{"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":"cca02587-373d-5f06-9aa2-49740c274a9e","task_key":"default--v0~2e1~2e0~5fhf--cca02587-373d-5f06-9aa2-49740c274a9e","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf cca02587-373d-5f06-9aa2-49740c274a9e","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from scipy.stats import zscore\\nimport matplotlib.pyplot as plt\\ndef task_func(df):\\n\",\"complete_prompt\":\"from scipy.stats import zscore\\nimport matplotlib.pyplot as plt\\n\\n\\ndef task_func(df):\\n    \\\"\\\"\\\"\\n    Calculate Z-scores for numeric columns in a DataFrame and draw a histogram for each column.\\n    - Missing values are replaced by the column's average.\\n    - The histograms are plotted with 10 bins.\\n\\n    Parameters:\\n    - df (pandas.DataFrame): The input pandas DataFrame with numeric columns.\\n\\n    Returns:\\n    - tuple:\\n        1. pandas.DataFrame: A DataFrame with computed z-scores.\\n        2. list: A list of Axes objects representing the histograms of the numeric columns.\\n\\n    Requirements:\\n    - pandas.\\n    - numpy.\\n    - scipy.stats.zscore.\\n    - matplotlib.pyplot.\\n\\n    Example:\\n    >>> import pandas as pd\\n    >>> import numpy as np\\n    >>> df_input = pd.DataFrame([[1, 2, 3], [4, 5, 6], [7.0, np.nan, 9.0]], columns=[\\\"col1\\\", \\\"col2\\\", \\\"col3\\\"])\\n    >>> zscore_output, plots = task_func(df_input)\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Calculate Z-scores for numeric columns in a DataFrame and draw a histogram for each column. - Missing values are replaced by the column's average. - The histograms are plotted with 10 bins.\\nThe function should output with:\\n    tuple:\\n    1. pandas.DataFrame: A DataFrame with computed z-scores.\\n    2. list: A list of Axes objects representing the histograms of the numeric columns.\\nYou should write self-contained code starting with:\\n```\\nfrom scipy.stats import zscore\\nimport matplotlib.pyplot as plt\\ndef task_func(df):\\n```\",\"libs\":\"['matplotlib', 'scipy']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}