{"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":"noindex","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[]}},"task_id":"9d02d105-331b-513d-b37a-253474fea2f4","task_key":"default--v0~2e1~2e0~5fhf--9d02d105-331b-513d-b37a-253474fea2f4","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 9d02d105-331b-513d-b37a-253474fea2f4","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\nimport matplotlib.pyplot as plt\\n# Constants\\nFEATURE_NAMES = [\\\"Feature 1\\\", \\\"Feature 2\\\", \\\"Feature 3\\\", \\\"Feature 4\\\", \\\"Feature 5\\\"]\\ndef task_func(data_matrix):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\nimport matplotlib.pyplot as plt\\n\\n# Constants\\nFEATURE_NAMES = [\\\"Feature 1\\\", \\\"Feature 2\\\", \\\"Feature 3\\\", \\\"Feature 4\\\", \\\"Feature 5\\\"]\\n\\n\\ndef task_func(data_matrix):\\n    \\\"\\\"\\\"\\n    Standardize a 2D data matrix, calculate the mean value of each row and then visualize the distribution of the mean values with an histogram.\\n    - Each row of the matrix represent a data point, its length is the same as that of FEATURE_NAMES.\\n    - The plot title should be 'Distribution of Means'.\\n\\n    Parameters:\\n    data_matrix (numpy.array): The 2D data matrix.\\n\\n    Returns:\\n    tuple: A tuple containing:\\n        - pandas.DataFrame: A DataFrame containing the standardized data and the mean of each row.\\n                            Its column names should be FEATURE_NAMES and 'Mean'.\\n        - matplotlib.axes.Axes: The histogram plot of the distribution of means.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn.preprocessing.StandardScaler\\n    - matplotlib.pyplot\\n\\n    Example:\\n    >>> import numpy as np\\n    >>> data = np.array([[6, 8, 1, 3, 4], [-1, 0, 3, 5, 1]])\\n    >>> df, ax = task_func(data)\\n    >>> print(df)\\n       Feature 1  Feature 2  Feature 3  Feature 4  Feature 5  Mean\\n    0        1.0        1.0       -1.0       -1.0        1.0   0.2\\n    1       -1.0       -1.0        1.0        1.0       -1.0  -0.2\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Standardize a 2D data matrix, calculate the mean value of each row and then visualize the distribution of the mean values with an histogram. - Each row of the matrix represent a data point, its length is the same as that of FEATURE_NAMES. - The plot title should be 'Distribution of Means'.\\nThe function should output with:\\n    tuple: A tuple containing:\\n    pandas.DataFrame: A DataFrame containing the standardized data and the mean of each row.\\n    Its column names should be FEATURE_NAMES and 'Mean'.\\n    matplotlib.axes.Axes: The histogram plot of the distribution of means.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\nimport matplotlib.pyplot as plt\\n# Constants\\nFEATURE_NAMES = [\\\"Feature 1\\\", \\\"Feature 2\\\", \\\"Feature 3\\\", \\\"Feature 4\\\", \\\"Feature 5\\\"]\\ndef task_func(data_matrix):\\n```\",\"libs\":\"['pandas', 'matplotlib', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}