{"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":"50b05a53-df08-5160-b254-77ed87fa0bb3","task_key":"default--v0~2e1~2e0~5fhf--50b05a53-df08-5160-b254-77ed87fa0bb3","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 50b05a53-df08-5160-b254-77ed87fa0bb3","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport numpy as np\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(df):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport numpy as np\\nfrom sklearn.preprocessing import MinMaxScaler\\n\\ndef task_func(df):\\n    \\\"\\\"\\\"\\n    Scale the 'Age' and 'Income' columns between 0 and 1 for each group by 'id' in the provided pandas DataFrame. \\n    Additionally, create a histogram of the 'Income' column after scaling and return both the scaled DataFrame \\n    and the histogram data.\\n\\n    Parameters:\\n    df (DataFrame): The pandas DataFrame with columns ['id', 'age', 'income'].\\n\\n    Returns:\\n    tuple: A tuple containing the scaled DataFrame and the histogram data for the 'income' column.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn.preprocessing.MinMaxScaler\\n    - numpy\\n\\n    Example:\\n    >>> df = pd.DataFrame({'id': [1, 1, 2, 2, 3, 3], 'age': [25, 26, 35, 36, 28, 29],'income': [50000, 60000, 70000, 80000, 90000, 100000]})\\n    >>> df_scaled, income_hist = task_func(df)\\n    >>> print(df_scaled.iloc[0]['age'])\\n    0.0\\n    >>> print(df_scaled.iloc[0]['income'])\\n    0.0\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Scale the 'Age' and 'Income' columns between 0 and 1 for each group by 'id' in the provided pandas DataFrame. Additionally, create a histogram of the 'Income' column after scaling and return both the scaled DataFrame and the histogram data.\\nThe function should output with:\\n    tuple: A tuple containing the scaled DataFrame and the histogram data for the 'income' column.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport numpy as np\\nfrom sklearn.preprocessing import MinMaxScaler\\ndef task_func(df):\\n```\",\"libs\":\"['pandas', 'numpy', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}