{"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":"9c0c43bd-f254-5504-bf17-3ff62abf38d9","task_key":"default--v0~2e1~2e0~5fhf--9c0c43bd-f254-5504-bf17-3ff62abf38d9","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 9c0c43bd-f254-5504-bf17-3ff62abf38d9","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\ndef task_func(df):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\n\\ndef task_func(df):\\n    \\\"\\\"\\\"\\n    Perform exploratory data analysis on a dataframe. This function converts the 'date' column to an ordinal format,\\n    creates a correlation matrix, and generates a pair plot of the dataframe.\\n\\n    Parameters:\\n        df (pandas.DataFrame): A dataframe with columns 'group', 'date', and 'value'. The 'date' column should be in datetime format.\\n\\n    Returns:\\n        matplotlib.figure.Figure: The figure object for the correlation matrix heatmap.\\n        seaborn.axisgrid.PairGrid: The PairGrid object for the pair plot.\\n\\n        The title of the plot is 'Correlation Matrix'. \\n    Raises:\\n        ValueError: If the dataframe is empty, if required columns are missing, or if 'date' column is not in datetime format.\\n\\n    Requirements:\\n        - pandas\\n        - matplotlib.pyplot\\n        - seaborn\\n\\n    Example:\\n        >>> df = pd.DataFrame({\\n        ...     \\\"group\\\": [\\\"A\\\", \\\"A\\\", \\\"A\\\", \\\"B\\\", \\\"B\\\"],\\n        ...     \\\"date\\\": pd.to_datetime([\\\"2022-01-02\\\", \\\"2022-01-13\\\", \\\"2022-02-01\\\", \\\"2022-02-23\\\", \\\"2022-03-05\\\"]),\\n        ...     \\\"value\\\": [10, 20, 16, 31, 56],\\n        ... })\\n        >>> heatmap_fig, pairplot_grid = task_func(df)\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Perform exploratory data analysis on a dataframe. This function converts the 'date' column to an ordinal format, creates a correlation matrix, and generates a pair plot of the dataframe. The title of the plot is 'Correlation Matrix'.\\nThe function should raise the exception for: ValueError: If the dataframe is empty, if required columns are missing, or if 'date' column is not in datetime format.\\nThe function should output with:\\n    matplotlib.figure.Figure: The figure object for the correlation matrix heatmap.\\n    seaborn.axisgrid.PairGrid: The PairGrid object for the pair plot.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\nimport seaborn as sns\\ndef task_func(df):\\n```\",\"libs\":\"['pandas', '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":[]}