("pattern"))       # Contains pattern
df.select(pl.col("text").str.replace("text", "new"))      # Replace text
df.select(pl.col("text").str.to_lowercase())         # Convert case
df.select(pl.col("text").str.strip())             # Remove whitespace
df.select(pl.col("text").str.split(" "))           # Split strings
# Window Functions
df.with_columns(pl.lit("group1").alias("group"))
df.with_columns(
pl.col("value").sum().over("group")            # Sum by group
)
# Working with Missing Values
df.with_columns(pl.lit(None).alias("null_col").cast(pl.Int64))
df = df.with_columns(pl.col("null_col").fill_null(strategy="forward"))
df.drop_nulls()                        # Drop null rows
df.drop_nulls(subset=["index", "d"])         # Drop for specific columns
df.fill_null(strategy="forward")               # Forward fill
df.with_columns(
pl.col("value").fill_null(pl.col("value").mean())     # Fill with mean
)
df.with_columns(
pl.when(pl.col("value").is_null())
.then(pl.lit(0))
.otherwise(pl.col("value"))
.alias("value")                      # Replace nulls conditionally
)
# Data Type Operations
df.select(pl.col("d").cast(pl.Int64))             # Cast to type
df.with_columns(pl.lit(datetime(2023, 1, 1)).alias("date"))
df.select(pl.col("date").dt.year())              # Extract year
df.select(pl.col("date").dt.month())             # Extract month
df.select(pl.col("date").dt.strftime("%Y-%m-%d"))       # Format date
df.schema                           # Get schema
# Advanced Features
# LazyFrame for optimized execution
lazy_df = df.lazy()
result = lazy_df.filter(pl.col("d") > 0).collect()
# Joins
df1 = pl.DataFrame({"key": [1, 2, 3], "val1": ["a", "b", "c"]})
df2 = pl.DataFrame({"key": [1, 2, 4], "val2": ["x", "y", "z"]})
df1.join(df2, on="key", how="inner")             # Inner join
df1.join(df2, on="key", how="left")              # Left join
df1 = pl.DataFrame({"key1": [1, 2, 3], "key2": [4, 5, 6], "val1": ["a", "b", "c"]})
df2 = pl.DataFrame({"key1": [1, 2, 4], "key2": [4, 5, 7], "val2": ["x", "y", "z"]})
df1.join(df2, on=["key1", "key2"], how="full")        # Multiple join keys
# Sampling
df.sample(n=10, seed=1)                        # Sample n rows
df.sample(frac=0.1, with_replacement=True, seed=1)          # Sample fraction
# User-defined functions
df.select(pl.col("val1").map_elements(lambda x: x ,return_dtype=pl.Utf8))     # Apply function
# Efficient operations
df.select([
pl.all(),                       # All columns
pl.exclude("text")             # Exclude column
])
df.with_columns(pl.exclude("value").name.prefix("new_"))   # Batch operations
# Expression contexts
(
df.lazy()
.filter(pl.col("d") > 0)
.with_columns(pl.lit(1).alias("constant"))
.collect()
)                              # Method chaining
# Date and Time Operations
df.with_columns(
pl.col("date").dt.date()                 # Extract date
)
df.with_columns(
pl.col("date").dt.time()                 # Extract time
)
pl.date_range(datetime(2023, 1, 1), datetime(2023, 3, 31), interval="1d", eager=True) # Create a date range Series
# First load the data using Polars as follows
#import polars as pl
#df = pl.read_csv("data/titanic.csv")

# Select the Age and Fare columns, but round Fare to 1 decimal place
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked
def solve():
    df = pl.read_csv("data/titanic.csv")
    return df.select([
        pl.col("Age"),
        pl.col("Fare").round(1),
    ])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_b6z5nsl.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select(
    pl.col("Age"),
    pl.col("Fare").round(1)
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select([
    pl.col("Age"),
    pl.col("Fare").round(1)
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(
    "Age",
    pl.col("Fare").round(1).alias("Fare")
)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.select([
    pl.col("Age"),
    pl.col("Fare").round(1)
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select([pl.col("Age"), pl.col("Fare").round(1)])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select([
    "Age",
    pl.col("Fare").round(1)
])

Status: ✓ Success

sort_basic

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort the DataFrame by Age in ascending order
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of sorting: df.sort("column_name")

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_sorted = df.sort("Age")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_sorted = df.sort("Age")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort the DataFrame by Age in ascending order
df_sorted = df.sort("Age", descending=False)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
sorted_df = df.sort("Age")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
sorted_df = df.sort("Age")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.sort("Age")

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df_sorted = df.sort("Age")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.sort("Age")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df_sorted = df.sort("Age", descending=False)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def sort_by_age(df: pl.DataFrame) -> pl.DataFrame:
    """
    Sorts the DataFrame by Age in ascending order.

    Args:
        df: The input Polars DataFrame.

    Returns:
        A new Polars DataFrame sorted by Age.
    """
    return df.sort("Age")

if __name__ == '__main__':
    df = pl.read_csv("data/titanic.csv")
    sorted_df = sort_by_age(df)
    print(sorted_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpaopopwia.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df_sorted = df.sort("Age")

print(df_sorted)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwsj8axgv.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

df = pl.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
#df = pl.read_csv("file.csv",try_parse_dates=True)
# Selection
df.select("a", "b")            # Select columns
df.select(pl.all())                # Select all columns
# Filtering
df.filter(pl.col("a") > 1)            # Basic comparison
df.filter(pl.col("b").is_in(["x", "y"]))    # Is in list
df.filter(pl.col("a").is_between(1, 2))     # Range check
df.filter(pl.col("a").is_null())          # Check for nulls
# AND and OR conditions
df.filter((pl.col("a") > 1) & (pl.col("a") < 3))   # AND condition
df.filter((pl.col("a") > 1) | (pl.col("a") < 3))   # OR condition
df.filter(~(pl.col("a") > 1))             # NOT condition
# Expression Arithmetic
df.with_columns(
(pl.col("a") + pl.lit(1)).alias("sum"),     # Addition
(pl.col("a") - pl.lit(1)).alias("diff"),     # Subtraction
(pl.col("a") * pl.lit(1)).alias("product"),   # Multiplication
(pl.col("a") / pl.lit(1)).alias("division")   # Division
)
# Column Operations
df.with_columns(pl.lit("constant").alias("new_col")) # Add constant column
df.rename({"a": "new_a"})          # Rename columns
df.drop("new_a")             # Drop columns
# Sorting
df.sort("b", descending=True)          # Sort by column
df.sort(["b", "a"], descending=[True, False])  # Multi-column sort
# Aggregation and Grouping
df.select(pl.col("a").sum())           # Sum values
df.select(pl.col("a").mean())           # Calculate mean
df.select(pl.col("a").len())          # Count values
df.select(pl.col("a").n_unique())         # Count unique values
# GroupBy operations
df.group_by("b").agg(pl.col("a").mean())  # Group and aggregate
df.group_by("b").agg(        # Group and aggregate
pl.col("a").mean().name.suffix("_mean"),
pl.col("a").min().name.suffix("_min"))
df.group_by(["b", "a"]).agg(          # Multiple aggregations
pl.col("a").sum()
)
# Reshaping Data
#df.pivot(values="value", index="row_var", on="col_var") # Pivot table
#df.collect().pivot(values="value", index="row_var", on="col_var", aggregate_function="mean") # Pivot lazyframe
#df.unpivot(index=["id"], on=["val1", "val2"])     # Unpivot/melt
#df.explode("list_column")                   # Explode lists
df.with_row_index("index")                  # Add row indices
# String Operations
df = pl.DataFrame({"text": ["abc", "def", "ghi"]})
df.select(pl.col("text").str.len_chars())           # String length
df.select(pl.col("text").str.contains("a"))       # Contains pattern
df.select(pl.col("text").str.replace("a", "b"))      # Replace text
df.select(pl.col("text").str.to_lowercase())         # Convert case
df.select(pl.col("text").str.strip())             # Remove whitespace
df.select(pl.col("text").str.split(" "))           # Split strings
# Window Functions
df = pl.DataFrame({"value": [1, 2, 3], "group": ["a", "a", "b"]})
df.with_columns(
pl.col("value").sum().over("group")            # Sum by group
)
# Working with Missing Values
df = pl.DataFrame({"value": [1, None, 3], "a":[1,2,3]})
df.drop_nulls()                        # Drop null rows
df.drop_nulls(subset=["value"])         # Drop for specific columns
df = pl.DataFrame({"value": [1, None, 3], "a":[1,2,3]})
df.fill_null(strategy="forward")               # Forward fill
df = pl.DataFrame({"value": [1, None, 3], "a":[1,2,3]})
df.with_columns(
pl.col("value").fill_null(pl.col("value").mean())     # Fill with mean
)
df = pl.DataFrame({"value": [1, None, 3], "a":[1,2,3]})
df.with_columns(
pl.when(pl.col("value").is_null())
.then(pl.lit(0))
.otherwise(pl.col("value"))
.alias("value")                      # Replace nulls conditionally
)
# Data Type Operations
df = pl.DataFrame({"a": [1, 2, 3], "date": [datetime(2023,1,1), datetime(2023,2,1), datetime(2023,3,1)]})
df.select(pl.col("a").cast(pl.Int64))             # Cast to type
df.select(pl.col("date").dt.year())              # Extract year
df.select(pl.col("date").dt.month())             # Extract month
df.select(pl.col("date").dt.strftime("%Y-%m-%d"))       # Format date
df.schema                           # Get schema
# Advanced Features
# LazyFrame for optimized execution
lazy_df = df.lazy()
result = lazy_df.filter(pl.col("a") > 0).collect()
# Joins
df1 = pl.DataFrame({"key": [1, 2, 3], "a": [4, 5, 6]})
df2 = pl.DataFrame({"key": [1, 2, 4], "b": [7, 8, 9]})
df1.join(df2, on="key", how="inner")             # Inner join
df1.join(df2, on="key", how="left")              # Left join
df1 = pl.DataFrame({"key1": [1, 2, 3], "key2": [4, 5, 6], "a": [7, 8, 9]})
df2 = pl.DataFrame({"key1": [1, 2, 4], "key2": [4, 5, 7], "b": [10, 11, 12]})
df1.join(df2, on=["key1", "key2"], how="full")        # Multiple join keys
# Sampling
df = pl.DataFrame({"a": range(100)})
df.sample(n=10)                        # Sample n rows
df.sample(frac=0.1, with_replacement=True)          # Sample fraction
# User-defined functions
df = pl.DataFrame({"a": [1, 2, 3]})
df.select(pl.col("a").map_elements(lambda x: x * 2,return_dtype=pl.Int64))     # Apply function
# Efficient operations
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
df.select([
pl.all(),                       # All columns
pl.exclude("b")             # Exclude column
])
df.with_columns(pl.exclude("a").name.prefix("new_"))   # Batch operations
# Expression contexts
df = pl.DataFrame({"a": [1, 2, 3]})
(
df.lazy()
.filter(pl.col("a") > 0)
.with_columns(pl.lit(1).alias("constant"))
.collect()
)                              # Method chaining
# Date and Time Operations
df = pl.DataFrame({"date": [datetime(2023,1,1), datetime(2023,2,1), datetime(2023,3,1)]})
df.with_columns(
pl.col("date").dt.date()                 # Extract date
)
df.with_columns(
pl.col("date").dt.time()                 # Extract time
)
pl.date_range(datetime(2023, 1, 1), datetime(2023, 3, 31), interval="1d", eager=True) # Create a date range Series

df = pl.read_csv("data/titanic.csv")
df = df.sort("Age", descending=False)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_vsrtsx5.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Sort the DataFrame by Age in ascending order
sorted_df = df.sort("Age")

# Display the sorted DataFrame
print(sorted_df)

Output:

shape: (891, 12)
┌─────────────┬──────────┬────────┬────────────────────┬───┬──────────┬─────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name               ┆ … ┆ Ticket   ┆ Fare    ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---                ┆   ┆ ---      ┆ ---     ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str                ┆   ┆ str      ┆ f64     ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪════════════════════╪═══╪══════════╪═════════╪═══════╪══════════╡
│ 6           ┆ 0        ┆ 3      ┆ Moran, Mr. James   ┆ … ┆ 330877   ┆ 8.4583  ┆ null  ┆ Q        │
│ 18          ┆ 1        ┆ 2      ┆ Williams, Mr.      ┆ … ┆ 244373   ┆ 13.0    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Charles Eugene     ┆   ┆          ┆         ┆       ┆          │
│ 20          ┆ 1        ┆ 3      ┆ Masselmani, Mrs.   ┆ … ┆ 2649     ┆ 7.225   ┆ null  ┆ C        │
│             ┆          ┆        ┆ Fatima             ┆   ┆          ┆         ┆       ┆          │
│ 27          ┆ 0        ┆ 3      ┆ Emir, Mr. Farred   ┆ … ┆ 2631     ┆ 7.225   ┆ null  ┆ C        │
│             ┆          ┆        ┆ Chehab             ┆   ┆          ┆         ┆       ┆          │
│ 29          ┆ 1        ┆ 3      ┆ O'Dwyer, Miss.     ┆ … ┆ 330959   ┆ 7.8792  ┆ null  ┆ Q        │
│             ┆          ┆        ┆ Ellen "Nellie"     ┆   ┆          ┆         ┆       ┆          │
│ …           ┆ …        ┆ …      ┆ …                  ┆ … ┆ …        ┆ …       ┆ …     ┆ …        │
│ 117         ┆ 0        ┆ 3      ┆ Connors, Mr.       ┆ … ┆ 370369   ┆ 7.75    ┆ null  ┆ Q        │
│             ┆          ┆        ┆ Patrick            ┆   ┆          ┆         ┆       ┆          │
│ 97          ┆ 0        ┆ 1      ┆ Goldschmidt, Mr.   ┆ … ┆ PC 17754 ┆ 34.6542 ┆ A5    ┆ C        │
│             ┆          ┆        ┆ George B           ┆   ┆          ┆         ┆       ┆          │
│ 494         ┆ 0        ┆ 1      ┆ Artagaveytia, Mr.  ┆ … ┆ PC 17609 ┆ 49.5042 ┆ null  ┆ C        │
│             ┆          ┆        ┆ Ramon              ┆   ┆          ┆         ┆       ┆          │
│ 852         ┆ 0        ┆ 3      ┆ Svensson, Mr.      ┆ … ┆ 347060   ┆ 7.775   ┆ null  ┆ S        │
│             ┆          ┆        ┆ Johan              ┆   ┆          ┆         ┆       ┆          │
│ 631         ┆ 1        ┆ 1      ┆ Barkworth, Mr.     ┆ … ┆ 27042    ┆ 30.0    ┆ A23   ┆ S        │
│             ┆          ┆        ┆ Algernon Henry …   ┆   ┆          ┆         ┆       ┆          │
└─────────────┴──────────┴────────┴────────────────────┴───┴──────────┴─────────┴───────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort the DataFrame by Age in ascending order
sorted_df = df.sort("Age")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

sorted_df = df.sort("Age")

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.sort("Age")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.sort("Age")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.sort("Age", descending=False)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.sort("Age")

print(df)

Output:

shape: (891, 12)
┌─────────────┬──────────┬────────┬────────────────────┬───┬──────────┬─────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name               ┆ … ┆ Ticket   ┆ Fare    ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---                ┆   ┆ ---      ┆ ---     ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str                ┆   ┆ str      ┆ f64     ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪════════════════════╪═══╪══════════╪═════════╪═══════╪══════════╡
│ 6           ┆ 0        ┆ 3      ┆ Moran, Mr. James   ┆ … ┆ 330877   ┆ 8.4583  ┆ null  ┆ Q        │
│ 18          ┆ 1        ┆ 2      ┆ Williams, Mr.      ┆ … ┆ 244373   ┆ 13.0    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Charles Eugene     ┆   ┆          ┆         ┆       ┆          │
│ 20          ┆ 1        ┆ 3      ┆ Masselmani, Mrs.   ┆ … ┆ 2649     ┆ 7.225   ┆ null  ┆ C        │
│             ┆          ┆        ┆ Fatima             ┆   ┆          ┆         ┆       ┆          │
│ 27          ┆ 0        ┆ 3      ┆ Emir, Mr. Farred   ┆ … ┆ 2631     ┆ 7.225   ┆ null  ┆ C        │
│             ┆          ┆        ┆ Chehab             ┆   ┆          ┆         ┆       ┆          │
│ 29          ┆ 1        ┆ 3      ┆ O'Dwyer, Miss.     ┆ … ┆ 330959   ┆ 7.8792  ┆ null  ┆ Q        │
│             ┆          ┆        ┆ Ellen "Nellie"     ┆   ┆          ┆         ┆       ┆          │
│ …           ┆ …        ┆ …      ┆ …                  ┆ … ┆ …        ┆ …       ┆ …     ┆ …        │
│ 117         ┆ 0        ┆ 3      ┆ Connors, Mr.       ┆ … ┆ 370369   ┆ 7.75    ┆ null  ┆ Q        │
│             ┆          ┆        ┆ Patrick            ┆   ┆          ┆         ┆       ┆          │
│ 97          ┆ 0        ┆ 1      ┆ Goldschmidt, Mr.   ┆ … ┆ PC 17754 ┆ 34.6542 ┆ A5    ┆ C        │
│             ┆          ┆        ┆ George B           ┆   ┆          ┆         ┆       ┆          │
│ 494         ┆ 0        ┆ 1      ┆ Artagaveytia, Mr.  ┆ … ┆ PC 17609 ┆ 49.5042 ┆ null  ┆ C        │
│             ┆          ┆        ┆ Ramon              ┆   ┆          ┆         ┆       ┆          │
│ 852         ┆ 0        ┆ 3      ┆ Svensson, Mr.      ┆ … ┆ 347060   ┆ 7.775   ┆ null  ┆ S        │
│             ┆          ┆        ┆ Johan              ┆   ┆          ┆         ┆       ┆          │
│ 631         ┆ 1        ┆ 1      ┆ Barkworth, Mr.     ┆ … ┆ 27042    ┆ 30.0    ┆ A23   ┆ S        │
│             ┆          ┆        ┆ Algernon Henry …   ┆   ┆          ┆         ┆       ┆          │
└─────────────┴──────────┴────────┴────────────────────┴───┴──────────┴─────────┴───────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.sort("Age")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort the DataFrame by Age in ascending order
df = df.sort("Age")

Status: ✓ Success

sort_by_column

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort all columns based on the Age column values
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of sorting by a column: df.sort("column")

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df_sorted = df.sort("Age")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df_sorted = df.sort("Age")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df_sorted = df.sort("Age", descending=True)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def sort_by_age(df: pl.DataFrame) -> pl.DataFrame:
    """Sorts all columns based on the Age column values.

    Args:
        df: The input Polars DataFrame.

    Returns:
        The DataFrame sorted by Age.
    """
    return df.sort(by="Age")

if __name__ == '__main__':
    df = pl.read_csv("data/titanic.csv")
    sorted_df = sort_by_age(df)
    print(sorted_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpb_k3hl8k.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

columns = df.columns

df_sorted = df.sort("Age")

print(df_sorted)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmps3f821wi.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

sorted_df = df.sort("Age")

print(sorted_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp5mm_ztzl.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.sort("Age")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.sort("Age")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.sort("Age")

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Sort all columns based on the Age column values
sorted_df = df.sort("Age")

# Display the sorted dataframe
print(sorted_df)

Output:

shape: (891, 12)
┌─────────────┬──────────┬────────┬────────────────────┬───┬──────────┬─────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name               ┆ … ┆ Ticket   ┆ Fare    ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---                ┆   ┆ ---      ┆ ---     ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str                ┆   ┆ str      ┆ f64     ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪════════════════════╪═══╪══════════╪═════════╪═══════╪══════════╡
│ 6           ┆ 0        ┆ 3      ┆ Moran, Mr. James   ┆ … ┆ 330877   ┆ 8.4583  ┆ null  ┆ Q        │
│ 18          ┆ 1        ┆ 2      ┆ Williams, Mr.      ┆ … ┆ 244373   ┆ 13.0    ┆ null  ┆ S        │
│             ┆          ┆        ┆ Charles Eugene     ┆   ┆          ┆         ┆       ┆          │
│ 20          ┆ 1        ┆ 3      ┆ Masselmani, Mrs.   ┆ … ┆ 2649     ┆ 7.225   ┆ null  ┆ C        │
│             ┆          ┆        ┆ Fatima             ┆   ┆          ┆         ┆       ┆          │
│ 27          ┆ 0        ┆ 3      ┆ Emir, Mr. Farred   ┆ … ┆ 2631     ┆ 7.225   ┆ null  ┆ C        │
│             ┆          ┆        ┆ Chehab             ┆   ┆          ┆         ┆       ┆          │
│ 29          ┆ 1        ┆ 3      ┆ O'Dwyer, Miss.     ┆ … ┆ 330959   ┆ 7.8792  ┆ null  ┆ Q        │
│             ┆          ┆        ┆ Ellen "Nellie"     ┆   ┆          ┆         ┆       ┆          │
│ …           ┆ …        ┆ …      ┆ …                  ┆ … ┆ …        ┆ …       ┆ …     ┆ …        │
│ 117         ┆ 0        ┆ 3      ┆ Connors, Mr.       ┆ … ┆ 370369   ┆ 7.75    ┆ null  ┆ Q        │
│             ┆          ┆        ┆ Patrick            ┆   ┆          ┆         ┆       ┆          │
│ 97          ┆ 0        ┆ 1      ┆ Goldschmidt, Mr.   ┆ … ┆ PC 17754 ┆ 34.6542 ┆ A5    ┆ C        │
│             ┆          ┆        ┆ George B           ┆   ┆          ┆         ┆       ┆          │
│ 494         ┆ 0        ┆ 1      ┆ Artagaveytia, Mr.  ┆ … ┆ PC 17609 ┆ 49.5042 ┆ null  ┆ C        │
│             ┆          ┆        ┆ Ramon              ┆   ┆          ┆         ┆       ┆          │
│ 852         ┆ 0        ┆ 3      ┆ Svensson, Mr.      ┆ … ┆ 347060   ┆ 7.775   ┆ null  ┆ S        │
│             ┆          ┆        ┆ Johan              ┆   ┆          ┆         ┆       ┆          │
│ 631         ┆ 1        ┆ 1      ┆ Barkworth, Mr.     ┆ … ┆ 27042    ┆ 30.0    ┆ A23   ┆ S        │
│             ┆          ┆        ┆ Algernon Henry …   ┆   ┆          ┆         ┆       ┆          │
└─────────────┴──────────┴────────┴────────────────────┴───┴──────────┴─────────┴───────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

sorted_df = df.sort("Age")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort all columns based on the Age column values
sorted_df = df.sort("Age")

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.sort("Age")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.sort("Age")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.sort("Age")

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_sorted = df.sort("Age")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_sorted = df.sort("Age")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
sorted_df = df.sort("Age")

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_sorted = df.sort("Age")
df_sorted

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.sort("Age")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_sorted = df.sort("Age")

Status: ✓ Success

sort_expression

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort all columns by their values using an expression
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of sorting with an expression: df.select(pl.all().sort())

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

sorted_df = df.sort(by=[pl.all()])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.select(pl.all().sort())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort all columns by their values
df.sort(by=[pl.all()])

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Sort all columns alphabetically
sorted_df = df.select(sorted(df.columns))

# Print the result
print(sorted_df)

Output:

shape: (891, 12)
┌──────┬───────┬──────────┬─────────┬───┬────────┬───────┬──────────┬──────────────────┐
│ Age  ┆ Cabin ┆ Embarked ┆ Fare    ┆ … ┆ Sex    ┆ SibSp ┆ Survived ┆ Ticket           │
│ ---  ┆ ---   ┆ ---      ┆ ---     ┆   ┆ ---    ┆ ---   ┆ ---      ┆ ---              │
│ f64  ┆ str   ┆ str      ┆ f64     ┆   ┆ str    ┆ i64   ┆ i64      ┆ str              │
╞══════╪═══════╪══════════╪═════════╪═══╪════════╪═══════╪══════════╪══════════════════╡
│ 22.0 ┆ null  ┆ S        ┆ 7.25    ┆ … ┆ male   ┆ 1     ┆ 0        ┆ A/5 21171        │
│ 38.0 ┆ C85   ┆ C        ┆ 71.2833 ┆ … ┆ female ┆ 1     ┆ 1        ┆ PC 17599         │
│ 26.0 ┆ null  ┆ S        ┆ 7.925   ┆ … ┆ female ┆ 0     ┆ 1        ┆ STON/O2. 3101282 │
│ 35.0 ┆ C123  ┆ S        ┆ 53.1    ┆ … ┆ female ┆ 1     ┆ 1        ┆ 113803           │
│ 35.0 ┆ null  ┆ S        ┆ 8.05    ┆ … ┆ male   ┆ 0     ┆ 0        ┆ 373450           │
│ …    ┆ …     ┆ …        ┆ …       ┆ … ┆ …      ┆ …     ┆ …        ┆ …                │
│ 27.0 ┆ null  ┆ S        ┆ 13.0    ┆ … ┆ male   ┆ 0     ┆ 0        ┆ 211536           │
│ 19.0 ┆ B42   ┆ S        ┆ 30.0    ┆ … ┆ female ┆ 0     ┆ 1        ┆ 112053           │
│ null ┆ null  ┆ S        ┆ 23.45   ┆ … ┆ female ┆ 1     ┆ 0        ┆ W./C. 6607       │
│ 26.0 ┆ C148  ┆ C        ┆ 30.0    ┆ … ┆ male   ┆ 0     ┆ 1        ┆ 111369           │
│ 32.0 ┆ null  ┆ Q        ┆ 7.75    ┆ … ┆ male   ┆ 0     ┆ 0        ┆ 370376           │
└──────┴───────┴──────────┴─────────┴───┴────────┴───────┴──────────┴──────────────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

sorted_df = df.select(pl.all().sort())
print(sorted_df)

Output:

shape: (891, 12)
┌─────────────┬──────────┬────────┬────────────────┬───┬─────────────┬──────────┬───────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name           ┆ … ┆ Ticket      ┆ Fare     ┆ Cabin ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---            ┆   ┆ ---         ┆ ---      ┆ ---   ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str            ┆   ┆ str         ┆ f64      ┆ str   ┆ str      │
╞═════════════╪══════════╪════════╪════════════════╪═══╪═════════════╪══════════╪═══════╪══════════╡
│ 1           ┆ 0        ┆ 1      ┆ Abbing, Mr.    ┆ … ┆ 110152      ┆ 0.0      ┆ null  ┆ null     │
│             ┆          ┆        ┆ Anthony        ┆   ┆             ┆          ┆       ┆          │
│ 2           ┆ 0        ┆ 1      ┆ Abbott, Mr.    ┆ … ┆ 110152      ┆ 0.0      ┆ null  ┆ null     │
│             ┆          ┆        ┆ Rossmore       ┆   ┆             ┆          ┆       ┆          │
│             ┆          ┆        ┆ Edward         ┆   ┆             ┆          ┆       ┆          │
│ 3           ┆ 0        ┆ 1      ┆ Abbott, Mrs.   ┆ … ┆ 110152      ┆ 0.0      ┆ null  ┆ C        │
│             ┆          ┆        ┆ Stanton (Rosa  ┆   ┆             ┆          ┆       ┆          │
│             ┆          ┆        ┆ Hun…           ┆   ┆             ┆          ┆       ┆          │
│ 4           ┆ 0        ┆ 1      ┆ Abelson, Mr.   ┆ … ┆ 110413      ┆ 0.0      ┆ null  ┆ C        │
│             ┆          ┆        ┆ Samuel         ┆   ┆             ┆          ┆       ┆          │
│ 5           ┆ 0        ┆ 1      ┆ Abelson, Mrs.  ┆ … ┆ 110413      ┆ 0.0      ┆ null  ┆ C        │
│             ┆          ┆        ┆ Samuel (Hannah ┆   ┆             ┆          ┆       ┆          │
│             ┆          ┆        ┆ W…             ┆   ┆             ┆          ┆       ┆          │
│ …           ┆ …        ┆ …      ┆ …              ┆ … ┆ …           ┆ …        ┆ …     ┆ …        │
│ 887         ┆ 1        ┆ 3      ┆ de Mulder, Mr. ┆ … ┆ W./C. 6609  ┆ 263.0    ┆ G6    ┆ S        │
│             ┆          ┆        ┆ Theodore       ┆   ┆             ┆          ┆       ┆          │
│ 888         ┆ 1        ┆ 3      ┆ de Pelsmaeker, ┆ … ┆ W.E.P. 5734 ┆ 263.0    ┆ G6    ┆ S        │
│             ┆          ┆        ┆ Mr. Alfons     ┆   ┆             ┆          ┆       ┆          │
│ 889         ┆ 1        ┆ 3      ┆ del Carlo, Mr. ┆ … ┆ W/C 14208   ┆ 512.3292 ┆ G6    ┆ S        │
│             ┆          ┆        ┆ Sebastiano     ┆   ┆             ┆          ┆       ┆          │
│ 890         ┆ 1        ┆ 3      ┆ van Billiard,  ┆ … ┆ WE/P 5735   ┆ 512.3292 ┆ G6    ┆ S        │
│             ┆          ┆        ┆ Mr. Austin     ┆   ┆             ┆          ┆       ┆          │
│             ┆          ┆        ┆ Blyle…         ┆   ┆             ┆          ┆       ┆          │
│ 891         ┆ 1        ┆ 3      ┆ van Melkebeke, ┆ … ┆ WE/P 5735   ┆ 512.3292 ┆ T     ┆ S        │
│             ┆          ┆        ┆ Mr. Philemon   ┆   ┆             ┆          ┆       ┆          │
└─────────────┴──────────┴────────┴────────────────┴───┴─────────────┴──────────┴───────┴──────────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

sorted_df = df.select([
    col.sort() for col in pl.all()
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpilv8lsqz.py", line 6, in 
    sorted_df = df.select([
                          ^
TypeError: 'Expr' object is not iterable

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df.sort(by=pl.col('*'))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.select([
    pl.col("PassengerId").sort(),
    pl.col("Survived").sort(),
    pl.col("Pclass").sort(),
    pl.col("Name").sort(),
    pl.col("Sex").sort(),
    pl.col("Age").sort(),
    pl.col("SibSp").sort(),
    pl.col("Parch").sort(),
    pl.col("Ticket").sort(),
    pl.col("Fare").sort(),
    pl.col("Cabin").sort(),
    pl.col("Embarked").sort(),
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code for the given task:

import polars as pl

df = pl.read_csv("data/titanic.csv")

df_sorted = df.select([
    pl.col(column).arr.sort().list.alias(column) 
    for column in df.columns
])

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def sort_all_columns(df: pl.DataFrame) -> pl.DataFrame:
    """
    Sorts all columns of a Polars DataFrame by their values.

    Args:
        df: The input Polars DataFrame.

    Returns:
        A new Polars DataFrame with all columns sorted by their values.
    """
    cols = df.columns
    sorted_df = df.select([pl.col(col).sort() for col in cols])
    return sorted_df

if __name__ == '__main__':
    # Create a dummy DataFrame for testing
    data = {
        'PassengerId': [3, 1, 2],
        'Survived': [1, 0, 1],
        'Pclass': [3, 1, 2],
        'Name': ['Charlie', 'Alice', 'Bob'],
        'Sex': ['female', 'male', 'male'],
        'Age': [30, 20, 25],
        'SibSp': [0, 1, 0],
        'Parch': [0, 0, 0],
        'Ticket': ['C', 'A', 'B'],
        'Fare': [10.0, 5.0, 7.5],
        'Cabin': ['C1', 'A1', 'B1'],
        'Embarked': ['S', 'C', 'Q']
    }
    df = pl.DataFrame(data)

    # Sort all columns
    sorted_df = sort_all_columns(df)
    print(sorted_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpj3q5f_se.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def sort_all_columns(df: pl.DataFrame) -> pl.DataFrame:
    """
    Sorts all columns of a Polars DataFrame by their values using an expression.

    Args:
        df: The input Polars DataFrame.

    Returns:
        A new Polars DataFrame with all columns sorted.
    """
    return df.select(pl.all().sort())

if __name__ == '__main__':
    # Example usage with a dummy DataFrame (replace with your actual data loading)
    data = {
        'PassengerId': [3, 1, 2],
        'Survived': [0, 1, 0],
        'Pclass': [3, 1, 2],
        'Name': ['Charlie', 'Alice', 'Bob'],
        'Sex': ['male', 'female', 'male'],
        'Age': [30, 20, 25],
        'SibSp': [2, 0, 1],
        'Parch': [0, 1, 0],
        'Ticket': ['C', 'A', 'B'],
        'Fare': [10.0, 20.0, 15.0],
        'Cabin': ['C1', 'A1', 'B1'],
        'Embarked': ['S', 'C', 'Q']
    }
    df = pl.DataFrame(data)

    sorted_df = sort_all_columns(df)
    print(sorted_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfwrejxxy.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.select(
    [pl.col(c).sort() for c in df.columns]
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp3tquo4jc.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

sorted_df = df.select([
    pl.col('PassengerId').sort(),
    pl.col('Survived').sort(),
    pl.col('Pclass').sort(),
    pl.col('Name').sort(),
    pl.col('Sex').sort(),
    pl.col('Age').sort(),
    pl.col('SibSp').sort(),
    pl.col('Parch').sort(),
    pl.col('Ticket').sort(),
    pl.col('Fare').sort(),
    pl.col('Cabin').sort(),
    pl.col('Embarked').sort()
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

sorted_df = df.select([
    pl.col("PassengerId").sort(),
    pl.col("Survived").sort(),
    pl.col("Pclass").sort(),
    pl.col("Name").sort(),
    pl.col("Sex").sort(),
    pl.col("Age").sort(),
    pl.col("SibSp").sort(),
    pl.col("Parch").sort(),
    pl.col("Ticket").sort(),
    pl.col("Fare").sort(),
    pl.col("Cabin").sort(),
    pl.col("Embarked").sort()
])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.select([
    pl.all().sort()
])

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.sort([
    pl.col("PassengerId"),
    pl.col("Survived"),
    pl.col("Pclass"),
    pl.col("Name"),
    pl.col("Sex"),
    pl.col("Age"),
    pl.col("SibSp"),
    pl.col("Parch"),
    pl.col("Ticket"),
    pl.col("Fare"),
    pl.col("Cabin"),
    pl.col("Embarked")
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.select(pl.all().sort())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.sort(
    [
        pl.col("PassengerId"),
        pl.col("Survived"),
        pl.col("Pclass"),
        pl.col("Name"),
        pl.col("Sex"),
        pl.col("Age"),
        pl.col("SibSp"),
        pl.col("Parch"),
        pl.col("Ticket"),
        pl.col("Fare"),
        pl.col("Cabin"),
        pl.col("Embarked"),
    ]
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_sorted = df.sort([pl.col(col) for col in df.columns])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_sorted = df.select(pl.all().sort())

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Sort all columns by their values using an expression
df_sorted = df.with_columns(
    [pl.col(col).sort().alias(col) for col in df.columns]
)

Status: ✓ Success

sort_multiple_columns

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort the DataFrame by:
# 1. Pclass in ascending order
# 2. Fare in descending order
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of sorting by multiple columns: df.sort(["col1", "col2"], descending=[False, True])

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.sort(["Pclass", "Fare"], ascending=[True, False])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmppoln8ymr.py", line 7, in 
    df = df.sort(["Pclass", "Fare"], ascending=[True, False])
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: DataFrame.sort() got an unexpected keyword argument 'ascending'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.sort(["Pclass", "Fare"], descending=[False, True])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.sort(["Pclass", "Fare"], ascending=[True, False])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpp1xoec4e.py", line 6, in 
    df.sort(["Pclass", "Fare"], ascending=[True, False])
TypeError: DataFrame.sort() got an unexpected keyword argument 'ascending'

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def sort_dataframe(df: pl.DataFrame) -> pl.DataFrame:
    """
    Sorts the DataFrame by:
    1. Pclass in ascending order
    2. Fare in descending order
    """
    return df.sort(by=["Pclass", "Fare"], descending=[False, True])

if __name__ == '__main__':
    # Sample DataFrame (replace with your actual data loading)
    data = {
        "PassengerId": [1, 2, 3, 4, 5],
        "Survived": [0, 1, 1, 0, 1],
        "Pclass": [3, 1, 3, 1, 2],
        "Name": ["A", "B", "C", "D", "E"],
        "Sex": ["male", "female", "male", "female", "male"],
        "Age": [22.0, 38.0, 26.0, 35.0, 35.0],
        "SibSp": [1, 1, 0, 1, 0],
        "Parch": [0, 0, 0, 0, 0],
        "Ticket": ["A", "B", "C", "D", "E"],
        "Fare": [7.25, 71.28, 7.92, 53.10, 8.05],
        "Cabin": [None, "C85", None, "C123", None],
        "Embarked": ["S", "C", "S", "S", "S"],
    }
    df = pl.DataFrame(data)

    sorted_df = sort_dataframe(df)
    print(sorted_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpzft6cksi.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_sorted = df.sort(["Pclass", "Fare"], descending=[False, True])

print(df_sorted)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmps35kmb47.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

sorted_df = df.sort(by=["Pclass", "Fare"], descending=[False, True])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpvxehrtq7.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Sort the DataFrame by Pclass (ascending) and Fare (descending)
sorted_df = df.sort(by=["Pclass", "Fare"], descending=[False, True])

print(sorted_df)

Output:

shape: (891, 12)
┌─────────────┬──────────┬────────┬─────────────┬───┬──────────┬──────────┬─────────────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name        ┆ … ┆ Ticket   ┆ Fare     ┆ Cabin       ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---         ┆   ┆ ---      ┆ ---      ┆ ---         ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str         ┆   ┆ str      ┆ f64      ┆ str         ┆ str      │
╞═════════════╪══════════╪════════╪═════════════╪═══╪══════════╪══════════╪═════════════╪══════════╡
│ 680         ┆ 1        ┆ 1      ┆ Cardeza,    ┆ … ┆ PC 17755 ┆ 512.3292 ┆ B51 B53 B55 ┆ C        │
│             ┆          ┆        ┆ Mr. Thomas  ┆   ┆          ┆          ┆             ┆          │
│             ┆          ┆        ┆ Drake Mart… ┆   ┆          ┆          ┆             ┆          │
│ 738         ┆ 1        ┆ 1      ┆ Lesurer,    ┆ … ┆ PC 17755 ┆ 512.3292 ┆ B101        ┆ C        │
│             ┆          ┆        ┆ Mr. Gustave ┆   ┆          ┆          ┆             ┆          │
│             ┆          ┆        ┆ J           ┆   ┆          ┆          ┆             ┆          │
│ 259         ┆ 1        ┆ 1      ┆ Ward, Miss. ┆ … ┆ PC 17755 ┆ 512.3292 ┆ null        ┆ C        │
│             ┆          ┆        ┆ Anna        ┆   ┆          ┆          ┆             ┆          │
│ 439         ┆ 0        ┆ 1      ┆ Fortune,    ┆ … ┆ 19950    ┆ 263.0    ┆ C23 C25 C27 ┆ S        │
│             ┆          ┆        ┆ Mr. Mark    ┆   ┆          ┆          ┆             ┆          │
│ 28          ┆ 0        ┆ 1      ┆ Fortune,    ┆ … ┆ 19950    ┆ 263.0    ┆ C23 C25 C27 ┆ S        │
│             ┆          ┆        ┆ Mr. Charles ┆   ┆          ┆          ┆             ┆          │
│             ┆          ┆        ┆ Alexander   ┆   ┆          ┆          ┆             ┆          │
│ …           ┆ …        ┆ …      ┆ …           ┆ … ┆ …        ┆ …        ┆ …           ┆ …        │
│ 379         ┆ 0        ┆ 3      ┆ Betros, Mr. ┆ … ┆ 2648     ┆ 4.0125   ┆ null        ┆ C        │
│             ┆          ┆        ┆ Tannous     ┆   ┆          ┆          ┆             ┆          │
│ 303         ┆ 0        ┆ 3      ┆ Johnson,    ┆ … ┆ LINE     ┆ 0.0      ┆ null        ┆ S        │
│             ┆          ┆        ┆ Mr. William ┆   ┆          ┆          ┆             ┆          │
│             ┆          ┆        ┆ Cahoone J…  ┆   ┆          ┆          ┆             ┆          │
│ 598         ┆ 0        ┆ 3      ┆ Johnson,    ┆ … ┆ LINE     ┆ 0.0      ┆ null        ┆ S        │
│             ┆          ┆        ┆ Mr. Alfred  ┆   ┆          ┆          ┆             ┆          │
│ 272         ┆ 1        ┆ 3      ┆ Tornquist,  ┆ … ┆ LINE     ┆ 0.0      ┆ null        ┆ S        │
│             ┆          ┆        ┆ Mr. William ┆   ┆          ┆          ┆             ┆          │
│             ┆          ┆        ┆ Henry       ┆   ┆          ┆          ┆             ┆          │
│ 180         ┆ 0        ┆ 3      ┆ Leonard,    ┆ … ┆ LINE     ┆ 0.0      ┆ null        ┆ S        │
│             ┆          ┆        ┆ Mr. Lionel  ┆   ┆          ┆          ┆             ┆          │
└─────────────┴──────────┴────────┴─────────────┴───┴──────────┴──────────┴─────────────┴──────────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

sorted_df = df.sort(["Pclass", "Fare"], descending=[False, True])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

sorted_df = df.sort(by=["Pclass", "Fare"], descending=[False, True])

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.sort(["Pclass", "Fare"], reverse=[False, True])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpbl8ryf0z.py", line 6, in 
    df = df.sort(["Pclass", "Fare"], reverse=[False, True])
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: DataFrame.sort() got an unexpected keyword argument 'reverse'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.sort(["Pclass", "Fare"], descending=[False, True])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.sort(["Pclass", "Fare"], descending=[False, True])

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
sorted_df = df.sort(["Pclass", "Fare"], descending=[False, True])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
sorted_df = df.sort(["Pclass", "Fare"], descending=[False, True])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.sort(["Pclass", "Fare"], descending=[False, True])

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

sorted_df = df.sort(["Pclass", "Fare"], descending=[False, True])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.sort(["Pclass", "Fare"], descending=[False, True])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.sort(["Pclass", "Fare"], descending=[False, True])

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
sorted_df = df.sort(['Pclass', 'Fare'], reverse=[False, True])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1948hdk9.py", line 3, in 
    sorted_df = df.sort(['Pclass', 'Fare'], reverse=[False, True])
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: DataFrame.sort() got an unexpected keyword argument 'reverse'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
sorted_df = df.sort(["Pclass", "Fare"], descending=[False, True])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort the DataFrame
sorted_df = df.sort(["Pclass", "Fare"], descending=[False, True])

# Display the result
print(sorted_df)

Output:

shape: (891, 12)
┌─────────────┬──────────┬────────┬─────────────┬───┬──────────┬──────────┬─────────────┬──────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name        ┆ … ┆ Ticket   ┆ Fare     ┆ Cabin       ┆ Embarked │
│ ---         ┆ ---      ┆ ---    ┆ ---         ┆   ┆ ---      ┆ ---      ┆ ---         ┆ ---      │
│ i64         ┆ i64      ┆ i64    ┆ str         ┆   ┆ str      ┆ f64      ┆ str         ┆ str      │
╞═════════════╪══════════╪════════╪═════════════╪═══╪══════════╪══════════╪═════════════╪══════════╡
│ 680         ┆ 1        ┆ 1      ┆ Cardeza,    ┆ … ┆ PC 17755 ┆ 512.3292 ┆ B51 B53 B55 ┆ C        │
│             ┆          ┆        ┆ Mr. Thomas  ┆   ┆          ┆          ┆             ┆          │
│             ┆          ┆        ┆ Drake Mart… ┆   ┆          ┆          ┆             ┆          │
│ 738         ┆ 1        ┆ 1      ┆ Lesurer,    ┆ … ┆ PC 17755 ┆ 512.3292 ┆ B101        ┆ C        │
│             ┆          ┆        ┆ Mr. Gustave ┆   ┆          ┆          ┆             ┆          │
│             ┆          ┆        ┆ J           ┆   ┆          ┆          ┆             ┆          │
│ 259         ┆ 1        ┆ 1      ┆ Ward, Miss. ┆ … ┆ PC 17755 ┆ 512.3292 ┆ null        ┆ C        │
│             ┆          ┆        ┆ Anna        ┆   ┆          ┆          ┆             ┆          │
│ 439         ┆ 0        ┆ 1      ┆ Fortune,    ┆ … ┆ 19950    ┆ 263.0    ┆ C23 C25 C27 ┆ S        │
│             ┆          ┆        ┆ Mr. Mark    ┆   ┆          ┆          ┆             ┆          │
│ 28          ┆ 0        ┆ 1      ┆ Fortune,    ┆ … ┆ 19950    ┆ 263.0    ┆ C23 C25 C27 ┆ S        │
│             ┆          ┆        ┆ Mr. Charles ┆   ┆          ┆          ┆             ┆          │
│             ┆          ┆        ┆ Alexander   ┆   ┆          ┆          ┆             ┆          │
│ …           ┆ …        ┆ …      ┆ …           ┆ … ┆ …        ┆ …        ┆ …           ┆ …        │
│ 379         ┆ 0        ┆ 3      ┆ Betros, Mr. ┆ … ┆ 2648     ┆ 4.0125   ┆ null        ┆ C        │
│             ┆          ┆        ┆ Tannous     ┆   ┆          ┆          ┆             ┆          │
│ 303         ┆ 0        ┆ 3      ┆ Johnson,    ┆ … ┆ LINE     ┆ 0.0      ┆ null        ┆ S        │
│             ┆          ┆        ┆ Mr. William ┆   ┆          ┆          ┆             ┆          │
│             ┆          ┆        ┆ Cahoone J…  ┆   ┆          ┆          ┆             ┆          │
│ 598         ┆ 0        ┆ 3      ┆ Johnson,    ┆ … ┆ LINE     ┆ 0.0      ┆ null        ┆ S        │
│             ┆          ┆        ┆ Mr. Alfred  ┆   ┆          ┆          ┆             ┆          │
│ 272         ┆ 1        ┆ 3      ┆ Tornquist,  ┆ … ┆ LINE     ┆ 0.0      ┆ null        ┆ S        │
│             ┆          ┆        ┆ Mr. William ┆   ┆          ┆          ┆             ┆          │
│             ┆          ┆        ┆ Henry       ┆   ┆          ┆          ┆             ┆          │
│ 180         ┆ 0        ┆ 3      ┆ Leonard,    ┆ … ┆ LINE     ┆ 0.0      ┆ null        ┆ S        │
│             ┆          ┆        ┆ Mr. Lionel  ┆   ┆          ┆          ┆             ┆          │
└─────────────┴──────────┴────────┴─────────────┴───┴──────────┴──────────┴─────────────┴──────────┘

Status: ✓ Success

sort_nulls_last

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort the DataFrame by Age in ascending order, putting null values at the end
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of sorting with nulls last: df.sort("column", nulls_last=True)

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def sort_by_age(df: pl.DataFrame) -> pl.DataFrame:
    """
    Sorts the DataFrame by Age in ascending order, putting null values at the end.

    Args:
        df: The input DataFrame.

    Returns:
        The sorted DataFrame.
    """
    return df.sort(by="Age", nulls_last=True)

if __name__ == '__main__':
    df = pl.read_csv("data/titanic.csv")
    sorted_df = sort_by_age(df)
    print(sorted_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpex_9ujin.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_sorted = df.sort("Age", nulls_last=True)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpw2m36_as.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.sort("Age", descending=False, nulls_last=True)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp62rdrqg_.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.sort("Age", nulls_last=True)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.sort("Age", nulls_last=True)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df.sort("Age", descending=False, nulls_last=True)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_sorted = df.sort("Age", nulls_last=True)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_sorted = df.sort("Age", nulls_last=True)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort the DataFrame by 'Age' in ascending order, putting null values at the end
df_sorted = df.sort("Age", nulls_last=True)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Sort by Age in ascending order, with nulls at the end
sorted_df = df.sort(by="Age", nulls_last=True)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sort by Age in ascending order with nulls at the end
sorted_df = df.sort("Age", nulls_last=True)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Sort by Age in ascending order, nulls at the end
sorted_df = df.sort(by=pl.col("Age").fill_null(float('inf')))

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.sort("Age", nulls_last=True)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.sort("Age", nulls_last=True)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.sort("Age", nulls_last=True)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.sort("Age", nulls_last=True)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.sort("Age", nulls_last=True)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.sort("Age", nulls_last=True)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df_sorted = df.sort(["Age"], descending=False, nulls_last=True)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
sorted_df = df.sort("Age", nulls_last=True)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.sort("Age", descending=False, nulls_last=True)

Status: ✓ Success

split_string_into_list

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Split the Name column into a list of words
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of splitting strings into lists: df.with_columns(pl.col("text").str.split(" ").alias("words"))

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    df["Name"].str.split(" ").alias("Name_Split")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Name").str.split(" ").alias("Name_words"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Split the Name column into a list of words
df = df.with_columns(
    df["Name"].str.split(" ").alias("Name_words")
)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    pl.col("Name").str.split(" ").alias("Name_Split")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Name").str.split(" ").alias("name_parts"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Name").str.split(" ").alias("Name_Split")
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
df["Name"] = df["Name"].str.split(" ")

Error:

TypeError(msg)
TypeError: DataFrame object does not support `Series` assignment by index

Use `DataFrame.with_columns`.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.col("Name").str.split(" ").alias("words"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmptmbyci8q.py", line 6, in 
    df = df.with_column(pl.col("Name").str.split(" ").alias("words"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Name").arr.split(" ").alias("Name_words"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp7trkcf_n.py", line 8, in 
    df = df.with_columns(pl.col("Name").arr.split(" ").alias("Name_words"))
                         ^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'ExprArrayNameSpace' object has no attribute 'split'

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Split the Name column into a list of words
df_with_split_name = df.with_column(
    pl.col("Name").str.split(" ").alias("NameWords")
)

print(df_with_split_name)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpnpuudtbj.py", line 8, in 
    df_with_split_name = df.with_column(
                         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Split the Name column into a list of words
df = df.with_columns(pl.col("Name").str.split(" ").alias("NameWords"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Split the Name column into a list of words
df = df.with_columns(
    pl.col("Name").str.split(" ").alias("Name_Words")
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Name").str.split(" ").alias("Name_list")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Name").str.split(" ").alias("Name_list"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Name").str.split(" ").alias("Name_list"))

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Name").str.split(" ").alias("Name_words"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Name").str.split(" ").alias("Name_words"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Split the Name column into a list of words
df = df.with_columns(
    pl.col("Name").str.split(" ").alias("Name_words")
)

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def split_name(df: pl.DataFrame) -> pl.DataFrame:
  """Splits the 'Name' column into a list of words."""
  return df.with_columns(pl.col("Name").str.split(separator=" ").alias("Name_Words"))

if __name__ == '__main__':
  # Create a dummy DataFrame for testing
  data = {
      "PassengerId": [1, 2, 3],
      "Survived": [0, 1, 0],
      "Pclass": [3, 1, 3],
      "Name": ["Braund, Mr. Owen Harris", "Cumings, Mrs. John Bradley (Florence Briggs Thayer)", "Heikkinen, Miss. Laina"],
      "Sex": ["male", "female", "female"],
      "Age": [22.0, 38.0, 26.0],
      "SibSp": [1, 1, 0],
      "Parch": [0, 0, 0],
      "Ticket": ["A/5 21171", "PC 17599", "STON/O2. 3101282"],
      "Fare": [7.25, 71.2833, 7.925],
      "Cabin": [None, "C85", None],
      "Embarked": ["S", "C", "S"]
  }
  df = pl.DataFrame(data)

  # Split the Name column
  df_with_name_words = split_name(df)

  # Print the result
  print(df_with_name_words)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpsw014ga_.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Name").str.split(" ").alias("Name_words"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmps_693j9e.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

string_manipulation

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Create a new column 'name_length' that contains the length of each passenger's Name
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of string manipulation: df.with_columns(pl.col("text").str.len_chars().alias("text_length"))

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    (df["Name"].str.lengths()).alias("name_length")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp73r7a77b.py", line 4, in 
    (df["Name"].str.lengths()).alias("name_length")
     ^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'StringNameSpace' object has no attribute 'lengths'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Name").str.len_chars().alias("name_length"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Name").str.len_chars().alias("name_length")
)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Name").str.lengths().alias("name_length"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpmvt0w6c7.py", line 3, in 
    df = df.with_columns(pl.col("Name").str.lengths().alias("name_length"))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'ExprStringNameSpace' object has no attribute 'lengths'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Name").str.len_chars().alias("name_length"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Name").str.len_chars().alias("name_length")
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Name").str.len_chars().alias("name_length")
)

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Name").str.lengths().alias("name_length")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp792arc27.py", line 8, in 
    pl.col("Name").str.lengths().alias("name_length")
    ^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'ExprStringNameSpace' object has no attribute 'lengths'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Name").str.len_chars().alias("name_length"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Name").str.len_chars().alias("name_length")
)

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.col("Name").str.len_chars().alias("name_length"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp_dcviuho.py", line 6, in 
    df = df.with_column(pl.col("Name").str.len_chars().alias("name_length"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Create a new column 'name_length' that contains the length of each passenger's Name
df = df.with_columns(pl.col("Name").str.len_chars().alias("name_length"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# First load the data using Polars
df = pl.read_csv("data/titanic.csv")

# Create a new column 'name_length' that contains the length of each passenger's Name
df = df.with_columns(
    pl.col("Name").str.len_chars().alias("name_length")
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.col("Name").arr-str.len().alias("name_length"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpj0u8gn6f.py", line 6, in 
    df = df.with_column(pl.col("Name").arr-str.len().alias("name_length"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Name").str.len_chars().alias("name_length"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code for the task:

import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Create a new column 'name_length' that contains the length of each passenger's Name
df = df.with_columns(pl.col("Name").str.len_chars().alias("name_length"))

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Name").str.lengths().alias("name_length"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpysfswj42.py", line 6, in 
    df = df.with_columns(pl.col("Name").str.lengths().alias("name_length"))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'ExprStringNameSpace' object has no attribute 'lengths'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Name").str.len_chars().alias("name_length"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Name").str.len_chars().alias("name_length"))

Status: ✓ Success

string_to_categorical

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Convert the Sex column to categorical type
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of converting to categorical: df.with_columns(pl.col("category").cast(pl.Categorical))

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Convert the Sex column to categorical type
df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Sex").cast(pl.Categorical)
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.col("Sex").cast(pl.Categorical))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmppz4qbn4b.py", line 6, in 
    df = df.with_column(pl.col("Sex").cast(pl.Categorical))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code:


import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Convert the Sex column to categorical type
df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

# Now you can perform operations on the DataFrame

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Sex").cast(pl.Categorical).alias("Sex"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpk_bfcemr.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1sdlg_d2.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmptpnkif2o.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Convert Sex column to categorical type
df = df.with_column(pl.col("Sex").cast(pl.Categorical))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpzetg9cs_.py", line 8, in 
    df = df.with_column(pl.col("Sex").cast(pl.Categorical))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Convert the Sex column to categorical type
df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# First load the data using Polars as follows
df = pl.read_csv("data/titanic.csv")

# Convert the Sex column to categorical type
df = df.with_columns(
    pl.col("Sex").cast(pl.Categorical)
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Sex").cast(pl.Categorical))

Status: ✓ Success

struct_column_operations

Original Prompt

# First create a DataFrame with a struct column:
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

# Extract the 'name' field from the struct into a new column

Suffix Prompt

# Here is an example of struct field access: df.with_columns(pl.col("info").struct.field("name").alias("name"))

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(
    pl.col("info").struct.field("name").alias("name")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("info").struct.field("name").alias("name"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpsy_74oj_.py", line 3, in 
    df = df.with_columns(pl.col("info").struct.field("name").alias("name"))
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: info

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'with_columns' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

# Extract the 'name' field from the struct
df = df.with_columns(
    pl.col("info").struct.field("name").alias("name")
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

# Extract the 'name' field from the struct into a new column
df = df.with_column(pl.col("info").struct.field("name").alias("name"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpp17ohrq4.py", line 14, in 
    df = df.with_column(pl.col("info").struct.field("name").alias("name"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(pl.col("info").struct.field("name").alias("name"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

# Extract the 'name' field from the struct into a new column
df = df.with_columns(pl.col("info").struct.field("name").alias("name"))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(
    pl.col("info").struct.field("name").alias("name")
)

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpyhyq3nv9.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(pl.col("info").struct.field("name").alias("name"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8o811iog.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(pl.col("info").struct.field("name").alias("name"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpeb3f2y9r.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_column(pl.col("info")["name"].alias("name"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp7jz1bw9g.py", line 14, in 
    df = df.with_column(pl.col("info")["name"].alias("name"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(pl.col("info").struct.field("name").alias("name"))
print(df)

Output:

shape: (3, 3)
┌─────┬─────────────┬──────┐
│ id  ┆ info        ┆ name │
│ --- ┆ ---         ┆ ---  │
│ i64 ┆ struct[2]   ┆ str  │
╞═════╪═════════════╪══════╡
│ 1   ┆ {"John",30} ┆ John │
│ 2   ┆ {"Jane",25} ┆ Jane │
│ 3   ┆ {"Bob",35}  ┆ Bob  │
└─────┴─────────────┴──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the code to extract the 'name' field from the struct into a new column:

import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_column(pl.col("info").struct.extract("name").alias("name"))

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(pl.col("info").struct_field("name").alias("name"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmprlhvxnzz.py", line 14, in 
    df = df.with_columns(pl.col("info").struct_field("name").alias("name"))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'struct_field'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(pl.col("info").struct.field("name").alias("name"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# First create a DataFrame with a struct column:
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

# Extract the 'name' field from the struct into a new column
df = df.with_columns(pl.col("info").struct.field("name").alias("name"))

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(pl.col("info").struct.field("name").alias("name"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

result = df.with_columns(pl.col("info").struct.field("name").alias("name"))
print(result)

Output:

shape: (3, 3)
┌─────┬─────────────┬──────┐
│ id  ┆ info        ┆ name │
│ --- ┆ ---         ┆ ---  │
│ i64 ┆ struct[2]   ┆ str  │
╞═════╪═════════════╪══════╡
│ 1   ┆ {"John",30} ┆ John │
│ 2   ┆ {"Jane",25} ┆ Jane │
│ 3   ┆ {"Bob",35}  ┆ Bob  │
└─────┴─────────────┴──────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df_with_name = df.with_columns(pl.col("info").struct.field("name").alias("name"))

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(pl.col("info").struct.field("name").alias("name"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(pl.col("info").struct.field("name").alias("name"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df.with_columns(pl.col("info").struct.field("name").alias("name"))

Status: ✓ Success

sum_float_columns

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Sum all float columns row-wise
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of summing float columns: df.with_columns(pl.sum_horizontal(pl.col(pl.Float64)).alias("float_sum"))

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
float_columns = [col for col in df.columns if df[col].dtype in (pl.Float32, pl.Float64)]
df = df.with_columns(pl.sum_horizontal(pl.col(float_columns)).alias("sum_floats"))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

float_columns = df.select(pl.col(pl.Float64)).columns
df = df.with_columns(pl.sum_horizontal(pl.col(float_columns)).alias("float_sum"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

float_cols = [col for col in df.columns if df[col].dtype in (pl.Float32, pl.Float64)]
df = df.with_columns(pl.sum_horizontal(pl.col(float_cols)).alias("row_sum"))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
# Sum all float columns row-wise
float_cols = [col for col, dtype in df.dtypes.items() if dtype == pl.Float64]
df = df.with_columns(pl.sum([pl.col(c) for c in float_cols]).alias("float_sum"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp32b6dg1s.py", line 4, in 
    float_cols = [col for col, dtype in df.dtypes.items() if dtype == pl.Float64]
                                        ^^^^^^^^^^^^^^^
AttributeError: 'list' object has no attribute 'items'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Sum all float columns row-wise
result_df = df.with_columns(pl.sum_horizontal(pl.col(pl.Float64)).alias("float_sum"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Sum all float columns row-wise and add as a new column
# Assuming "Fare" and "Age" are the float columns from the available columns listed
df_with_row_sums = df.with_columns(
    (pl.col("Fare") + pl.col("Age")).alias("Float_Sum")
)

print(df_with_row_sums)

Output:

shape: (891, 13)
┌─────────────┬──────────┬────────┬───────────────────┬───┬─────────┬───────┬──────────┬───────────┐
│ PassengerId ┆ Survived ┆ Pclass ┆ Name              ┆ … ┆ Fare    ┆ Cabin ┆ Embarked ┆ Float_Sum │
│ ---         ┆ ---      ┆ ---    ┆ ---               ┆   ┆ ---     ┆ ---   ┆ ---      ┆ ---       │
│ i64         ┆ i64      ┆ i64    ┆ str               ┆   ┆ f64     ┆ str   ┆ str      ┆ f64       │
╞═════════════╪══════════╪════════╪═══════════════════╪═══╪═════════╪═══════╪══════════╪═══════════╡
│ 1           ┆ 0        ┆ 3      ┆ Braund, Mr. Owen  ┆ … ┆ 7.25    ┆ null  ┆ S        ┆ 29.25     │
│             ┆          ┆        ┆ Harris            ┆   ┆         ┆       ┆          ┆           │
│ 2           ┆ 1        ┆ 1      ┆ Cumings, Mrs.     ┆ … ┆ 71.2833 ┆ C85   ┆ C        ┆ 109.2833  │
│             ┆          ┆        ┆ John Bradley (Fl… ┆   ┆         ┆       ┆          ┆           │
│ 3           ┆ 1        ┆ 3      ┆ Heikkinen, Miss.  ┆ … ┆ 7.925   ┆ null  ┆ S        ┆ 33.925    │
│             ┆          ┆        ┆ Laina             ┆   ┆         ┆       ┆          ┆           │
│ 4           ┆ 1        ┆ 1      ┆ Futrelle, Mrs.    ┆ … ┆ 53.1    ┆ C123  ┆ S        ┆ 88.1      │
│             ┆          ┆        ┆ Jacques Heath (…  ┆   ┆         ┆       ┆          ┆           │
│ 5           ┆ 0        ┆ 3      ┆ Allen, Mr.        ┆ … ┆ 8.05    ┆ null  ┆ S        ┆ 43.05     │
│             ┆          ┆        ┆ William Henry     ┆   ┆         ┆       ┆          ┆           │
│ …           ┆ …        ┆ …      ┆ …                 ┆ … ┆ …       ┆ …     ┆ …        ┆ …         │
│ 887         ┆ 0        ┆ 2      ┆ Montvila, Rev.    ┆ … ┆ 13.0    ┆ null  ┆ S        ┆ 40.0      │
│             ┆          ┆        ┆ Juozas            ┆   ┆         ┆       ┆          ┆           │
│ 888         ┆ 1        ┆ 1      ┆ Graham, Miss.     ┆ … ┆ 30.0    ┆ B42   ┆ S        ┆ 49.0      │
│             ┆          ┆        ┆ Margaret Edith    ┆   ┆         ┆       ┆          ┆           │
│ 889         ┆ 0        ┆ 3      ┆ Johnston, Miss.   ┆ … ┆ 23.45   ┆ null  ┆ S        ┆ null      │
│             ┆          ┆        ┆ Catherine Hele…   ┆   ┆         ┆       ┆          ┆           │
│ 890         ┆ 1        ┆ 1      ┆ Behr, Mr. Karl    ┆ … ┆ 30.0    ┆ C148  ┆ C        ┆ 56.0      │
│             ┆          ┆        ┆ Howell            ┆   ┆         ┆       ┆          ┆           │
│ 891         ┆ 0        ┆ 3      ┆ Dooley, Mr.       ┆ … ┆ 7.75    ┆ null  ┆ Q        ┆ 39.75     │
│             ┆          ┆        ┆ Patrick           ┆   ┆         ┆       ┆          ┆           │
└─────────────┴──────────┴────────┴───────────────────┴───┴─────────┴───────┴──────────┴───────────┘

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def sum_float_cols_rowwise(df: pl.DataFrame) -> pl.DataFrame:
    """Sum all float columns row-wise."""
    float_cols = [col for col in df.columns if df[col].dtype == pl.Float64 or df[col].dtype == pl.Float32]
    if not float_cols:
        return pl.DataFrame()  # or raise ValueError("No float columns found in the DataFrame")
    return df.select(pl.sum(float_cols).alias("float_sum"))

if __name__ == '__main__':
    df = pl.read_csv("data/titanic.csv")
    result_df = sum_float_cols_rowwise(df)
    print(result_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpzr2b42ly.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def sum_float_cols_rowwise(df: pl.DataFrame) -> pl.DataFrame:
    """
    Sums all float columns row-wise in a Polars DataFrame.

    Args:
        df: The input Polars DataFrame.

    Returns:
        A new Polars DataFrame with an additional column 'float_sum' containing the row-wise sum of float columns.
    """
    float_cols = [col for col in df.columns if df[col].dtype == pl.Float64 or df[col].dtype == pl.Float32]
    return df.with_columns(pl.sum_horizontal(pl.col(float_cols)).alias("float_sum"))

if __name__ == '__main__':
    # Create a dummy DataFrame for testing
    data = {
        'PassengerId': [1, 2, 3, 4, 5],
        'Survived': [0, 1, 0, 1, 0],
        'Pclass': [3, 1, 3, 1, 3],
        'Name': ['A', 'B', 'C', 'D', 'E'],
        'Sex': ['male', 'female', 'male', 'female', 'male'],
        'Age': [22.0, 38.0, 26.0, 35.0, 35.0],
        'SibSp': [1, 1, 0, 1, 0],
        'Parch': [0, 0, 0, 0, 0],
        'Ticket': ['A', 'B', 'C', 'D', 'E'],
        'Fare': [7.25, 71.28, 7.92, 53.10, 8.05],
        'Cabin': ['C', 'C', 'C', 'C', 'C'],
        'Embarked': ['S', 'C', 'S', 'S', 'S']
    }
    df = pl.DataFrame(data)

    # Sum the float columns row-wise
    df_with_sum = sum_float_cols_rowwise(df)

    # Print the resulting DataFrame
    print(df_with_sum)

    # Example usage with the provided titanic.csv
    # df = pl.read_csv("data/titanic.csv") # Assuming titanic.csv is in a directory named 'data'
    # df_with_sum = sum_float_cols_rowwise(df)
    # print(df_with_sum)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp7_pqgiul.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")

    float_cols = [col for col in df.columns if df[col].dtype == pl.Float64]
    
    if float_cols:
        df = df.with_columns(pl.sum(float_cols).alias("row_sum"))
        print(df.select("row_sum"))
    else:
        print("No float columns found in the DataFrame.")

solve()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpe2yip938.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
import numpy as np

df = pl.read_csv("data/titanic.csv")

float_cols = ['Age', 'Fare']
df['sum_float_cols'] = df.select(float_cols).arr.sum(axis=1)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpj0amu88r.py", line 10, in 
    df['sum_float_cols'] = df.select(float_cols).arr.sum(axis=1)
                           ^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'arr'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
`df.with_columns(pl.sum_horizontal(pl.col(pl.Float64, exclude=['PassengerId'])).alias("float_sum"))`

Error:

invalid syntax (, line 3)

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
result = df.select(pl.col(pl.Float64).arr.sum())
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpvnpd5h3j.py", line 6, in 
    result = df.select(pl.col(pl.Float64).arr.sum())
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9632, in select
    return self.lazy().select(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.SchemaError: invalid series dtype: expected `FixedSizeList`, got `f64` for series with name `Age`

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Get all float columns
float_cols = df.select(pl.col(c) for c in df.columns if df[c].dtype in [pl.Float32, pl.Float64]).columns

# Sum all float columns row-wise
if float_cols:
    df = df.with_column(pl.sum_horizontal(float_cols).alias("float_sum"))

# Display the result with the new sum column
print(df.select(["*", "float_sum"]) if float_cols else "No float columns found")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfmkvggcx.py", line 12, in 
    df = df.with_column(pl.sum_horizontal(float_cols).alias("float_sum"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Identify float columns in the dataframe
float_cols = df.select(pl.col(pl.Float64)).columns

# Sum all float columns row-wise
df_with_sum = df.with_columns(pl.sum_horizontal(pl.col(float_cols)).alias("float_sum"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# First load the data using Polars as follows
df = pl.read_csv("data/titanic.csv")

# Sum all float columns row-wise
float_cols = df.select(pl.col([col for col in df.columns if df[col].dtype in [pl.Float32, pl.Float64]]))

# If there are no float columns detected, let's handle numeric columns instead
if float_cols.width == 0:
    # Select columns that can be cast to float
    numeric_cols = [
        col for col in df.columns 
        if df[col].dtype in [pl.Int8, pl.Int16, pl.Int32, pl.Int64, pl.UInt8, pl.UInt16, pl.UInt32, pl.UInt64, pl.Float32, pl.Float64]
        or pl.col(col).cast(pl.Float64, strict=False).is_not_null().all()
    ]
    
    if numeric_cols:
        df_with_sum = df.with_columns(
            pl.sum_horizontal(numeric_cols).alias("row_sum")
        )
    else:
        # If no numeric columns, just add a zero column
        df_with_sum = df.with_columns(pl.lit(0).alias("row_sum"))
else:
    float_col_names = float_cols.columns
    df_with_sum = df.with_columns(
        pl.sum_horizontal(float_col_names).alias("row_sum")
    )

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

float_cols = df.select(pl.col('^.*$').filter(pl.Float64)).columns
result = df.select(pl.sum_horizontal(float_cols).alias('sum_float_cols'))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp89p2bhvc.py", line 7, in 
    float_cols = df.select(pl.col('^.*$').filter(pl.Float64)).columns
                           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/expr/expr.py", line 4266, in filter
    predicate = parse_predicates_constraints_into_expression(
                ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/parse/expr.py", line 164, in parse_predicates_constraints_into_expression
    all_predicates = _parse_positional_inputs(predicates)  # type: ignore[arg-type]
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/parse/expr.py", line 114, in _parse_positional_inputs
    return [parse_into_expression(e, structify=structify) for e in inputs_iter]
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/parse/expr.py", line 61, in parse_into_expression
    expr = F.lit(input, dtype=dtype)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/functions/lit.py", line 193, in lit
    return wrap_expr(plr.lit(item, allow_object, is_scalar=True))
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: cannot create expression literal for value of type DataTypeClass.

Hint: Pass `allow_object=True` to accept any value and create a literal of type Object.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

float_columns = ['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare']

df = df.with_columns(pl.sum_horizontal(pl.col(float_columns)).alias("float_sum"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.select([
    pl.sum_horizontal([pl.col(c) for c in df.select(pl.col("*")).select(pl.all().is_float()).columns])
    .alias("sum_float_cols")
])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpk8b3740a.py", line 8, in 
    pl.sum_horizontal([pl.col(c) for c in df.select(pl.col("*")).select(pl.all().is_float()).columns])
                                                                        ^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'is_float'

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

float_cols = df.select(pl.col(c).cast(pl.Float64) for c in df.columns if df[c].dtype == pl.Float64)

df = df.with_columns(float_cols.sum_row().alias("sum_floats"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp7n_xkbtj.py", line 9, in 
    df = df.with_columns(float_cols.sum_row().alias("sum_floats"))
                         ^^^^^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'sum_row'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.sum_horizontal(pl.col(pl.Float64)).alias("float_sum")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

float_cols = [col for col in df.columns if df[col].dtype == pl.Float64]
df = df.with_columns(pl.sum_row(df.select(float_cols)).alias("sum_of_floats"))

Error:

AttributeError(msg)
AttributeError: module 'polars' has no attribute 'sum_row'

Status: ✗ Failed

survival_by_age_group

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Calculate survival rate by age groups:
# 1. Create age groups: '0-15', '16-30', '31-45', '46-60', '60+'
# 2. Group by these age groups
# 3. Calculate survival rate (mean of Survived) for each group
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of creating age groups and calculating rates:
# df.with_columns([
#     pl.when(pl.col("age") <= 15)
#     .then(pl.lit("0-15"))
#     .when(pl.col("age") <= 30)
#     .then(pl.lit("16-30"))
#     .otherwise(pl.lit("30+"))
#     .alias("age_group")
# ])

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when((pl.col("Age") >= 0) & (pl.col("Age") <= 15))
    .then(pl.lit("0-15"))
    .when((pl.col("Age") >= 16) & (pl.col("Age") <= 30))
    .then(pl.lit("16-30"))
    .when((pl.col("Age") >= 31) & (pl.col("Age") <= 45))
    .then(pl.lit("31-45"))
    .when((pl.col("Age") >= 46) & (pl.col("Age") <= 60))
    .then(pl.lit("46-60"))
    .otherwise(pl.lit("60+"))
    .alias("AgeGroup")
)

result = df.group_by("AgeGroup").agg(pl.col("Survived").mean().alias("SurvivalRate"))

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpztuu_yv_.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Age") <= 15)
    .then(pl.lit("0-15"))
    .when(pl.col("Age") <= 30)
    .then(pl.lit("16-30"))
    .when(pl.col("Age") <= 45)
    .then(pl.lit("31-45"))
    .when(pl.col("Age") <= 60)
    .then(pl.lit("46-60"))
    .otherwise(pl.lit("60+"))
    .alias("age_group")
)

result = df.group_by("age_group").agg(pl.col("Survived").mean())

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1ykq5cjf.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def calculate_survival_rate_by_age_group(df: pl.DataFrame) -> pl.DataFrame:
    """
    Calculates survival rate by age groups: '0-15', '16-30', '31-45', '46-60', '60+'.

    Args:
        df: Polars DataFrame containing Titanic dataset.

    Returns:
        Polars DataFrame with age groups and their survival rates.
    """

    df = df.with_columns(
        pl.when(pl.col("Age") <= 15)
        .then(pl.lit("0-15"))
        .when((pl.col("Age") > 15) & (pl.col("Age") <= 30))
        .then(pl.lit("16-30"))
        .when((pl.col("Age") > 30) & (pl.col("Age") <= 45))
        .then(pl.lit("31-45"))
        .when((pl.col("Age") > 45) & (pl.col("Age") <= 60))
        .then(pl.lit("46-60"))
        .otherwise(pl.lit("60+"))
        .alias("AgeGroup")
    )

    survival_rates = df.group_by("AgeGroup").agg(
        pl.col("Survived").mean().alias("SurvivalRate")
    )

    return survival_rates
if __name__ == '__main__':
    # Example Usage (replace with your actual data loading)
    try:
        df = pl.read_csv("data/titanic.csv")  # Assuming titanic.csv is in the 'data' folder
    except FileNotFoundError:
        # Create a dummy DataFrame if the file is not found
        data = {
            "PassengerId": [1, 2, 3, 4, 5],
            "Survived": [0, 1, 1, 0, 1],
            "Pclass": [3, 1, 3, 1, 3],
            "Name": ["A", "B", "C", "D", "E"],
            "Sex": ["male", "female", "female", "male", "female"],
            "Age": [22, 38, 26, 35, 35],
            "SibSp": [1, 1, 0, 1, 0],
            "Parch": [0, 0, 0, 0, 0],
            "Ticket": ["A", "B", "C", "D", "E"],
            "Fare": [7.25, 71.2833, 7.925, 53.1, 8.05],
            "Cabin": [None, "C85", None, "C123", None],
            "Embarked": ["S", "C", "S", "S", "S"]
        }
        df = pl.DataFrame(data)
        print("Using dummy DataFrame for demonstration.")

    survival_rates = calculate_survival_rate_by_age_group(df)
    print(survival_rates)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpn2xv8haq.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

survival_by_age = (
    df
    .with_columns([
        pl.when(pl.col("Age") <= 15).then(pl.lit("0-15"))
        .when(pl.col("Age") <= 30).then(pl.lit("16-30"))
        .when(pl.col("Age") <= 45).then(pl.lit("31-45"))
        .when(pl.col("Age") <= 60).then(pl.lit("46-60"))
        .otherwise(pl.lit("60+"))
        .alias("AgeGroup")
    ])
    .group_by("AgeGroup")
    .agg(
        pl.col("Survived").mean().alias("SurvivalRate"),
        pl.col("Survived").count().alias("Count")
    )
    .sort("AgeGroup")
)

print(survival_by_age)

Output:

shape: (5, 3)
┌──────────┬──────────────┬───────┐
│ AgeGroup ┆ SurvivalRate ┆ Count │
│ ---      ┆ ---          ┆ ---   │
│ str      ┆ f64          ┆ u32   │
╞══════════╪══════════════╪═══════╡
│ 0-15     ┆ 0.590361     ┆ 83    │
│ 16-30    ┆ 0.358896     ┆ 326   │
│ 31-45    ┆ 0.425743     ┆ 202   │
│ 46-60    ┆ 0.407407     ┆ 81    │
│ 60+      ┆ 0.286432     ┆ 199   │
└──────────┴──────────────┴───────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (df
    .with_columns([
        pl.when(pl.col("Age") <= 15)
        .then(pl.lit("0-15"))
        .when(pl.col("Age") <= 30)
        .then(pl.lit("16-30"))
        .when(pl.col("Age") <= 45)
        .then(pl.lit("31-45"))
        .when(pl.col("Age") <= 60)
        .then(pl.lit("46-60"))
        .otherwise(pl.lit("60+"))
        .alias("age_group")
    ])
    .group_by("age_group")
    .agg([
        pl.col("Survived").mean().alias("survival_rate"),
        pl.count().alias("count")
    ])
    .sort("age_group")
)

print(result)

Output:

shape: (5, 3)
┌───────────┬───────────────┬───────┐
│ age_group ┆ survival_rate ┆ count │
│ ---       ┆ ---           ┆ ---   │
│ str       ┆ f64           ┆ u32   │
╞═══════════╪═══════════════╪═══════╡
│ 0-15      ┆ 0.590361      ┆ 83    │
│ 16-30     ┆ 0.358896      ┆ 326   │
│ 31-45     ┆ 0.425743      ┆ 202   │
│ 46-60     ┆ 0.407407      ┆ 81    │
│ 60+       ┆ 0.286432      ┆ 199   │
└───────────┴───────────────┴───────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0g5_17jw.py:23: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("count")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

(df
 .with_columns([
     pl.when(pl.col("Age") <= 15).then(pl.lit("0-15"))
     .when(pl.col("Age").is_between(16, 30)).then(pl.lit("16-30"))
     .when(pl.col("Age").is_between(31, 45)).then(pl.lit("31-45"))
     .when(pl.col("Age").is_between(46, 60)).then(pl.lit("46-60"))
     .when(pl.col("Age") > 60).then(pl.lit("60+"))
     .alias("AgeGroup")
 ])
 .group_by("AgeGroup")
 .agg(
     pl.col("Survived").mean().alias("SurvivalRate")
 )
 .sort("AgeGroup"))

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

def age_group(age: pl.Expr) -> pl.Expr:
    return pl.when(age <= 15)
        .then(pl.lit("0-15"))
        .when(age <= 30)
        .then(pl.lit("16-30"))
        .when(age <= 45)
        .then(pl.lit("31-45"))
        .when(age <= 60)
        .then(pl.lit("46-60"))
        .otherwise(pl.lit("60+"))

df = df.with_columns(
    pl.col("Age").alias("Age")
).with_columns(
    age_group(pl.col("Age")).alias("AgeGroup")
)
survival_rates = df.group_by("AgeGroup").agg(
    pl.col("Survived").mean().alias("SurvivalRate")
)

print(survival_rates)

Error:

unexpected indent (, line 9)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns([
    pl.when(pl.col("Age") <= 15)
    .then(pl.lit("0-15"))
    .when(pl.col("Age") <= 30)
    .then(pl.lit("16-30"))
    .when(pl.col("Age") <= 45)
    .then(pl.lit("31-45"))
    .when(pl.col("Age") <= 60)
    .then(pl.lit("46-60"))
    .otherwise(pl.lit("60+"))
    .alias("age_group")
])

survival_rates = df.group_by("age_group").agg(
    pl.mean("Survived").alias("survival_rate")
)

print(survival_rates)

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

def age_group(age):
    if age <= 15:
        return "0-15"
    elif age <= 30:
        return "16-30"
    elif age <= 45:
        return "31-45"
    elif age <= 60:
        return "46-60"
    else:
        return "60+"

df = df.with_columns(pl.col("Age").map_elements(age_group).alias("AgeGroup"))

result = df.group_by("AgeGroup").agg(pl.col("Survived").mean())

print(result)

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

age_bins = [0, 15, 30, 45, 60, float('inf')]
age_labels = ['0-15', '16-30', '31-45', '46-60', '60+']

result = (
    df.with_columns(
        pl.when(pl.col("Age").is_null())
          .then(None)
          .otherwise(
              pl.cut(pl.col("Age"), bins=age_bins, labels=age_labels)
          )
          .alias("AgeGroup")
    )
    .group_by("AgeGroup")
    .agg(pl.col("Survived").mean().alias("SurvivalRate"))
    .sort("AgeGroup")
)

print(result)

Error:

AttributeError(msg)
AttributeError: module 'polars' has no attribute 'cut'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.with_columns([
    pl.when(pl.col("Age") <= 15)
    .then(pl.lit("0-15"))
    .when(pl.col("Age") <= 30)
    .then(pl.lit("16-30"))
    .when(pl.col("Age") <= 45)
    .then(pl.lit("31-45"))
    .when(pl.col("Age") <= 60)
    .then(pl.lit("46-60"))
    .otherwise(pl.lit("60+"))
    .alias("age_group")
]).group_by("age_group").agg(
    pl.col("Survived").mean().alias("survival_rate")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df.with_columns(
        pl.when(pl.col("Age") <= 15)
          .then(pl.lit("0-15"))
          .when((pl.col("Age") > 15) & (pl.col("Age") <= 30))
          .then(pl.lit("16-30"))
          .when((pl.col("Age") > 30) & (pl.col("Age") <= 45))
          .then(pl.lit("31-45"))
          .when((pl.col("Age") > 45) & (pl.col("Age") <= 60))
          .then(pl.lit("46-60"))
          .otherwise(pl.lit("60+"))
          .alias("age_group")
    )
    .group_by("age_group")
    .agg(pl.col("Survived").mean().alias("survival_rate"))
    .sort("age_group")
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

age_bins = [0, 15, 30, 45, 60, float('inf')]
age_labels = ['0-15', '16-30', '31-45', '46-60', '60+']

df = df.with_columns(
    pl.col("Age").cut(bins=age_bins, labels=age_labels).alias("AgeGroup")
)

result = df.groupby("AgeGroup").agg(
    pl.col("Survived").mean().alias("SurvivalRate")
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpqi_kje4i.py", line 11, in 
    pl.col("Age").cut(bins=age_bins, labels=age_labels).alias("AgeGroup")
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/unstable.py", line 58, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: Expr.cut() got an unexpected keyword argument 'bins'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

age_grouped_df = df.with_columns(
    pl.when(pl.col("Age") <= 15)
    .then(pl.lit("0-15"))
    .when(pl.col("Age") <= 30)
    .then(pl.lit("16-30"))
    .when(pl.col("Age") <= 45)
    .then(pl.lit("31-45"))
    .when(pl.col("Age") <= 60)
    .then(pl.lit("46-60"))
    .otherwise(pl.lit("60+"))
    .alias("age_group")
)

survival_rates = age_grouped_df.groupby("age_group").agg(
    pl.col("Survived").mean().alias("survival_rate")
)

survival_rates

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpcw9te4tu.py", line 20, in 
    survival_rates = age_grouped_df.groupby("age_group").agg(
                     ^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.when(pl.col("Age") <= 15).then("0-15")
    .when(pl.col("Age").is_between(16, 30)).then("16-30")
    .when(pl.col("Age").is_between(31, 45)).then("31-45")
    .when(pl.col("Age").is_between(46, 60)).then("46-60")
    .otherwise("60+")
    .alias("AgeGroup")
)

survival_rate_by_age_group = (
    df.group_by("AgeGroup")
    .agg(pl.col("Survived").mean().alias("Survival Rate"))
)

print(survival_rate_by_age_group)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp5h2skrci.py", line 7, in 
    df = df.with_columns(
         ^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9805, in with_columns
    return self.lazy().with_columns(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: 0-15

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'with_columns' <---
DF ["PassengerId", "Survived", "Pclass", "Name", ...]; PROJECT */12 COLUMNS

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

df['Age_Group'] = pl.when(df['Age'] <= 15).then('0-15')
    .when((df['Age'] > 15) & (df['Age'] <= 30)).then('16-30')
    .when((df['Age'] > 30) & (df['Age'] <= 45)).then('31-45')
    .when((df['Age'] > 45) & (df['Age'] <= 60)).then('46-60')
    .otherwise('60+')

result = df.groupby('Age_Group').agg(pl.mean('Survived'))
print(result)

Error:

unexpected indent (, line 9)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns([
    pl.case()
   .when(pl.col("Age") <= 15)
   .then(pl.lit("0-15"))
   .when(pl.col("Age") <= 30)
   .then(pl.lit("16-30"))
   .when(pl.col("Age") <= 45)
   .then(pl.lit("31-45"))
   .when(pl.col("Age") <= 60)
   .then(pl.lit("46-60"))
   .otherwise(pl.lit("60+"))
   .alias("age_group")
])

result = df.groupby("age_group").agg(pl.col("Survived").mean().alias("survival_rate"))
print(result)

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.cut(pl.col("Age"), bins=[0, 16, 31, 46, 61], labels=["0-15", "16-30", "31-45", "46-60", "60+"]).alias("Age_Group")
)

result = df.group_by("Age_Group").agg(pl.col("Survived").mean())

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Create age groups
age_groups = (
    df
    .with_columns(
        pl.when(pl.col("Age") <= 15).then(pl.lit("0-15"))
        .when(pl.col("Age") <= 30).then(pl.lit("16-30"))
        .when(pl.col("Age") <= 45).then(pl.lit("31-45"))
        .when(pl.col("Age") <= 60).then(pl.lit("46-60"))
        .otherwise(pl.lit("60+"))
        .alias("AgeGroup")
    )
    .group_by("AgeGroup")
    .agg(
        pl.col("Survived").mean().alias("SurvivalRate"),
        pl.col("Survived").count().alias("Count")
    )
    .sort("AgeGroup")
)

print(age_groups)

Output:

shape: (5, 3)
┌──────────┬──────────────┬───────┐
│ AgeGroup ┆ SurvivalRate ┆ Count │
│ ---      ┆ ---          ┆ ---   │
│ str      ┆ f64          ┆ u32   │
╞══════════╪══════════════╪═══════╡
│ 0-15     ┆ 0.590361     ┆ 83    │
│ 16-30    ┆ 0.358896     ┆ 326   │
│ 31-45    ┆ 0.425743     ┆ 202   │
│ 46-60    ┆ 0.407407     ┆ 81    │
│ 60+      ┆ 0.286432     ┆ 199   │
└──────────┴──────────────┴───────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df
    .with_columns([
        pl.when(pl.col("Age") <= 15)
        .then(pl.lit("0-15"))
        .when(pl.col("Age") <= 30)
        .then(pl.lit("16-30"))
        .when(pl.col("Age") <= 45)
        .then(pl.lit("31-45"))
        .when(pl.col("Age") <= 60)
        .then(pl.lit("46-60"))
        .otherwise(pl.lit("60+"))
        .alias("age_group")
    ])
    .group_by("age_group")
    .agg(
        pl.col("Survived").mean().alias("survival_rate"),
        pl.count().alias("count")
    )
    .sort("age_group")
)

print(result)

Output:

shape: (5, 3)
┌───────────┬───────────────┬───────┐
│ age_group ┆ survival_rate ┆ count │
│ ---       ┆ ---           ┆ ---   │
│ str       ┆ f64           ┆ u32   │
╞═══════════╪═══════════════╪═══════╡
│ 0-15      ┆ 0.590361      ┆ 83    │
│ 16-30     ┆ 0.358896      ┆ 326   │
│ 31-45     ┆ 0.425743      ┆ 202   │
│ 46-60     ┆ 0.407407      ┆ 81    │
│ 60+       ┆ 0.286432      ┆ 199   │
└───────────┴───────────────┴───────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpj12kre4n.py:24: DeprecationWarning: `pl.count()` is deprecated. Please use `pl.len()` instead.
  pl.count().alias("count")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (
    df
    .with_columns(
        pl.when(pl.col("Age") <= 15).then(pl.lit("0-15"))
        .when(pl.col("Age") <= 30).then(pl.lit("16-30"))
        .when(pl.col("Age") <= 45).then(pl.lit("31-45"))
        .when(pl.col("Age") <= 60).then(pl.lit("46-60"))
        .otherwise(pl.lit("60+"))
        .alias("AgeGroup")
    )
    .group_by("AgeGroup")
    .agg(
        pl.col("Survived").mean().alias("SurvivalRate")
    )
    .sort("AgeGroup")
)

print(result)

Output:

shape: (5, 2)
┌──────────┬──────────────┐
│ AgeGroup ┆ SurvivalRate │
│ ---      ┆ ---          │
│ str      ┆ f64          │
╞══════════╪══════════════╡
│ 0-15     ┆ 0.590361     │
│ 16-30    ┆ 0.358896     │
│ 31-45    ┆ 0.425743     │
│ 46-60    ┆ 0.407407     │
│ 60+      ┆ 0.286432     │
└──────────┴──────────────┘

Status: ✓ Success

transform_cast_dtypes

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Cast all numeric columns to 16-bit integers
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of casting dtypes: df.cast({cs.numeric(): pl.Int16})

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

numeric_columns = ["PassengerId", "Survived", "Pclass", "Age", "SibSp", "Parch", "Fare"]
df = df.with_columns([pl.col(col).cast(pl.Int16) for col in numeric_columns])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.cast({
    "PassengerId": pl.Int16,
    "Survived": pl.Int16,
    "Pclass": pl.Int16,
    "Age": pl.Int16,
    "SibSp": pl.Int16,
    "Parch": pl.Int16,
    "Fare": pl.Int16
})

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

numeric_columns = ["PassengerId", "Survived", "Pclass", "Age", "SibSp", "Parch", "Fare"]
df = df.with_columns([pl.col(col).cast(pl.Int16) for col in numeric_columns])

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Identify numeric columns (excluding 'Name', 'Sex', 'Ticket', 'Cabin', 'Embarked')
numeric_cols = ['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare']

# Cast all numeric columns to 16-bit integers
for col in numeric_cols:
    if col in df.columns:
        df = df.with_column(pl.col(col).cast(pl.Int16))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpvp1t4lfd.py", line 13, in 
    df = df.with_column(pl.col(col).cast(pl.Int16))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Define the numeric columns to cast
numeric_columns = ["PassengerId", "Survived", "Pclass", "Age", "SibSp", "Parch", "Fare"]

# Create a dictionary mapping numeric columns to Int16 type
cast_dict = {col: pl.Int16 for col in numeric_columns if col in df.columns}

# Cast the columns
df = df.cast(cast_dict)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load data
df = pl.read_csv("data/titanic.csv")

# Cast numeric columns to 16-bit integers
df = df.with_columns([
    pl.col("PassengerId").cast(pl.Int16),
    pl.col("Survived").cast(pl.Int16),
    pl.col("Pclass").cast(pl.Int16),
    pl.col("Age").cast(pl.Int16),
    pl.col("SibSp").cast(pl.Int16),
    pl.col("Parch").cast(pl.Int16),
    pl.col("Fare").cast(pl.Int16)
])

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
numeric_cols = ['PassengerId', 'Survived', 'Pclass', 'Age', 'SibSp', 'Parch', 'Fare']
df = df.with_columns([pl.col(x).cast(pl.Int16) for x in numeric_cols])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.cast({col: pl.Int16 for col in df.columns if df[col].dtype == pl.Int64 or df[col].dtype == pl.Float64})

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns([
    pl.col("PassengerId").cast(pl.Int16),
    pl.col("Survived").cast(pl.Int16),
    pl.col("Pclass").cast(pl.Int16),
    pl.col("Age").cast(pl.Int16),
    pl.col("SibSp").cast(pl.Int16),
    pl.col("Parch").cast(pl.Int16),
    pl.col("Fare").cast(pl.Int16)
])

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
numeric_cols = ["PassengerId", "Survived", "Pclass", "Age", "SibSp", "Parch", "Fare"]
df = df.with_columns([pl.col(col).cast(pl.Int16) for col in numeric_cols])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns([pl.col(col).cast(pl.Int16) for col in df.columns if df[col].dtype in [pl.Int64, pl.Float64]])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
numeric_cols = df.select(pl.col(pl.Int64)).columns
df = df.with_columns([pl.col(col).cast(pl.Int16) for col in numeric_cols])

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

numeric_cols = ["PassengerId", "Survived", "Pclass", "Age", "SibSp", "Parch", "Fare"]

for col in numeric_cols:
    if col in df.columns:
        df = df.with_columns(pl.col(col).cast(pl.Int16))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
import polars.datatypes as cs

df = pl.read_csv("data/titanic.csv")

numeric_cols = [
    "PassengerId",
    "Survived",
    "Pclass",
    "Age",
    "SibSp",
    "Parch",
    "Fare",
]

for col in numeric_cols:
    if col in df.columns:
        df = df.with_columns(pl.col(col).cast(pl.Int16))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

numeric_cols = [
    "PassengerId",
    "Survived",
    "Pclass",
    "Age",
    "SibSp",
    "Parch",
    "Fare",
]

for col in numeric_cols:
    df = df.with_columns(pl.col(col).cast(pl.Int16))

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

numeric_cols = ["PassengerId", "Survived", "Pclass", "Age", "SibSp", "Parch", "Fare"]
df = df.with_columns([pl.col(col).cast(pl.Int16) for col in numeric_cols])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

numeric_columns = ["PassengerId", "Survived", "Pclass", "Age", "SibSp", "Parch", "Fare"]
df = df.with_columns([pl.col(col).cast(pl.Int16) for col in numeric_columns])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns([
    pl.col("PassengerId").cast(pl.Int16),
    pl.col("Survived").cast(pl.Int16), 
    pl.col("Pclass").cast(pl.Int16),
    pl.col("Age").cast(pl.Int16),
    pl.col("SibSp").cast(pl.Int16),
    pl.col("Parch").cast(pl.Int16),
    pl.col("Fare").cast(pl.Int16)
])

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

numeric_cols = ["PassengerId", "Survived", "Pclass", "Age", "SibSp", "Parch", "Fare"]

for col in numeric_cols:
    if col in df.columns:
        try:
            df = df.with_columns(pl.col(col).cast(pl.Float32))
        except:
            pass
        try:
            df = df.with_columns(pl.col(col).cast(pl.Int16))
        except:
            pass
print(df.dtypes)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpp1w0jydj.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    [
        pl.col(col).cast(pl.Int16).alias(col)
        for col in df.select(pl.col(pl.Int64)).columns
    ]
)

df = df.with_columns(
    [
        pl.col(col).cast(pl.Int16).alias(col)
        for col in df.select(pl.col(pl.Float64)).columns
    ]
)

print(df.dtypes)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp53dsij57.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def cast_numeric_to_int16(df: pl.DataFrame) -> pl.DataFrame:
    """Casts all numeric columns in a Polars DataFrame to 16-bit integers."""

    for col in df.columns:
        if df[col].dtype in [pl.Int64, pl.Int32, pl.Float64, pl.Float32]:
            try:
                df = df.with_columns(pl.col(col).cast(pl.Int16))
            except:
                pass # column probably contains null and casting is impossible

    return df

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpgesjrayb.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

transform_drop_columns

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Drop the PassengerId, Ticket, and Cabin columns
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of dropping columns: df.drop(["col1", "col2"])

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.drop(["PassengerId", "Ticket", "Cabin"])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9mac4anf.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.drop(["PassengerId", "Ticket", "Cabin"])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpvfur8mkz.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.drop(["PassengerId", "Ticket", "Cabin"])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpn2dq9_27.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.drop(["PassengerId", "Ticket", "Cabin"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.drop(["PassengerId", "Ticket", "Cabin"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.drop("PassengerId")
df = df.drop("Ticket")
df = df.drop("Cabin")

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.drop(["PassengerId", "Ticket", "Cabin"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.drop(["PassengerId", "Ticket", "Cabin"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Drop the PassengerId, Ticket, and Cabin columns
df = df.drop(["PassengerId", "Ticket", "Cabin"])

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.drop(['PassengerId', 'Ticket', 'Cabin'])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.drop(["PassengerId", "Ticket", "Cabin"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.drop("PassengerId", "Ticket", "Cabin")

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.drop(["PassengerId", "Ticket", "Cabin"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.drop(["PassengerId", "Ticket", "Cabin"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.drop("PassengerId", "Ticket", "Cabin")

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.drop(["PassengerId", "Ticket", "Cabin"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.drop(["PassengerId", "Ticket", "Cabin"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.drop("PassengerId", "Ticket", "Cabin")

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.drop(["PassengerId", "Ticket", "Cabin"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.drop(["PassengerId", "Ticket", "Cabin"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Drop the PassengerId, Ticket, and Cabin columns
df = df.drop("PassengerId", "Ticket", "Cabin")

Status: ✓ Success

transform_existing_column

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Transform the Fare column by rounding it to 1 decimal place using with_columns
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of Polars with_columns syntax: df.with_columns(pl.col("value").round(2))

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").round(1))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").round(1))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").round(1).alias("Fare")
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    pl.col("Fare").round(1).alias("Fare")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Fare").round(1))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Fare").round(1).alias("Fare")
)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").round(1))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(pl.col("Fare").round(1))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").round(1))

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Round the Fare column to 1 decimal place
df = df.with_columns(pl.col("Fare").round(1))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Transform the Fare column by rounding it to 1 decimal place
df = df.with_columns(pl.col("Fare").round(1))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").round(1)
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Fare").round(1))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Fare").round(1))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").round(1))

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").round(1).alias("Fare")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpr95_7zr0.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Fare").round(1))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpefe04yof.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns пл.round(pl.col("Fare"), 1)

Error:

invalid syntax (, line 5)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").round(1))

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(pl.col("Fare").round(1).alias("Fare"))

Status: ✗ Failed

transform_pipe

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Use pipe to:
# 1. Sort the columns alphabetically 
# 2. Select only the first 5 columns
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of using pipe: df.pipe(lambda df: df.select(sorted(df.columns)))

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.pipe(lambda x: x.select(sorted(x.columns)))[:, :5]

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
(df.pipe(lambda df: df.select(sorted(df.columns)))
   .select(pl.col("*").head(5)))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = (df
    .select(sorted(df.columns))  # Sort columns alphabetically
    .select(pl.all().head(5))    # Select first 5 columns
)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.pipe(
    lambda df: df.select(sorted(df.columns))
).pipe(
    lambda df: df.select(df.columns[:5])
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.pipe(lambda df: df.select(sorted(df.columns)[:5]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

(
    df.pipe(lambda df: df.select(sorted(df.columns)))
    .pipe(lambda df: df.select(df.columns[:5]))
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.pipe(lambda x: x.sort_by(x.columns)).pipe(lambda x: x.select(x.columns[:5]))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmps0yk1nlm.py", line 6, in 
    df = df.pipe(lambda x: x.sort_by(x.columns)).pipe(lambda x: x.select(x.columns[:5]))
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 6351, in pipe
    return function(self, *args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmps0yk1nlm.py", line 6, in 
    df = df.pipe(lambda x: x.sort_by(x.columns)).pipe(lambda x: x.select(x.columns[:5]))
                           ^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'sort_by'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.pipe(lambda df: df.select(sorted(df.columns)[:5]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code:

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.pipe(
    lambda df: df.sort_columns(),
    lambda df: df.select(pl.all()[:5])
)

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = (
    df.sort(pl.all().name)
    .select(pl.first(5).name)
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpu9k_kvv7.py", line 8, in 
    df.sort(pl.all().name)
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 5614, in sort
    self.lazy()
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 1371, in sort
    by = parse_into_list_of_expressions(by, *more_by)
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/parse/expr.py", line 100, in parse_into_list_of_expressions
    exprs = _parse_positional_inputs(inputs, structify=__structify)  # type: ignore[arg-type]
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/parse/expr.py", line 114, in _parse_positional_inputs
    return [parse_into_expression(e, structify=structify) for e in inputs_iter]
            ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/parse/expr.py", line 61, in parse_into_expression
    expr = F.lit(input, dtype=dtype)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/functions/lit.py", line 193, in lit
    return wrap_expr(plr.lit(item, allow_object, is_scalar=True))
                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: cannot create expression literal for value of type ExprNameNameSpace.

Hint: Pass `allow_object=True` to accept any value and create a literal of type Object.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df.pipe(lambda df: df.select(sorted(df.columns)).select(df.columns[:5]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = (
    df.sort(pl.all())
    .select(["PassengerId", "Survived", "Pclass", "Name", "Sex"])
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.pipe(
    lambda df: df.select(sorted(df.columns))
).pipe(
    lambda df: df.select(df.columns[:5])
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.pipe(lambda df: df.select(sorted(df.columns))).pipe(lambda df: df.select(df.columns[:5]))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.pipe(
    lambda df: df.select(sorted(df.columns))
).pipe(
    lambda df: df.select(df.columns[:5])
)

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = (df
      .select(sorted(df.columns))
      .select(df.columns[:5]))

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df.pipe(lambda df: df.select(sorted(df.columns))) \
  .select(df.columns[:5])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = (
    pl.read_csv("data/titanic.csv")
    .select(pl.all().sort())   # Sort columns alphabetically
    .select(pl.all().limit(5)) # Select only the first 5 columns
)

df

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def process_dataframe(df: pl.DataFrame) -> pl.DataFrame:
    """
    Sorts columns alphabetically and selects the first 5.
    """
    return df.pipe(
        lambda df: df.select(sorted(df.columns))
    ).pipe(
        lambda df: df.select(df.columns[:5])
    )

if __name__ == '__main__':
    df = pl.read_csv("data/titanic.csv")
    processed_df = process_dataframe(df)
    print(processed_df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpl_tic5h_.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
def process_dataframe(df: pl.DataFrame) -> pl.DataFrame:
    """
    Processes the input Polars DataFrame using pipe.

    Args:
        df: The input Polars DataFrame.

    Returns:
        The processed Polars DataFrame.
    """
    return df.pipe(lambda df: df.select(sorted(df.columns))).pipe(lambda df: df.select(df.columns[:5]))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0f15n94h.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")

    result = (
        df
        .pipe(lambda df: df.select(sorted(df.columns)))
        .pipe(lambda df: df.select(df.columns[:5]))
    )
    return result

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpx4z019ud.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

transform_rename_columns

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Rename the following columns:
# - PassengerId to ID
# - Pclass to Class
# - SibSp to Siblings
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of renaming columns: df.rename({"old_name": "new_name"})

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def rename_columns(df: pl.DataFrame) -> pl.DataFrame:
    """Renames specific columns in a Polars DataFrame.

    Args:
        df: The input Polars DataFrame.

    Returns:
        A new Polars DataFrame with the specified columns renamed.
    """
    df = df.rename({
        "PassengerId": "ID",
        "Pclass": "Class",
        "SibSp": "Siblings"
    })
    return df

if __name__ == '__main__':
    df = pl.read_csv("data/titanic.csv")
    df = rename_columns(df)
    print(df.columns)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpmc3rrrjn.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.rename({
    "PassengerId": "ID",
    "Pclass": "Class",
    "SibSp": "Siblings"
})

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp10pl8_yn.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

# Creating DataFrames
df = pl.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
#df = pl.read_csv("file.csv",try_parse_dates=True)
# Selection
df.select("a", "b")            # Select columns
df.select(pl.all())                # Select all columns
# Filtering
df.filter(pl.col("a") > 1)            # Basic comparison
df.filter(pl.col("b").is_in(["x", "y"]))    # Is in list
df.filter(pl.col("a").is_between(1, 2))     # Range check
# Assuming there's a null value in 'a'
#df = df.with_columns(pl.when(pl.col("a")==1).then(None).otherwise(pl.col("a")).alias("a"))
#df.filter(pl.col("a").is_null())          # Check for nulls
# AND and OR conditions
df.filter((pl.col("a") > 1) & (pl.col("a") < 3))   # AND condition
df.filter((pl.col("a") > 1) | (pl.col("a") < 3))   # OR condition
df.filter(~(pl.col("a") > 1))             # NOT condition
# Expression Arithmetic
df = df.with_columns(
(pl.col("a") + pl.col("a")).alias("sum"),     # Addition
(pl.col("a") - pl.col("a")).alias("diff"),     # Subtraction
(pl.col("a") * pl.col("a")).alias("product"),   # Multiplication
(pl.col("a") / pl.col("a")).alias("division")   # Division
)
# Column Operations
df = df.with_columns(pl.lit("constant").alias("new_col")) # Add constant column
df = df.rename({"a": "new_a"})          # Rename columns
df = df.drop("b", "new_col")             # Drop columns
# Sorting
df = df.sort("new_a", descending=True)          # Sort by column
df = df.sort(["new_a", "sum"], descending=[True, False])  # Multi-column sort
# Aggregation and Grouping
df = pl.DataFrame({"value": [1, 2, 3, 1, 2], "category": ["A", "B", "A", "B", "A"]})

df.select(pl.col("value").sum())           # Sum values
df.select(pl.col("value").mean())           # Calculate mean
df.select(pl.col("value").len())          # Count values
df.select(pl.col("value").n_unique())         # Count unique values
# GroupBy operations
df.group_by("category").agg(pl.col("value").mean())  # Group and aggregate
df.group_by("category").agg(        # Group and aggregate
pl.col("value").mean().name.suffix("_mean"),
pl.col("value").min().name.suffix("_min"))
df.group_by(["category", "value"]).agg(          # Multiple aggregations
pl.lit(1).sum()
)
# Reshaping Data
df = pl.DataFrame({"row_var": ["A", "A", "B", "B"], "col_var": ["X", "Y", "X", "Y"], "value": [1, 2, 3, 4]})

#df.pivot(values="value", index="row_var", on="col_var") # Pivot table
#df.collect().pivot(values="value", index="row_var", on="col_var", aggregate_function="mean") # Pivot lazyframe
#df.unpivot(index=["id"], on=["val1", "val2"])     # Unpivot/melt
#df.explode("list_column")                   # Explode lists
df = df.with_row_index("index")                  # Add row indices
# String Operations
df = pl.DataFrame({"text": ["abc", "def", "ghi"]})
df.select(pl.col("text").str.len_chars())           # String length
df.select(pl.col("text").str.contains("a"))       # Contains pattern
df.select(pl.col("text").str.replace("a", "b"))      # Replace text
df.select(pl.col("text").str.to_lowercase())         # Convert case
df.select(pl.col("text").str.strip())             # Remove whitespace
df = pl.DataFrame({"text": ["a b c", "d e f", "g h i"]})
df.select(pl.col("text").str.split(" "))           # Split strings
# Window Functions
df = pl.DataFrame({"value": [1, 2, 3, 4, 5], "group": ["A", "A", "B", "B", "B"]})
df = df.with_columns(
pl.col("value").sum().over("group").alias("sum_by_group")            # Sum by group
)
# Working with Missing Values
df = pl.DataFrame({"value": [1, None, 3, None, 5], "a": [1,2,3,4,5], "b":[1,None,3,None,5]})

df = df.drop_nulls()                        # Drop null rows
df = df.drop_nulls(subset=["a", "b"])         # Drop for specific columns
#df.fill_null(strategy="forward")               # Forward fill
df = df.with_columns(
pl.col("value").fill_null(pl.col("value").mean()).alias("filled")     # Fill with mean
)
df = df.with_columns(
pl.when(pl.col("value").is_null())
.then(pl.lit(0))
.otherwise(pl.col("value"))
.alias("value")                      # Replace nulls conditionally
)
# Data Type Operations
df = pl.DataFrame({"a": [1.1, 2.2, 3.3], "date": [datetime(2023, 1, 1), datetime(2023, 2, 1), datetime(2023, 3, 1)]})

df.select(pl.col("a").cast(pl.Int64))             # Cast to type
df.select(pl.col("date").dt.year())              # Extract year
df.select(pl.col("date").dt.month())             # Extract month
df.select(pl.col("date").dt.strftime("%Y-%m-%d"))       # Format date
#df.schema                           # Get schema
# Advanced Features
# LazyFrame for optimized execution
lazy_df = df.lazy()
result = lazy_df.filter(pl.col("a") > 0).collect()
# Joins
df1 = pl.DataFrame({"key": [1, 2, 3], "value1": ["a", "b", "c"]})
df2 = pl.DataFrame({"key": [2, 3, 4], "value2": ["x", "y", "z"]})
#df1.join(df2, on="key", how="inner")             # Inner join
#df1.join(df2, on="key", how="left")              # Left join
#df1.join(df2, on=["key", "key"], how="full")        # Multiple join keys
# Sampling
df = pl.DataFrame({"a": range(100)})
df = df.sample(n=10)                        # Sample n rows
df = df.sample(frac=0.1, with_replacement=True)          # Sample fraction
# User-defined functions
df = pl.DataFrame({"a": [1, 2, 3]})
df = df.select(pl.col("a").map_elements(lambda x: x * 2,return_dtype=pl.Int64))     # Apply function
# Efficient operations
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
df = df.select([
pl.all(),                       # All columns
pl.exclude("a")             # Exclude column
])
df = df.with_columns(pl.exclude("a").name.prefix("new_"))   # Batch operations
# Expression contexts
(
pl.DataFrame({"a": [1, 2, 3]}).lazy()
.filter(pl.col("a") > 0)
.with_columns(pl.lit(1).alias("constant"))
.collect()
)                              # Method chaining
# Date and Time Operations
df = pl.DataFrame({"date": [datetime(2023, 1, 1, 12, 30, 0), datetime(2023, 2, 1, 10, 0, 0)]})

df = df.with_columns(
pl.col("date").dt.date().alias("date_only")                 # Extract date
)
df = df.with_columns(
pl.col("date").dt.time().alias("time_only")                 # Extract time
)
date_range = pl.date_range(datetime(2023, 1, 1), datetime(2023, 3, 31), interval="1d", eager=True) # Create a date range Series

df = pl.read_csv("data/titanic.csv")
df = df.rename({"PassengerId": "ID", "Pclass": "Class", "SibSp": "Siblings"})
print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp15c6tmlx.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.rename({"PassengerId": "ID", "Pclass": "Class", "SibSp": "Siblings"})

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.rename({"PassengerId": "ID", "Pclass": "Class", "SibSp": "Siblings"})

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.rename({
    "PassengerId": "ID",
    "Pclass": "Class", 
    "SibSp": "Siblings"
})

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.rename({"PassengerId": "ID", "Pclass": "Class", "SibSp": "Siblings"})

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.rename({
    "PassengerId": "ID",
    "Pclass": "Class",
    "SibSp": "Siblings"
})

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.rename({
    "PassengerId": "ID",
    "Pclass": "Class",
    "SibSp": "Siblings"
})

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.rename({
    "PassengerId": "ID",
    "Pclass": "Class",
    "SibSp": "Siblings"
})

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.rename({"PassengerId": "ID", "Pclass": "Class", "SibSp": "Siblings"})

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.rename({"PassengerId": "ID", "Pclass": "Class", "SibSp": "Siblings"})

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.rename({"PassengerId": "ID", "Pclass": "Class", "SibSp": "Siblings"})

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.rename({"PassengerId": "ID", "Pclass": "Class", "SibSp": "Siblings"})

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Rename the columns
df = df.rename({"PassengerId": "ID", "Pclass": "Class", "SibSp": "Siblings"})

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.rename({
    "PassengerId": "ID",
    "Pclass": "Class",
    "SibSp": "Siblings"
})

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.rename({
    "PassengerId": "ID",
    "Pclass": "Class",
    "SibSp": "Siblings"
})

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.rename({
    "PassengerId": "ID",
    "Pclass": "Class",
    "SibSp": "Siblings"
})

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.rename({"PassengerId": "ID", "Pclass": "Class", "SibSp": "Siblings"})

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.rename({
    "PassengerId": "ID",
    "Pclass": "Class",
    "SibSp": "Siblings"
})

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

# Load data
df = pl.read_csv("data/titanic.csv")

# Rename columns
df = df.rename({"PassengerId": "ID", "Pclass": "Class", "SibSp": "Siblings"})

Status: ✓ Success

unique_values

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of dropping duplicate rows: df.unique()

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.unique()

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.unique()

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows
df = df.unique()

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.unique()

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.unique()

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.drop_duplicates()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpnui0z1t4.py", line 5, in 
    df = df.drop_duplicates()
         ^^^^^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'drop_duplicates'. Did you mean: 'is_duplicated'?

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.unique(subset=['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.unique()

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code to solve the task:

import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows
df = df.unique(subset=["PassengerId", "Survived", "Pclass", "Name", "Sex", "Age", "SibSp", "Parch", "Ticket", "Fare", "Cabin", "Embarked"])

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows
df = df.unique()

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows
df = df.unique()

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows
df = df.unique()

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Read the dataset
df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows
df = df.unique()

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows
df = df.unique()

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows
df = df.unique()

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def drop_duplicate_rows(df: pl.DataFrame) -> pl.DataFrame:
    """
    Drops all duplicate rows from a Polars DataFrame.

    Args:
        df: The input Polars DataFrame.

    Returns:
        A new Polars DataFrame with duplicate rows removed.
    """
    return df.unique()

if __name__ == '__main__':
    df = pl.read_csv("data/titanic.csv")
    df_no_duplicates = drop_duplicate_rows(df)
    print(df_no_duplicates.shape)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmplcvq286p.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.unique()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpn3akhwnd.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.unique()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp48zs9wk3.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.unique()

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.unique()

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows
df = df.unique()

Status: ✓ Success

unique_values_subset

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows, but only consider the Name and Sex columns
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of dropping duplicate rows: df.unique(subset=["Name", "Sex"])

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.unique(subset=["Name", "Sex"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.unique(subset=["Name", "Sex"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Drop duplicates based on Name and Sex columns
df = df.unique(subset=["Name", "Sex"])

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.unique(subset=["Name", "Sex"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.unique(subset=["Name", "Sex"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.unique(subset=["Name", "Sex"])

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.drop_duplicates(subset=["Name", "Sex"])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp47mn0ogr.py", line 6, in 
    df = df.drop_duplicates(subset=["Name", "Sex"])
         ^^^^^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'drop_duplicates'. Did you mean: 'is_duplicated'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.unique(subset=["Name", "Sex"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.drop_duplicates(subset=["Name", "Sex"])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpu0lmw68k.py", line 7, in 
    df = df.drop_duplicates(subset=["Name", "Sex"])
         ^^^^^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'drop_duplicates'. Did you mean: 'is_duplicated'?

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.unique(subset=['Name', 'Sex'])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.unique(subset=["Name", "Sex"], keep="first")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code:

import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.drop_duplicates(subset=["Name", "Sex"])

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df_unique = df.unique(subset=["Name", "Sex"])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows, but only consider the Name and Sex columns
df_unique = df.unique(subset=["Name", "Sex"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Drop all duplicate rows, but only consider the Name and Sex columns
df = df.unique(subset=["Name", "Sex"], keep="first")

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.unique(subset=["Name", "Sex"])

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpspjo2nv2.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.unique(subset=["Name", "Sex"])
print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpqd3efo0u.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.unique(subset=["Name", "Sex"])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpwn0ewtlh.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.unique(subset=['Name', 'Sex'], maintain_order=True)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.unique(subset=["Name", "Sex"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.unique(subset=["Name", "Sex"], maintain_order=True)

Status: ✓ Success

unnest_struct_column

Original Prompt

# First create a DataFrame with a struct column:
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

# Separate the info column into separate columns for each field

Suffix Prompt

# Use the unnest method to separate the info column into separate columns for each field

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

result = df.with_columns([
    pl.col("info").struct.field("name").alias("name"),
    pl.col("info").struct.field("age").alias("age")
]).drop("info")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df_unnested = df.unnest("info")
print(df_unnested)

Output:

shape: (3, 3)
┌─────┬──────┬─────┐
│ id  ┆ name ┆ age │
│ --- ┆ ---  ┆ --- │
│ i64 ┆ str  ┆ i64 │
╞═════╪══════╪═════╡
│ 1   ┆ John ┆ 30  │
│ 2   ┆ Jane ┆ 25  │
│ 3   ┆ Bob  ┆ 35  │
└─────┴──────┴─────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Create DataFrame with struct column
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25}, 
        {"name": "Bob", "age": 35}
    ]
})

# Separate struct column into individual columns
df_expanded = df.with_columns([
    pl.col("info").struct.field("name").alias("name"),
    pl.col("info").struct.field("age").alias("age")
]).drop("info")

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df_unpack(df, "info")
df

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmposknh3jl.py", line 14, in 
    df = df_unpack(df, "info")
         ^^^^^^^^^
NameError: name 'df_unpack' is not defined

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})
df = df.unnest("info")
print(df)

Output:

shape: (3, 3)
┌─────┬──────┬─────┐
│ id  ┆ name ┆ age │
│ --- ┆ ---  ┆ --- │
│ i64 ┆ str  ┆ i64 │
╞═════╪══════╪═════╡
│ 1   ┆ John ┆ 30  │
│ 2   ┆ Jane ┆ 25  │
│ 3   ┆ Bob  ┆ 35  │
└─────┴──────┴─────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code to separate the `info` column into separate columns for each field:

import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.select(pl.col("id"), pl.col("info").arr.get("name").alias("name"), pl.col("info").arr.get("age").alias("age"))

print(df)

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(
    pl.col("info").struct.to_frames()
)

df = df.drop("info")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpqgoz57s6.py", line 14, in 
    pl.col("info").struct.to_frames()
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'ExprStructNameSpace' object has no attribute 'to_frames'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.unnest(["info"])
df = df.select([
    "id",
    pl.col("info.name").alias("name"),
    pl.col("info.age").alias("age")
])

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmptbcn9nop.py", line 14, in 
    df = df.select([
         ^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 9632, in select
    return self.lazy().select(*exprs, **named_exprs).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ColumnNotFoundError: info.name

Resolved plan until failure:

	---> FAILED HERE RESOLVING 'select' <---
DF ["id", "name", "age"]; PROJECT */3 COLUMNS

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(
    pl.col("info").field("name").alias("name"),
    pl.col("info").field("age").alias("age")
).drop("info")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpzo3fmbat.py", line 15, in 
    pl.col("info").field("name").alias("name"),
    ^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'field'

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns([
    pl.col("info").struct.field("name").alias("name"),
    pl.col("info").struct.field("age").alias("age")
])

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.unnest("info")

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(
    pl.col("info").struct.field("name").alias("name"),
    pl.col("info").struct.field("age").alias("age")
)

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.unnest("info")
print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp0n3ju4r4.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns(pl.col("info").struct.unnest())

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp6s71ac4g.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
from datetime import datetime

# Creating DataFrames
df = pl.DataFrame({"a": [1, 2, 3], "b": ["x", "y", "z"]})
# df = pl.read_csv("file.csv",try_parse_dates=True)
# Selection
df.select("a", "b")            # Select columns
df.select(pl.all())                # Select all columns
# Filtering
df = pl.DataFrame({"value": [5, 12, 15, 8, 21], "category": ["A", "B", "A", "C", "B"]})
df.filter(pl.col("value") > 10)            # Basic comparison
df.filter(pl.col("category").is_in(["A", "B"]))    # Is in list
df.filter(pl.col("value").is_between(10, 20))     # Range check
df = pl.DataFrame({"value": [1, 2, None, 4]})
df.filter(pl.col("value").is_null())          # Check for nulls
# AND and OR conditions
df = pl.DataFrame({"a": [1, 6, 3], "b": [12, 7, 9]})
df.filter((pl.col("a") > 5) & (pl.col("b") < 10))   # AND condition
df.filter((pl.col("a") > 5) | (pl.col("b") < 10))   # OR condition
df.filter(~(pl.col("a") > 5))             # NOT condition
# Expression Arithmetic
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6]})
df.with_columns(
(pl.col("a") + pl.col("b")).alias("sum"),     # Addition
(pl.col("a") - pl.col("b")).alias("diff"),     # Subtraction
(pl.col("a") * pl.col("b")).alias("product"),   # Multiplication
(pl.col("a") / pl.col("b")).alias("division")   # Division
)
# Column Operations
df = pl.DataFrame({"a": [1, 2, 3]})
df.with_columns(pl.lit("constant").alias("new_col")) # Add constant column
df = pl.DataFrame({"old_name": [1, 2, 3]})
df.rename({"old_name": "new_name"})          # Rename columns
df = pl.DataFrame({"column1": [1, 2, 3], "column2": [4, 5, 6], "column3":[7,8,9]})
df.drop("column1", "column2")             # Drop columns
# Sorting
df = pl.DataFrame({"column": [3, 1, 2]})
df.sort("column", descending=True)          # Sort by column
df = pl.DataFrame({"col1": [3, 1, 2], "col2": [4, 6, 5]})
df.sort(["col1", "col2"], descending=[True, False])  # Multi-column sort
# Aggregation and Grouping
df = pl.DataFrame({"value": [1, 2, 3]})
df.select(pl.col("value").sum())           # Sum values
df.select(pl.col("value").mean())           # Calculate mean
df.select(pl.col("value").len())          # Count values
df.select(pl.col("value").n_unique())         # Count unique values
# GroupBy operations
df = pl.DataFrame({"category": ["A", "B", "A"], "value": [1, 2, 3]})
df.group_by("category").agg(pl.col("value").mean())  # Group and aggregate
df.group_by("category").agg(        # Group and aggregate
pl.col("value").mean().name.suffix("_mean"),
pl.col("value").min().name.suffix("_min"))
df = pl.DataFrame({"cat1": ["A", "B", "A", "B"], "cat2": ["X", "Y", "X", "Y"], "val1": [1, 2, 3, 4], "val2": [5, 6, 7, 8]})
df.group_by(["cat1", "cat2"]).agg(          # Multiple aggregations
pl.col("val1").sum(),
pl.col("val2").mean()
)
# Reshaping Data
df = pl.DataFrame({"row_var": ["A", "B", "A"], "col_var": ["X", "Y", "Y"], "value": [1, 2, 3]})
df.pivot(values="value", index="row_var", on="col_var") # Pivot table
#df.collect().pivot(values="value", index="row_var", on="col_var", aggregate_function="mean") # Pivot lazyframe
df = pl.DataFrame({"id": [1, 2, 3], "val1": [4, 5, 6], "val2": [7, 8, 9]})
df.unpivot(index=["id"], on=["val1", "val2"])     # Unpivot/melt
df = pl.DataFrame({"list_column": [[1, 2], [3, 4], [5, 6]]})
df.explode("list_column")                   # Explode lists
df = pl.DataFrame({"a": [1, 2, 3]})
df.with_row_index("index")                  # Add row indices
# String Operations
df = pl.DataFrame({"text": ["hello", "world", "Polars"]})
df.select(pl.col("text").str.len_chars())           # String length
df.select(pl.col("text").str.contains("pattern"))       # Contains pattern
df.select(pl.col("text").str.replace("old", "new"))      # Replace text
df.select(pl.col("text").str.to_lowercase())         # Convert case
df.select(pl.col("text").str.strip())             # Remove whitespace
df.select(pl.col("text").str.split(" "))           # Split strings
# Window Functions
df = pl.DataFrame({"group": ["A", "A", "B", "B"], "value": [1, 2, 3, 4]})
df.with_columns(
pl.col("value").sum().over("group")            # Sum by group
)
# Working with Missing Values
df = pl.DataFrame({"a": [1, 2, None]})
df.drop_nulls()                        # Drop null rows
df = pl.DataFrame({"column1": [1, None, 3], "column2": [4, 5, None]})
df.drop_nulls(subset=["column1", "column2"])         # Drop for specific columns
df = pl.DataFrame({"value": [None, 1, 2, None]})
df.fill_null(strategy="forward")               # Forward fill
df = pl.DataFrame({"value": [None, 1, 2, None]})
df.with_columns(
pl.col("value").fill_null(pl.col("value").mean())     # Fill with mean
)
df = pl.DataFrame({"value": [None, 1, 2, None]})
df.with_columns(
pl.when(pl.col("value").is_null())
.then(pl.lit(0))
.otherwise(pl.col("value"))
.alias("value")                      # Replace nulls conditionally
)
# Data Type Operations
df = pl.DataFrame({"a": [1.0, 2.0, 3.0]})
df.select(pl.col("a").cast(pl.Int64))             # Cast to type
df = pl.DataFrame({"date": [datetime(2023, 1, 1), datetime(2023, 2, 1), datetime(2023, 3, 1)]})
df.select(pl.col("date").dt.year())              # Extract year
df.select(pl.col("date").dt.month())             # Extract month
df.select(pl.col("date").dt.strftime("%Y-%m-%d"))       # Format date
df = pl.DataFrame({"a": [1, 2, 3]})
df.schema                           # Get schema
# Advanced Features
# LazyFrame for optimized execution
lazy_df = df.lazy()
result = lazy_df.filter(pl.col("a") > 0).collect()
# Joins
df1 = pl.DataFrame({"key": [1, 2, 3], "val1": [4, 5, 6]})
df2 = pl.DataFrame({"key": [1, 2, 4], "val2": [7, 8, 9]})
df1.join(df2, on="key", how="inner")             # Inner join
df1.join(df2, on="key", how="left")              # Left join
df1 = pl.DataFrame({"key1": [1, 2, 3], "key2": ["A", "B", "C"], "val1": [4, 5, 6]})
df2 = pl.DataFrame({"key1": [1, 2, 4], "key2": ["A", "B", "D"], "val2": [7, 8, 9]})
df1.join(df2, on=["key1", "key2"], how="full")        # Multiple join keys
# Sampling
df = pl.DataFrame({"a": range(20)})
df.sample(n=10)                        # Sample n rows
df.sample(frac=0.1, with_replacement=True)          # Sample fraction
# User-defined functions
df = pl.DataFrame({"a": [1, 2, 3]})
df.select(pl.col("a").map_elements(lambda x: x * 2,return_dtype=pl.Int64))     # Apply function
# Efficient operations
df = pl.DataFrame({"a": [1, 2, 3], "b": [4, 5, 6], "c": [7, 8, 9]})
df.select([
pl.all(),                       # All columns
pl.exclude("a")             # Exclude column
])
df.with_columns(pl.exclude("a").name.prefix("new_"))   # Batch operations
# Expression contexts
df = pl.DataFrame({"a": [1, 2, 3]})
(
df.lazy()
.filter(pl.col("a") > 0)
.with_columns(pl.lit(1).alias("constant"))
.collect()
)                              # Method chaining
# Date and Time Operations
df = pl.DataFrame({"date": [datetime(2023, 1, 1, 10, 30, 0)]})
df.with_columns(
pl.col("date").dt.date()                 # Extract date
)
df.with_columns(
pl.col("date").dt.time()                 # Extract time
)
pl.date_range(datetime(2023, 1, 1), datetime(2023, 3, 31), interval="1d", eager=True) # Create a date range Series

# First create a DataFrame with a struct column:
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

# Separate the info column into separate columns for each field
df = df.with_columns(
    pl.struct("info").struct.unnest()
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9ycwdcu6.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

result = df.with_columns([
    pl.col("info").struct.field("name").alias("name"),
    pl.col("info").struct.field("age").alias("age")
]).drop("info")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

# Separate the info column into separate columns for each field
df_unnested = df.unnest("info")

print(df_unnested)

Output:

shape: (3, 3)
┌─────┬──────┬─────┐
│ id  ┆ name ┆ age │
│ --- ┆ ---  ┆ --- │
│ i64 ┆ str  ┆ i64 │
╞═════╪══════╪═════╡
│ 1   ┆ John ┆ 30  │
│ 2   ┆ Jane ┆ 25  │
│ 3   ┆ Bob  ┆ 35  │
└─────┴──────┴─────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

# Separate the info column into separate columns for each field
df = df.with_columns([
    pl.col("info").struct.field("name").alias("name"),
    pl.col("info").struct.field("age").alias("age")
])

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df = df.with_columns([
    pl.col("info").struct.field("name").alias("name"),
    pl.col("info").struct.field("age").alias("age")
])

print(df)

Output:

shape: (3, 4)
┌─────┬─────────────┬──────┬─────┐
│ id  ┆ info        ┆ name ┆ age │
│ --- ┆ ---         ┆ ---  ┆ --- │
│ i64 ┆ struct[2]   ┆ str  ┆ i64 │
╞═════╪═════════════╪══════╪═════╡
│ 1   ┆ {"John",30} ┆ John ┆ 30  │
│ 2   ┆ {"Jane",25} ┆ Jane ┆ 25  │
│ 3   ┆ {"Bob",35}  ┆ Bob  ┆ 35  │
└─────┴─────────────┴──────┴─────┘

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

df_unnested = df.unnest("info")
print(df_unnested)

Output:

shape: (3, 3)
┌─────┬──────┬─────┐
│ id  ┆ name ┆ age │
│ --- ┆ ---  ┆ --- │
│ i64 ┆ str  ┆ i64 │
╞═════╪══════╪═════╡
│ 1   ┆ John ┆ 30  │
│ 2   ┆ Jane ┆ 25  │
│ 3   ┆ Bob  ┆ 35  │
└─────┴──────┴─────┘

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.DataFrame({
    "id": [1, 2, 3],
    "info": [
        {"name": "John", "age": 30},
        {"name": "Jane", "age": 25},
        {"name": "Bob", "age": 35}
    ]
})

# Separate the info column into separate columns for each field
df = df.with_columns(
    pl.col("info").struct.field("name").alias("name"),
    pl.col("info").struct.field("age").alias("age")
)

print(df)

Output:

shape: (3, 4)
┌─────┬─────────────┬──────┬─────┐
│ id  ┆ info        ┆ name ┆ age │
│ --- ┆ ---         ┆ ---  ┆ --- │
│ i64 ┆ struct[2]   ┆ str  ┆ i64 │
╞═════╪═════════════╪══════╪═════╡
│ 1   ┆ {"John",30} ┆ John ┆ 30  │
│ 2   ┆ {"Jane",25} ┆ Jane ┆ 25  │
│ 3   ┆ {"Bob",35}  ┆ Bob  ┆ 35  │
└─────┴─────────────┴──────┴─────┘

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

unpivot_table

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# 1. Select the Pclass and Sex columns
# 2. Unpivot the Sex columns back into rows
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of unpivoting:
# df.unpivot(index="id_column", on=["col1", "col2"])

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.select(['Pclass', 'Sex'])
df = df.melt(id_vars='Pclass', variable_name='sex_type', value_name='sex')

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpew7vu0ec.py:7: DeprecationWarning: `DataFrame.melt` is deprecated. Use `unpivot` instead, with `index` instead of `id_vars` and `on` instead of `value_vars`
  df = df.melt(id_vars='Pclass', variable_name='sex_type', value_name='sex')

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code for the task:


import polars as pl

df = pl.read_csv("data/titanic.csv")
unpivoted_df = df.select([pl.col("Pclass"), pl.col("Sex")]).unpivot("Pclass", ["Sex"], "Value", "Sex_Type")

Error:

invalid syntax (, line 3)

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(["Pclass", "Sex"]).unpivot("variable", "value", ["Sex"])

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmptz21ry4q.py", line 8, in 
    result = df.select(["Pclass", "Sex"]).unpivot("variable", "value", ["Sex"])
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: DataFrame.unpivot() takes from 1 to 2 positional arguments but 4 were given

Status: ✗ Failed

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select(["Pclass", "Sex"]).melt(id_vars="Pclass", value_vars="Sex")

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp9z_rf39z.py:6: DeprecationWarning: `DataFrame.melt` is deprecated. Use `unpivot` instead, with `index` instead of `id_vars` and `on` instead of `value_vars`
  result = df.select(["Pclass", "Sex"]).melt(id_vars="Pclass", value_vars="Sex")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

result = df.select(["Pclass", "Sex"]).unpivot(index="Pclass", on=["Sex"])

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# 1. Select the Pclass and Sex columns
df.select(["Pclass", "Sex"])

# 2. Unpivot the Sex columns back into rows
df.unpivot(index=["Pclass"], on=["Sex"])

Status: ✓ Success

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(["Pclass", "Sex"]).melt(id_vars="Pclass", value_vars="Sex")

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpalygkp8b.py:7: DeprecationWarning: `DataFrame.melt` is deprecated. Use `unpivot` instead, with `index` instead of `id_vars` and `on` instead of `value_vars`
  result = df.select(["Pclass", "Sex"]).melt(id_vars="Pclass", value_vars="Sex")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Select the Pclass and Sex columns
selected_df = df.select(["Pclass", "Sex"])

# Unpivot the Sex column back into rows
unpivoted_df = selected_df.unpivot(id_column="Pclass", value_column="Sex")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpg22_vgx8.py", line 10, in 
    unpivoted_df = selected_df.unpivot(id_column="Pclass", value_column="Sex")
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: DataFrame.unpivot() got an unexpected keyword argument 'id_column'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# 1. Select the Pclass and Sex columns
selected_df = df.select(["Pclass", "Sex"])

# 2. Unpivot the Sex columns back into rows
unpivoted_df = selected_df.melt(id_vars="Pclass", value_vars="Sex")

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpq4gdt4l6.py:10: DeprecationWarning: `DataFrame.melt` is deprecated. Use `unpivot` instead, with `index` instead of `id_vars` and `on` instead of `value_vars`
  unpivoted_df = selected_df.melt(id_vars="Pclass", value_vars="Sex")

Status: ✓ Success

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(["Pclass", "Sex"]).melt(
    id_vars=["Pclass"],
    value_vars=["Sex"],
    variable_name="variable",
    value_name="value"
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpnioev8zq.py:7: DeprecationWarning: `DataFrame.melt` is deprecated. Use `unpivot` instead, with `index` instead of `id_vars` and `on` instead of `value_vars`
  result = df.select(["Pclass", "Sex"]).melt(

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(["Pclass", "Sex"]).melt(
    id_vars=["Pclass"],
    value_vars=["Sex"]
)

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp81653x40.py:7: DeprecationWarning: `DataFrame.melt` is deprecated. Use `unpivot` instead, with `index` instead of `id_vars` and `on` instead of `value_vars`
  result = df.select(["Pclass", "Sex"]).melt(

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df.select(["Pclass", "Sex"]).unpivot(index=["Pclass"], on=["Sex"])

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# 1. Select the Pclass and Sex columns
df_selected = df[["Pclass", "Sex"]]

# 2. Unpivot the Sex columns back into rows
df_unpivot = df_selected.unnest(["Sex"])

print(df_unpivot)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpx2okm3nt.py", line 11, in 
    df_unpivot = df_selected.unnest(["Sex"])
                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/dataframe/frame.py", line 11536, in unnest
    return self.lazy().unnest(columns, *more_columns).collect(_eager=True)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/lazyframe/frame.py", line 2065, in collect
    return wrap_df(ldf.collect(callback))
                   ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.SchemaError: invalid series dtype: expected `Struct`, got `str` for series with name `Sex`

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# 1. Select the Pclass and Sex columns
df_selected = df[["Pclass", "Sex"]]

# 2. Unpivot the Sex columns back into rows
df_unpivoted = df_selected.unpivot(names_to="variable", values_to="value")

print(df_unpivoted)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp60rijv3d.py", line 11, in 
    df_unpivoted = df_selected.unpivot(names_to="variable", values_to="value")
                   ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: DataFrame.unpivot() got an unexpected keyword argument 'names_to'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

# 1. Select the Pclass and Sex columns
df = df.select(["Pclass", "Sex"])

# 2. Unpivot the Sex columns back into rows
df = df.unpivot(index=["Pclass"], on=["Sex"])

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# 1. Select the Pclass and Sex columns
df_selected = df.select(["Pclass", "Sex"])

# 2. Unpivot the Sex column back into rows
# Since Sex is already a column, we'll melt it to transform it into rows
df_unpivoted = df_selected.melt(
    id_vars=["Pclass"],
    value_vars=["Sex"],
    variable_name="Attribute",
    value_name="Value"
)

print(df_unpivoted)

Output:

shape: (891, 3)
┌────────┬───────────┬────────┐
│ Pclass ┆ Attribute ┆ Value  │
│ ---    ┆ ---       ┆ ---    │
│ i64    ┆ str       ┆ str    │
╞════════╪═══════════╪════════╡
│ 3      ┆ Sex       ┆ male   │
│ 1      ┆ Sex       ┆ female │
│ 3      ┆ Sex       ┆ female │
│ 1      ┆ Sex       ┆ female │
│ 3      ┆ Sex       ┆ male   │
│ …      ┆ …         ┆ …      │
│ 2      ┆ Sex       ┆ male   │
│ 1      ┆ Sex       ┆ female │
│ 3      ┆ Sex       ┆ female │
│ 1      ┆ Sex       ┆ male   │
│ 3      ┆ Sex       ┆ male   │
└────────┴───────────┴────────┘

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpo4nfisiq.py:13: DeprecationWarning: `DataFrame.melt` is deprecated. Use `unpivot` instead, with `index` instead of `id_vars` and `on` instead of `value_vars`
  df_unpivoted = df_selected.melt(

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# Load the data
df = pl.read_csv("data/titanic.csv")

# 1. Select the Pclass and Sex columns
# 2. Unpivot the Sex column back into rows
result = df.select(["PassengerId", "Pclass", "Sex"]).unpivot(
    index="PassengerId", 
    columns=["Sex"],
    names_to="Sex_Category",
    values_to="Sex_Value"
)

print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpaw__n6ys.py", line 10, in 
    result = df.select(["PassengerId", "Pclass", "Sex"]).unpivot(
             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
TypeError: DataFrame.unpivot() got an unexpected keyword argument 'columns'

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

# First load the data using Polars
df = pl.read_csv("data/titanic.csv")

# 1. Select the Pclass and Sex columns
# 2. Unpivot the Sex columns back into rows
result = df.select(["Pclass", "Sex"])
result = result.melt(id_vars=["Pclass"], value_vars=["Sex"], variable_name="Variable", value_name="Value")

Error:

/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpfq4xv3r1.py:11: DeprecationWarning: `DataFrame.melt` is deprecated. Use `unpivot` instead, with `index` instead of `id_vars` and `on` instead of `value_vars`
  result = result.melt(id_vars=["Pclass"], value_vars=["Sex"], variable_name="Variable", value_name="Value")

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    result = df.select(["Pclass", "Sex"])
    return result

if __name__ == "__main__":
    # Create a dummy data/titanic.csv file for testing
    import os
    if not os.path.exists("data"):
        os.makedirs("data")
    
    with open("data/titanic.csv", "w") as f:
        f.write("PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked\n")
        f.write("1,0,3,Braund,Mr. Owen Harris,male,22,1,0,A/5 21171,7.25,,S\n")
        f.write("2,1,1,Cumings,Mrs. John Bradley (Florence Briggs Thayer),female,38,1,0,PC 17599,71.2833,C85,C\n")
        f.write("3,1,3,Heikkinen,Miss. Laina,female,26,0,0,STON/O2. 3101282,7.925,,S\n")
        f.write("4,1,1,Futrelle,Mrs. Jacques Heath (Lily May Peel),female,35,1,0,113803,53.1,C123,S\n")
        f.write("5,0,3,Allen,Mr. William Henry,male,35,0,0,373450,8.05,,S\n")

    df = solve()
    print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpc0ah0gli.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

result = df.select(["Pclass", "Sex"])
result = result.unpivot(on=["Sex"])
print(result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpthxrslu6.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.select(["Pclass", "Sex"])
print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmph4x8ywl4.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

window_multiple_columns

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Add a column 'avg_age_by_class_sex' that contains the average age for each combination of Pclass and Sex
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of window aggregation over multiple columns: df.with_columns(pl.col("value").mean().over(["group1", "group2"]))

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col('Age').mean().over(['Pclass', 'Sex']).alias('avg_age_by_class_sex')
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Age").mean().over(["Pclass", "Sex"]).alias("avg_age_by_class_sex"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Age").mean().over(["Pclass", "Sex"]).alias("avg_age_by_class_sex")
)

Status: ✓ Success

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Calculate average age by Pclass and Sex
avg_age_by_class_sex = df.group_by(["Pclass", "Sex"]).agg(
    pl.mean("Age").alias("avg_age_by_class_sex")
)

# Join this back to the original dataframe
df = df.join(avg_age_by_class_sex, on=["Pclass", "Sex"], how="left")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Age").mean().over(["Pclass", "Sex"]).alias("avg_age_by_class_sex")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Age")
    .mean()
    .over(["Pclass", "Sex"])
    .alias("avg_age_by_class_sex")
)

Status: ✓ Success

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    pl.col("Age").mean().over(["Pclass", "Sex"]).alias("avg_age_by_class_sex")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    pl.col("Age").mean().over(["Pclass", "Sex"]).alias("avg_age_by_class_sex")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Age")
    .mean()
    .over(["Pclass", "Sex"])
    .alias("avg_age_by_class_sex")
)

Status: ✓ Success

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Age").fill_null(
        df.group_by(["Pclass", "Sex"])["Age"].mean()
    ).alias("Age")
)

df = df.with_columns(
    df.group_by(["Pclass", "Sex"])["Age"].mean().alias("avg_age_by_class_sex")
)

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpc52fdj87.py", line 9, in 
    df.group_by(["Pclass", "Sex"])["Age"].mean()
    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^
TypeError: 'GroupBy' object is not subscriptable

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Age").mean().over(["Pclass", "Sex"]).alias("avg_age_by_class_sex")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    df.group_by(["Pclass", "Sex"])
    .agg(pl.col("Age").mean())
    .alias("avg_age")
)

df = df.join(df[["Pclass", "Sex", "avg_age"]], on=["Pclass", "Sex"])

df = df.with_columns(pl.col("avg_age").alias("avg_age_by_class_sex"))

df = df.drop("avg_age")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpaw4mwwoq.py", line 10, in 
    .alias("avg_age")
     ^^^^^
AttributeError: 'DataFrame' object has no attribute 'alias'

Status: ✗ Failed

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.groupby(["Pclass", "Sex"]).agg плarctest({"Age": "mean"}).explode("Age").join(df, on=["Pclass", "Sex"], how="left").rename({"Age": "avg_age_by_class_sex"})

Error:

invalid syntax (, line 7)

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Age").mean().over(["Pclass", "Sex"]).alias("avg_age_by_class_sex"))

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

df = df.with_columns(
    pl.col("Age").mean().over("Pclass", "Sex").alias("avg_age_by_class_sex")
)

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    df.groupby(['Pclass', 'Sex'])['Age'].mean().alias('avg_age_by_class_sex').over(['Pclass', 'Sex'])
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpit5cqh_y.py", line 4, in 
    df.groupby(['Pclass', 'Sex'])['Age'].mean().alias('avg_age_by_class_sex').over(['Pclass', 'Sex'])
    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Age").mean().over(["Pclass", "Sex"]).alias("avg_age_by_class_sex")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    df.groupby(['Pclass', 'Sex'])
    .agg(pl.col('Age').mean().alias('avg_age'))
    .join(df, on=['Pclass', 'Sex'])
    .select(['avg_age'])
    .alias('avg_age_by_class_sex')
)

df.head()

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8uiq2f7m.py", line 8, in 
    df.groupby(['Pclass', 'Sex'])
    ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")

    df = df.with_columns(
        pl.col("Age").mean().over(["Pclass", "Sex"]).alias("avg_age_by_class_sex")
    )
    return df

if __name__ == "__main__":
    # Create a dummy titanic.csv for demonstration purposes
    data = """PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
1,0,3,Braund, Mr. Owen Harris,male,22.0,1,0,A/5 21171,7.25,,S
2,1,1,Cumings, Mrs. John Bradley (Florence Briggs Thayer),female,38.0,1,0,PC 17599,71.2833,C85,C
3,1,3,Heikkinen, Miss. Laina,female,26.0,0,0,STON/O2. 3101282,7.925,,S
4,1,1,Futrelle, Mrs. Jacques Heath (Lily May Peel),female,35.0,1,0,113803,53.1,C123,S
5,0,3,Allen, Mr. William Henry,male,35.0,0,0,373450,8.05,,S
6,0,3,Moran, Mr. James,male,,0,0,330877,8.4583,,Q
7,0,1,McCarthy, Mr. Timothy J,male,54.0,0,0,17463,51.8625,E46,S
8,0,3,Palsson, Master. Gosta Leonard,male,2.0,3,1,349909,21.075,,S
9,1,3,Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg),female,27.0,0,2,347742,11.1333,,S
10,1,2,Nasser, Mrs. Nicholas (Adele Achem),female,14.0,1,0,237736,30.0708,,C
"""
    
    import os
    if not os.path.exists("data"):
        os.makedirs("data")
    with open("data/titanic.csv", "w") as f:
        f.write(data)

    df_result = solve()
    print(df_result)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1adx0sfq.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Age").mean().over(["Pclass", "Sex"]).alias("avg_age_by_class_sex")
)

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpaundugno.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Age").mean().over(["Pclass", "Sex"]).alias("avg_age_by_class_sex")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmphnb2lwoh.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

window_single_column

Original Prompt

# First load the data using Polars as follows
import polars as pl
df = pl.read_csv("data/titanic.csv")

# Add a column 'avg_fare_by_class' that contains the average fare for each Pclass
# Available columns: PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked

Suffix Prompt

# Here is an example of window aggregation: df.with_columns(pl.col("value").mean().over("group"))

api/claude-3-7-sonnet-20250219

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

# Calculate average fare by class
avg_fare_by_class = df.group_by("Pclass").agg(pl.col("Fare").mean().alias("avg_fare"))

# Join back to original dataframe
df = df.join(avg_fare_by_class, on="Pclass", how="left")

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    avg_fare_by_class=pl.col("Fare").mean().over("Pclass")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class")
)

Status: ✓ Success

api/deepseek/deepseek-chat-v3-0324

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(
    pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class")
)

Status: ✓ Success

api/meta-llama/llama-3-70b-instruct

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_column(pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class"))

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpgcd75vgm.py", line 6, in 
    df = df.with_column(pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class"))
         ^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'with_column'. Did you mean: 'with_columns'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')

import polars as pl
df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class"))

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
Here is the Python code to add a column 'avg_fare_by_class' that contains the average fare for each Pclass:


import polars as pl

df = pl.read_csv("data/titanic.csv")
df = df.with_columns(pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class"))

Error:

invalid syntax (, line 3)

Status: ✗ Failed

api/google/gemma-3-27b-it

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").groupby(df["Pclass"]).mean().alias("avg_fare_by_class")
)

print(df)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpu7lh6jpb.py", line 8, in 
    pl.col("Fare").groupby(df["Pclass"]).mean().alias("avg_fare_by_class")
    ^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'groupby'

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").groupby(pl.col("Pclass")).mean().alias("avg_fare_by_class")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpxxtw97of.py", line 8, in 
    pl.col("Fare").groupby(pl.col("Pclass")).mean().alias("avg_fare_by_class")
    ^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'Expr' object has no attribute 'groupby'

Status: ✗ Failed

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

No code to check

Status: ✗ Failed

Suffix Code

Error:

No suffix code

Status: ✗ Failed

Alt Suffix Code

Error:

No alt suffix code

Status: ✗ Failed

api/gpt-4o

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
avg_fares = df.groupby("Pclass").agg(pl.col("Fare").mean().alias("avg_fare_by_class"))
df = df.join(avg_fares, on="Pclass", how="left")

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp8g8v0j9m.py", line 3, in 
    avg_fares = df.groupby("Pclass").agg(pl.col("Fare").mean().alias("avg_fare_by_class"))
                ^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'groupby'. Did you mean: 'group_by'?

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    df.select(pl.col("Fare").mean().over("Pclass")).alias("avg_fare_by_class")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpl6ynj5az.py", line 8, in 
    df.select(pl.col("Fare").mean().over("Pclass")).alias("avg_fare_by_class")
    ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'alias'

Status: ✗ Failed

api/claude-3-5-sonnet-20241022

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
df = df.with_columns(
    pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class")
)

Status: ✓ Success

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl
df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class")
)

Status: ✓ Success

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns([
    pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class")
])

Status: ✓ Success

api/gemini-2.0-flash

Basic Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    
    df = df.with_columns(
        pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class")
    )
    return df

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpen0njxki.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

def solve():
    df = pl.read_csv("data/titanic.csv")
    df = df.with_columns(
        pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class")
    )
    return df

if __name__ == "__main__":
    df = solve()
    print(df.head())

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmp1gddf6fz.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Alt Suffix Code

import polars as pl
df = pl.read_csv('data/titanic.csv')
import polars as pl

df = pl.read_csv("data/titanic.csv")

df = df.with_columns(
    pl.col("Fare").mean().over("Pclass").alias("avg_fare_by_class")
)

Error:

Traceback (most recent call last):
  File "/var/folders/jz/b7p15vxj3v324bkt1kmm2mn80000gn/T/tmpzhzlfqa4.py", line 2, in 
    df = pl.read_csv('data/titanic.csv')
         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/_utils/deprecation.py", line 92, in wrapper
    return function(*args, **kwargs)
           ^^^^^^^^^^^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 537, in read_csv
    df = _read_csv_impl(
         ^^^^^^^^^^^^^^^
  File "/Users/liambrannigan/playModels/evals-ds/.venv/lib/python3.12/site-packages/polars/io/csv/functions.py", line 685, in _read_csv_impl
    pydf = PyDataFrame.read_csv(
           ^^^^^^^^^^^^^^^^^^^^^
polars.exceptions.ComputeError: found more fields than defined in 'Schema'

Consider setting 'truncate_ragged_lines=True'.

Status: ✗ Failed

Model	total_prompts	Overall Performance			Filter			Group			Iter			Sort			Sorting			Tform			Viz			Window
Model Performance by Category
Model	total_prompts	overall_orig	overall_suffix	overall_alt_suffix	orig_filter	suffix_filter	alt_suffix_filter	orig_group	suffix_group	alt_suffix_group	orig_iter	suffix_iter	alt_suffix_iter	orig_sort	suffix_sort	alt_suffix_sort	orig_sorting	suffix_sorting	alt_suffix_sorting	orig_tform	suffix_tform	alt_suffix_tform	orig_viz	suffix_viz	alt_suffix_viz	orig_window	suffix_window	alt_suffix_window
api/deepseek/deepseek-chat-v3-0324	86	86	91	88	100	87	87	85	100	92	100	100	100	100	100	100	100	100	100	84	92	88	0	0	0	100	100	100
api/claude-3-7-sonnet-20250219	86	75	95	90	100	100	100	100	85	92	100	100	100	100	100	100	100	100	75	64	100	92	0	0	0	100	100	100
api/claude-3-5-sonnet-20241022	86	68	93	87	100	100	100	42	92	78	100	100	50	100	100	100	100	100	100	67	94	90	0	0	0	100	100	100
api/google/gemma-3-27b-it	86	61	87	75	100	100	100	35	78	85	100	50	50	100	100	100	75	100	75	64	90	75	0	0	0	0	100	0
api/gpt-4o	86	56	79	86	100	87	100	21	64	92	50	50	50	100	100	100	75	100	100	62	83	88	0	0	0	0	100	0
api/meta-llama/llama-3-70b-instruct	86	39	72	41	87	87	75	21	35	14	0	50	0	100	100	100	75	100	75	37	81	45	0	0	0	0	50	0
api/gemini-2.0-flash	86	15	13	15	50	50	50	14	14	14	0	0	0	0	0	0	0	0	0	13	11	13	0	0	0	0	0	0
api/gemini-2.5-pro-exp-03-25	86	2	2	2	0	0	0	7	7	7	0	0	0	0	0	0	0	0	0	1	1	1	0	0	0	0	0	0

Code Generation Evaluation Results

Query Types Explained

Summary Statistics

Visualizations

Detailed Prompt Results

Filters

Models

Categories

add_calculated_column

Original Prompt

Suffix Prompt

api/gpt-4o

Basic Code

Suffix Code

Alt Suffix Code

api/google/gemma-3-27b-it

Basic Code

Suffix Code

Alt Suffix Code

api/meta-llama/llama-3-70b-instruct

Basic Code

Error:

Suffix Code

Alt Suffix Code

api/gemini-2.5-pro-exp-03-25

Basic Code

Error:

Suffix Code

Error:

Alt Suffix Code

Error:

api/claude-3-5-sonnet-20241022

Basic Code

Suffix Code

Alt Suffix Code

api/claude-3-7-sonnet-20250219

Basic Code

Suffix Code

Alt Suffix Code

api/gemini-2.0-flash

Basic Code

Output:

Suffix Code

Alt Suffix Code

api/deepseek/deepseek-chat-v3-0324

Basic Code

Suffix Code

Alt Suffix Code

add_constant_column

Original Prompt

Suffix Prompt

api/deepseek/deepseek-chat-v3-0324

Basic Code

Suffix Code

Alt Suffix Code

api/claude-3-5-sonnet-20241022

Basic Code

Suffix Code

Alt Suffix Code

api/meta-llama/llama-3-70b-instruct

Basic Code

Error:

Suffix Code

Alt Suffix Code

api/claude-3-7-sonnet-20250219

Basic Code

Error:

Suffix Code

Alt Suffix Code

api/google/gemma-3-27b-it

Basic Code

Suffix Code

Alt Suffix Code

api/gemini-2.0-flash

Basic Code

Suffix Code

Alt Suffix Code

api/gemini-2.5-pro-exp-03-25

Basic Code

Suffix Code