{"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":"e7245d3d-18ec-50ae-b1d0-b91d991c3f25","task_key":"default--v0~2e1~2e0~5fhf--e7245d3d-18ec-50ae-b1d0-b91d991c3f25","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf e7245d3d-18ec-50ae-b1d0-b91d991c3f25","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(n_rows, scale_cols, columns=['A', 'B', 'C', 'D', 'E'], random_seed=None):\\n\",\"complete_prompt\":\"import numpy as np\\nimport pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\n\\n\\ndef task_func(n_rows, scale_cols, columns=['A', 'B', 'C', 'D', 'E'], random_seed=None):\\n    \\\"\\\"\\\"\\n    Generate a DataFrame with columns 'columns' and fill them with random\\n    values. Scale the columns at the provided indexes with sklearn StandardScaler.\\n    If scale_cols is empty no column is scaled\\n        \\n    Parameters:\\n    n_rows (int): The number of rows in the DataFrame.\\n    scale_cols (list of int): The indices of columns to be scaled. The indices are based on the predefined column names.\\n    columns (list of str, optional): The columns to be included in the DataFrame. Defaults to ['A', 'B', 'C', 'D', 'E'].\\n    random_seed (int): Seed used in rng. Default is None.\\n\\n    Returns:\\n    DataFrame: The resulting DataFrame after scaling the selected columns.\\n\\n    Requirements:\\n    - numpy\\n    - pandas\\n    - sklearn\\n    \\n    Example:\\n    >>> df = task_func(3, [1], columns=['test', 'scale'], random_seed=1)\\n    >>> print(df)\\n       test     scale\\n    0    37  1.162476\\n    1    72  0.116248\\n    2    75 -1.278724\\n\\n    >>> df = task_func(5, [1, 2, 3], random_seed=12)\\n    >>> print(df)\\n        A         B         C         D   E\\n    0  75 -0.840307 -0.791926 -1.462784   3\\n    1  67  0.673481  1.517859 -0.855820  49\\n    2  52 -1.519967 -0.406962  1.177511  34\\n    3  75  0.611694 -1.121896  0.782984  13\\n    4  82  1.075099  0.802925  0.358109  35\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generate a DataFrame with columns 'columns' and fill them with random values. Scale the columns at the provided indexes with sklearn StandardScaler. If scale_cols is empty no column is scaled >>> df = task_func(5, [1, 2, 3], random_seed=12) >>> print(df) A         B         C         D   E 0  75 -0.840307 -0.791926 -1.462784   3 1  67  0.673481  1.517859 -0.855820  49 2  52 -1.519967 -0.406962  1.177511  34 3  75  0.611694 -1.121896  0.782984  13 4  82  1.075099  0.802925  0.358109  35\\nThe function should output with:\\n    DataFrame: The resulting DataFrame after scaling the selected columns.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(n_rows, scale_cols, columns=['A', 'B', 'C', 'D', 'E'], random_seed=None):\\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":[]}