{"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":"3b816f85-dedd-53ed-b1c0-60354b5fdc38","task_key":"default--v0~2e1~2e0~5fhf--3b816f85-dedd-53ed-b1c0-60354b5fdc38","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 3b816f85-dedd-53ed-b1c0-60354b5fdc38","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import MinMaxScaler\\nimport matplotlib.pyplot as plt\\ndef task_func(data):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.preprocessing import MinMaxScaler\\nimport matplotlib.pyplot as plt\\n\\n\\ndef task_func(data):\\n    \\\"\\\"\\\"\\n    Normalizes a given dataset using MinMax scaling and calculates the average of each row. This average is then\\n    added as a new column 'Average' to the resulting DataFrame. The function also visualizes these averages in a plot.\\n\\n    Parameters:\\n    data (numpy.array): A 2D array where each row represents a sample and each column a feature, with a\\n    shape of (n_samples, 8).\\n\\n    Returns:\\n    DataFrame: A pandas DataFrame where data is normalized, with an additional column 'Average' representing the\\n    mean of each row.\\n    Axes: A matplotlib Axes object showing a bar subplot of the average values across the dataset.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn\\n    - matplotlib\\n\\n    Example:\\n    >>> import numpy as np\\n    >>> data = np.array([[1, 2, 3, 4, 4, 3, 7, 1], [6, 2, 3, 4, 3, 4, 4, 1]])\\n    >>> df, ax = task_func(data)\\n    >>> print(df.round(2))\\n         A    B    C    D    E    F    G    H  Average\\n    0  0.0  0.0  0.0  0.0  1.0  0.0  1.0  0.0     0.25\\n    1  1.0  0.0  0.0  0.0  0.0  1.0  0.0  0.0     0.25\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Normalizes a given dataset using MinMax scaling and calculates the average of each row. This average is then added as a new column 'Average' to the resulting DataFrame. The function also visualizes these averages in a plot.\\nThe function should output with:\\n    DataFrame: A pandas DataFrame where data is normalized, with an additional column 'Average' representing the\\n    mean of each row.\\n    Axes: A matplotlib Axes object showing a bar subplot of the average values across the dataset.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.preprocessing import MinMaxScaler\\nimport matplotlib.pyplot as plt\\ndef task_func(data):\\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":[]}