{"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":"663a856a-8e51-5ffc-a1ea-b4237d80a6d7","task_key":"default--v0~2e1~2e0~5fhf--663a856a-8e51-5ffc-a1ea-b4237d80a6d7","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 663a856a-8e51-5ffc-a1ea-b4237d80a6d7","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import MinMaxScaler\\n# Updated function to handle empty input list\\ndef task_func(d):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import MinMaxScaler\\n\\n# Updated function to handle empty input list\\ndef task_func(d):\\n    \\\"\\\"\\\"\\n    Scale all values with the keys \\\"x,\\\" \\\"y\\\" and \\\"z\\\" from a list of dictionaries \\\"d\\\" with MinMaxScaler.\\n\\n    Parameters:\\n    d (list): A list of dictionaries.\\n\\n    Returns:\\n    DataFrame: A pandas DataFrame with scaled values.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn.preprocessing.MinMaxScaler\\n\\n    Examples:\\n    >>> data = [{'x': 1, 'y': 10, 'z': 5}, {'x': 3, 'y': 15, 'z': 6}, {'x': 2, 'y': 1, 'z': 7}]\\n    >>> print(task_func(data))\\n         x         y    z\\n    0  0.0  0.642857  0.0\\n    1  1.0  1.000000  0.5\\n    2  0.5  0.000000  1.0\\n\\n    >>> data = [{'x': -1, 'y': 0, 'z': 5}, {'x': 3, 'y': -15, 'z': 0}, {'x': 0, 'y': 1, 'z': -7}]\\n    >>> print(task_func(data))\\n          x       y         z\\n    0  0.00  0.9375  1.000000\\n    1  1.00  0.0000  0.583333\\n    2  0.25  1.0000  0.000000\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Scale all values with the keys \\\"x,\\\" \\\"y\\\" and \\\"z\\\" from a list of dictionaries \\\"d\\\" with MinMaxScaler. >>> data = [{'x': -1, 'y': 0, 'z': 5}, {'x': 3, 'y': -15, 'z': 0}, {'x': 0, 'y': 1, 'z': -7}] >>> print(task_func(data)) x       y         z 0  0.00  0.9375  1.000000 1  1.00  0.0000  0.583333 2  0.25  1.0000  0.000000\\nThe function should output with:\\n    DataFrame: A pandas DataFrame with scaled values.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.preprocessing import MinMaxScaler\\n# Updated function to handle empty input list\\ndef task_func(d):\\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":[]}