{"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":"c75d0a81-14af-5385-83cb-054565a14d75","task_key":"default--v0~2e1~2e0~5fhf--c75d0a81-14af-5385-83cb-054565a14d75","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf c75d0a81-14af-5385-83cb-054565a14d75","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(data):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\n\\n\\ndef task_func(data):\\n    \\\"\\\"\\\"Scales numeric columns of a data dictionary using the StandardScaler.\\n\\n    This function scales the numeric columns of a dataframe using the StandardScaler from scikit-learn.\\n    Non-numeric columns remain unchanged. If a column contains mixed data types, it tries to convert the entire column\\n    to float. If any value in the column cannot be converted to float, the entire column is left unchanged.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn.preprocessing.StandardScaler\\n    \\n    Parameters:\\n    - data (dict): Input data.\\n\\n    Returns:\\n    - pd.DataFrame: Dataframe with scaled numeric columns.\\n\\n    Example:\\n    >>> result = task_func({'x': [10, 20, 30, 40]})\\n    >>> result\\n              x\\n    0 -1.341641\\n    1 -0.447214\\n    2  0.447214\\n    3  1.341641\\n    >>> result2 = task_func({'a': [10.5, 23.4, 15.6, 78.9],'b': [45.6, 67.8, 89.0, 12.3],'c': ['apple', 'banana', 'cherry', 'date']})\\n    >>> result2\\n              a         b       c\\n    0 -0.788098 -0.284409   apple\\n    1 -0.317428  0.497496  banana\\n    2 -0.602019  1.244180  cherry\\n    3  1.707546 -1.457267    date\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Scales numeric columns of a data dictionary using the StandardScaler. This function scales the numeric columns of a dataframe using the StandardScaler from scikit-learn. Non-numeric columns remain unchanged. If a column contains mixed data types, it tries to convert the entire column to float. If any value in the column cannot be converted to float, the entire column is left unchanged.\\nThe function should output with:\\n    pd.DataFrame: Dataframe with scaled numeric columns.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(data):\\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":[]}