{"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":"88128b25-7465-56ea-9965-f84e628b353f","task_key":"default--v0~2e1~2e0~5fhf--88128b25-7465-56ea-9965-f84e628b353f","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 88128b25-7465-56ea-9965-f84e628b353f","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"from sklearn.preprocessing import StandardScaler\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\ndef task_func(df):\\n\",\"complete_prompt\":\"from sklearn.preprocessing import StandardScaler\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\n\\ndef task_func(df):\\n    \\\"\\\"\\\"\\n    Standardize numeric columns in a DataFrame and return the heatmap of the correlation matrix. Missing values are replaced by the column's average.\\n\\n    Parameters:\\n    - df (pandas.DataFrame): The pandas DataFrame to be standardized.\\n\\n    Returns:\\n    - DataFrame: The pandas DataFrame after standardization.\\n    - Axes: A heatmap of the correlation matrix.\\n\\n    Requirements:\\n    - sklearn.preprocessing.StandardScaler\\n    - seaborn\\n    - matplotlib.pyplot\\n\\n    Example:\\n    >>> import pandas as pd\\n    >>> import numpy as np\\n    >>> df = pd.DataFrame([[1,2,3],[4,5,6],[7.0,np.nan,9.0]], columns=[\\\"c1\\\",\\\"c2\\\",\\\"c3\\\"])\\n    >>> standardized_df, heatmap = task_func(df)\\n    >>> print(standardized_df)\\n             c1        c2        c3\\n    0 -1.224745 -1.224745 -1.224745\\n    1  0.000000  1.224745  0.000000\\n    2  1.224745  0.000000  1.224745\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Standardize numeric columns in a DataFrame and return the heatmap of the correlation matrix. Missing values are replaced by the column's average.\\nThe function should output with:\\n    DataFrame: The pandas DataFrame after standardization.\\n    Axes: A heatmap of the correlation matrix.\\nYou should write self-contained code starting with:\\n```\\nfrom sklearn.preprocessing import StandardScaler\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\ndef task_func(df):\\n```\",\"libs\":\"['sklearn', 'matplotlib', 'seaborn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}