{"kind":"task","effective_mode":"full","benchmark":{"kind":"benchmark","effective_mode":"full","slug":"bigcodebench","formal_name":"BigCodeBench","introduction":"BigCodeBench poses 1,140 function-level tasks drawn across 139 libraries. It tests whether a model can compose several real APIs correctly rather than write one self-contained function.","introduction_ja":"","introduction_en":"","category":"Category not supplied","task_count":null,"acquisition_status":"Acquisition status not supplied","official_url":"https://bigcode-bench.github.io/","indexing_mode":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"c437b695-18f6-59ba-b4d9-62be0f5b32f3","task_key":"default--v0~2e1~2e0~5fhf--c437b695-18f6-59ba-b4d9-62be0f5b32f3","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf c437b695-18f6-59ba-b4d9-62be0f5b32f3","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.feature_selection import f_oneway\\ndef task_func(data_file_path: str):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.feature_selection import f_oneway\\n\\ndef task_func(data_file_path: str):\\n    \\\"\\\"\\\"\\n    Analyzes numerical data from a CSV file. The function reads the CSV file, converts string representations of\\n    numbers with commas into floating point numbers, calculates the mean and standard deviation for each numerical column,\\n    generates a histogram plot for each numerical column, and performs an ANOVA test to check the statistical significance \\n    of differences between means of numerical columns (if applicable).\\n\\n    Parameters:\\n    - data_file_path (str): Path to the CSV data file.\\n\\n    Returns:\\n    - means (pd.Series): Mean values of each numerical column.\\n    - std_devs (pd.Series): Standard deviation values of each numerical column.\\n    - axes (list[matplotlib.axes.Axes]): List of histogram plots for each numerical column.\\n    - anova_results (pd.DataFrame): ANOVA test results for each pair of numerical columns (if more than one numerical column is present).\\n\\n    Requirements:\\n    - pandas\\n    - sklearn\\n\\n    Note:\\n    - The function assumes that all columns in the CSV file contain numerical data or string representations of numerical data.\\n    - The ANOVA test is only performed if there are two or more numerical columns. Compute two columns \\\"F-value\\\" and \\\"P-value\\\" for each pair of numerical columns.\\n\\n    Example:\\n    >>> means, std_devs, axes, anova_results = task_func('data.csv')\\n    >>> print(f'Means: {means}, Standard Deviations: {std_devs}')\\n    >>> print(anova_results)\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Analyzes numerical data from a CSV file. The function reads the CSV file, converts string representations of numbers with commas into floating point numbers, calculates the mean and standard deviation for each numerical column, generates a histogram plot for each numerical column, and performs an ANOVA test to check the statistical significance of differences between means of numerical columns (if applicable).\\nNote that: The function assumes that all columns in the CSV file contain numerical data or string representations of numerical data. The ANOVA test is only performed if there are two or more numerical columns. Compute two columns \\\"F-value\\\" and \\\"P-value\\\" for each pair of numerical columns.\\nThe function should output with:\\n    means (pd.Series): Mean values of each numerical column.\\n    std_devs (pd.Series): Standard deviation values of each numerical column.\\n    axes (list[matplotlib.axes.Axes]): List of histogram plots for each numerical column.\\n    anova_results (pd.DataFrame): ANOVA test results for each pair of numerical columns (if more than one numerical column is present).\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.feature_selection import f_oneway\\ndef task_func(data_file_path: str):\\n```\",\"libs\":\"['pandas', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}