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BigCodeBench / BigCodeBench v0.1.0_hf 41092e4d-79bb-55a6-ac85-3abc91e628f4

Problem

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complete prompt

import pandas as pd
from scipy.stats import chi2_contingency

def task_func(df, columns=['A', 'B', 'C'], larger=50, equal=900):
    """
    Filters a pandas DataFrame based on the values of specific rows, and performs
    a chi-square independence test on the first two columns.

    The function filters rows based on the following criteria:
        Keep only rows where:
            The value of the second column: df['second'] > larger
            and
            The value of the third column: df['third'] == equal
    
    After filtering a conigency table of the first two columns is computed,
    which is then used in the chi2 independence test. The p_value of the test
    is returned.        

    Parameters:
    df (pd.DataFrame): A DataFrame containing at least the columns specified in the 'columns' parameter.
    columns (list): A list of column names to consider for the operation, defaulting to ['A', 'B', 'C'].
                    The first column should contain categorical data, the second numerical data (used for filtering with values > 'larger'),
                    and the third numerical data (used for filtering with a fixed value of 'equal').
    larger (float, optional): Used for filtering rows against the second column where values > 'larger'.
                              Defaults to 50.
    equal (float, optional): Used for filtering rows against the third column where values == equal.
                             Defaults to 900.

    Returns:
    float: The p-value from the chi-square independence test, indicating the statistical significance.
           
    Raises:
    ValueError: If there's insufficient data for the test (no rows meeting the criteria).
    ValueError: If the number of specified columns is not 3.
    ValueError: If the specified columns are not contained in df.
    

    Requirements:
    - pandas
    - scipy.stats

    Example:
    >>> df = pd.DataFrame({
    ...     'A': ['Yes', 'No', 'Yes', 'No'],
    ...     'B': [55, 70, 40, 85],
    ...     'C': [900, 900, 800, 900]
    ... })
    >>> task_func(df)
    0.22313016014842973

    >>> df = pd.DataFrame({
    ...     'test': ['A', 'b', 'b', 'a', 'c', 'd'],
    ...     'hi': [45, 2, 2, 3, 4, 4],
    ...     'column3': [50, 50, 50, 50, 50, 50, ]
    ... })
    >>> task_func(df, ['test', 'hi', 'column3'], larger=2, equal=50)
    0.23810330555354436
    """

instruct prompt

Filters a pandas DataFrame based on the values of specific rows, and performs a chi-square independence test on the first two columns. The function filters rows based on the following criteria: Keep only rows where: The value of the second column: df['second'] > larger and The value of the third column: df['third'] == equal After filtering a conigency table of the first two columns is computed, which is then used in the chi2 independence test. The p_value of the test is returned. >>> df = pd.DataFrame({ ...     'test': ['A', 'b', 'b', 'a', 'c', 'd'], ...     'hi': [45, 2, 2, 3, 4, 4], ...     'column3': [50, 50, 50, 50, 50, 50, ] ... }) >>> task_func(df, ['test', 'hi', 'column3'], larger=2, equal=50) 0.23810330555354436
The function should raise the exception for: ValueError: If there's insufficient data for the test (no rows meeting the criteria). ValueError: If the number of specified columns is not 3. ValueError: If the specified columns are not contained in df.
The function should output with:
    float: The p-value from the chi-square independence test, indicating the statistical significance.
You should write self-contained code starting with:

Code

import pandas as pd
from scipy.stats import chi2_contingency
def task_func(df, columns=['A', 'B', 'C'], larger=50, equal=900):

code prompt

Code

import pandas as pd
from scipy.stats import chi2_contingency
def task_func(df, columns=['A', 'B', 'C'], larger=50, equal=900):

entry point

task_func

libs

  • pandas
  • scipy

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Source and history

task_id
41092e4d-79bb-55a6-ac85-3abc91e628f4
task_key
default--v0~2e1~2e0~5fhf--41092e4d-79bb-55a6-ac85-3abc91e628f4
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import