{"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":"e3fa0e77-4193-51ac-9e97-e71a02fff873","task_key":"default--v0~2e1~2e0~5fhf--e3fa0e77-4193-51ac-9e97-e71a02fff873","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf e3fa0e77-4193-51ac-9e97-e71a02fff873","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\n# Constants\\nCOLUMNS = ['col1', 'col2', 'col3']\\ndef task_func(data):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\n\\n# Constants\\nCOLUMNS = ['col1', 'col2', 'col3']\\n\\ndef task_func(data):\\n    \\\"\\\"\\\"\\n    You are given a list of elements. Each element is a list with the same length as COLUMNS, representing one row a dataframe df to create. Visualize the distribution of different values in a column \\\"col3\\\" of a pandas DataFrame df, grouped by \\\"col1\\\" and \\\"col2,\\\" using a heatmap.\\n\\n    Parameters:\\n    - data (list): A list of elements. Each element is a list with the same length as COLUMNS, representing one row of the dataframe to build.\\n\\n    Returns:\\n    - tuple:\\n        pandas.DataFrame: The DataFrame of the analyzed data.\\n        plt.Axes: The heatmap visualization.\\n\\n    Requirements:\\n    - pandas\\n    - seaborn\\n    - matplotlib\\n\\n    Example:\\n    >>> data = [[1, 1, 1], [1, 1, 1], [1, 1, 2], [1, 2, 3], [1, 2, 3], [1, 2, 3], [2, 1, 1], [2, 1, 2], [2, 1, 3], [2, 2, 3], [2, 2, 3], [2, 2, 3]]\\n    >>> analyzed_df, ax = task_func(data)\\n    >>> print(analyzed_df)\\n    col2  1  2\\n    col1      \\n    1     2  1\\n    2     3  1\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"You are given a list of elements. Each element is a list with the same length as COLUMNS, representing one row a dataframe df to create. Visualize the distribution of different values in a column \\\"col3\\\" of a pandas DataFrame df, grouped by \\\"col1\\\" and \\\"col2,\\\" using a heatmap.\\nThe function should output with:\\n    tuple:\\n    pandas.DataFrame: The DataFrame of the analyzed data.\\n    plt.Axes: The heatmap visualization.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport seaborn as sns\\nimport matplotlib.pyplot as plt\\n# Constants\\nCOLUMNS = ['col1', 'col2', 'col3']\\ndef task_func(data):\\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":[]}