{"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":"894b5d4b-1857-539b-841c-844eacf2b2ca","task_key":"default--v0~2e1~2e0~5fhf--894b5d4b-1857-539b-841c-844eacf2b2ca","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 894b5d4b-1857-539b-841c-844eacf2b2ca","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport seaborn as sns\\nfrom scipy.stats import zscore\\ndef task_func(data_matrix):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport seaborn as sns\\nfrom scipy.stats import zscore\\n\\n\\ndef task_func(data_matrix):\\n    \\\"\\\"\\\"\\n    Calculate the Z-values of a 2D data matrix, calculate the mean value of each row and then visualize the correlation matrix of the Z-values with a heatmap.\\n\\n    Parameters:\\n    data_matrix (numpy.array): The 2D data matrix of shape (m, n) where m is the number of rows and n is the number of columns.\\n\\n    Returns:\\n    tuple: A tuple containing:\\n      - pandas.DataFrame: A DataFrame with columns 'Feature 1', 'Feature 2', ..., 'Feature n' containing the Z-scores (per matrix row).\\n                      There is also an additional column 'Mean' the mean of z-score per row.\\n      - matplotlib.axes.Axes: The Axes object of the plotted heatmap.\\n\\n    Requirements:\\n    - pandas\\n    - seaborn\\n    - scipy.stats.zscore\\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   0.662085   1.489691  -1.406930  -0.579324  -0.165521 -2.053913e-16\\n    1  -1.207020  -0.742781   0.649934   1.578410  -0.278543 -3.330669e-17\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Calculate the Z-values of a 2D data matrix, calculate the mean value of each row and then visualize the correlation matrix of the Z-values with a heatmap.\\nThe function should output with:\\n    tuple: A tuple containing:\\n    pandas.DataFrame: A DataFrame with columns 'Feature 1', 'Feature 2', ..., 'Feature n' containing the Z-scores (per matrix row).\\n    There is also an additional column 'Mean' the mean of z-score per row.\\n    matplotlib.axes.Axes: The Axes object of the plotted heatmap.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport seaborn as sns\\nfrom scipy.stats import zscore\\ndef task_func(data_matrix):\\n```\",\"libs\":\"['pandas', 'scipy', 'seaborn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}