{"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":"40f607bf-a565-5b38-9dbc-d086ed9f1464","task_key":"default--v0~2e1~2e0~5fhf--40f607bf-a565-5b38-9dbc-d086ed9f1464","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 40f607bf-a565-5b38-9dbc-d086ed9f1464","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import heapq\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(df, col1, col2, N=10):\\n\",\"complete_prompt\":\"import heapq\\nfrom sklearn.preprocessing import StandardScaler\\n\\ndef task_func(df, col1, col2, N=10):\\n    \\\"\\\"\\\"\\n    Standardize two columns ('col1' and 'col2') in the DataFrame, find the biggest differences between the individual \\n    elements of the standardized columns, and return the indices of the N largest differences.\\n    \\n    Parameters:\\n    df (pandas.DataFrame): A DataFrame with at least two numerical columns.\\n    col1, col2 (str): Names of the columns to compare.\\n    N (int, optional): Number of indices to return. Default is 10.\\n    \\n    Returns:\\n    list[int]: The indices of the N largest differences.\\n    \\n    Raises:\\n    ValueError: If specified columns are not in the provided DataFrame.\\n\\n    Requirements:\\n    - heapq\\n    - sklearn.preprocessing\\n    \\n    Example:\\n    >>> df = pd.DataFrame({\\n    ...     'col1': [99, 86, 90, 70, 86, 95, 56, 98, 80, 81, 1, 2],\\n    ...     'col2': [21, 11, 21, 1, 26, 40, 4, 50, 34, 37, 3, 4]\\n    ... })\\n    >>> indices = task_func(df, 'col1', 'col2', N=6)\\n    >>> print(indices)     \\n    [3, 1, 11, 10, 7, 0]\\n\\n    >>> df = pd.DataFrame({\\n    ...     'a': [1, 2, 3, 4],\\n    ...     'b': [1, 2, 3, 5]\\n    ... })\\n    >>> indices = task_func(df, 'a', 'b')\\n    >>> print(indices)   \\n    [2, 3, 0, 1]\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Standardize two columns ('col1' and 'col2') in the DataFrame, find the biggest differences between the individual elements of the standardized columns, and return the indices of the N largest differences. >>> df = pd.DataFrame({ ...     'a': [1, 2, 3, 4], ...     'b': [1, 2, 3, 5] ... }) >>> indices = task_func(df, 'a', 'b') >>> print(indices) [2, 3, 0, 1]\\nThe function should raise the exception for: ValueError: If specified columns are not in the provided DataFrame.\\nThe function should output with:\\n    list[int]: The indices of the N largest differences.\\nYou should write self-contained code starting with:\\n```\\nimport heapq\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(df, col1, col2, N=10):\\n```\",\"libs\":\"['sklearn', 'heapq']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}