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BigCodeBench / BigCodeBench v0.1.0_hf 518a7168-b5b8-509a-97a8-0f7cbcaf15c8

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

import seaborn as sns
from scipy.stats import chi2_contingency


def task_func(df1, df2, column1="feature1", column2="feature2"):
    """
    Merge two dataframes based on the 'id' column, perform a chi-square independence test on the merged dataframe,
    and draw a heatmap of the contingency table created from the features in column1, column2.

    Parameters:
    - df1 (DataFrame): Left dataframe to merge. Must contain columns 'id' and one matching column1.
    - df2 (DataFrame): Right dataframe to merge from. Must contain columns 'id' and one matching column2.
    - column1   (str): Name of column containing features in df1. Defaults to 'feature1'.
    - column2   (str): Name of column containing features in df2. Defaults to 'feature2'.

    Returns:
    tuple: A tuple containing:
        - p (float): The p-value of the Chi-Squared test.
        - heatmap (matplotlib.pyplot.Axes): Seaborn heatmap of the contingency table.

    Requirements:
    - seaborn
    - scipy.stats.chi2_contingency

    Example:
    >>> import pandas as pd
    >>> df1 = pd.DataFrame({'id': [1, 2, 3], 'feature1': ['A', 'B', 'A']})
    >>> df2 = pd.DataFrame({'id': [1, 2, 3], 'feature2': ['X', 'Y', 'X']})
    >>> p_value, heatmap = task_func(df1, df2)
    >>> p_value
    0.6650055421020291
    >>> heatmap
    <Axes: xlabel='feature2', ylabel='feature1'>
    """

instruct prompt

Merge two dataframes based on the 'id' column, perform a chi-square independence test on the merged dataframe, and draw a heatmap of the contingency table created from the features in column1, column2.
The function should output with:
    tuple: A tuple containing:
    p (float): The p-value of the Chi-Squared test.
    heatmap (matplotlib.pyplot.Axes): Seaborn heatmap of the contingency table.
You should write self-contained code starting with:

Code

import seaborn as sns
from scipy.stats import chi2_contingency
def task_func(df1, df2, column1="feature1", column2="feature2"):

code prompt

Code

import seaborn as sns
from scipy.stats import chi2_contingency
def task_func(df1, df2, column1="feature1", column2="feature2"):

entry point

task_func

libs

  • scipy
  • seaborn

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Source and history

task_id
518a7168-b5b8-509a-97a8-0f7cbcaf15c8
task_key
default--v0~2e1~2e0~5fhf--518a7168-b5b8-509a-97a8-0f7cbcaf15c8
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import