{"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":"0020ba4b-aa63-55ff-8a11-6fde4bc76502","task_key":"default--v0~2e1~2e0~5fhf--0020ba4b-aa63-55ff-8a11-6fde4bc76502","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 0020ba4b-aa63-55ff-8a11-6fde4bc76502","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(data, columns):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import MinMaxScaler\\n\\ndef task_func(data, columns):\\n    \\\"\\\"\\\"\\n    Normalizes specified columns of a DataFrame using min-max scaling.\\n\\n    Parameters:\\n    data (dict): A dictionary where keys are column names and values are lists of values.\\n    columns (list of str): A list of column names to be normalized.\\n\\n    Returns:\\n    pandas.DataFrame: A new DataFrame with the specified columns normalized between 0 and 1.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn.preprocessing\\n\\n    Constants:\\n    - A MinMaxScaler object from sklearn.preprocessing is used internally for scaling.\\n\\n    Example:\\n    >>> data = {'a': [1, 2, 3], 'b': [4, 5, 6]}\\n    >>> normalized_df = task_func(data, ['a', 'b'])\\n    >>> print(normalized_df)\\n         a    b\\n    0  0.0  0.0\\n    1  0.5  0.5\\n    2  1.0  1.0\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Normalizes specified columns of a DataFrame using min-max scaling. Constants: - A MinMaxScaler object from sklearn.preprocessing is used internally for scaling.\\nThe function should output with:\\n    pandas.DataFrame: A new DataFrame with the specified columns normalized between 0 and 1.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(data, columns):\\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":[]}