{"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":"index","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[],"history":"","construction":"","limitations":"","saturation":"","scores":[]}},"task_id":"e4cd1819-b629-52f5-858d-cd06a6ca16a1","task_key":"default--v0~2e1~2e0~5fhf--e4cd1819-b629-52f5-858d-cd06a6ca16a1","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf e4cd1819-b629-52f5-858d-cd06a6ca16a1","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport matplotlib.pyplot as plt\\ndef task_func(data, column):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport matplotlib.pyplot as plt\\n\\ndef task_func(data, column):\\n    \\\"\\\"\\\"\\n    Draw and return a bar chart that shows the distribution of categories in a specific column of a dictionary.\\n    \\n    Note:\\n    The categories are defined by the constant CATEGORIES, \\n    which is a list containing ['A', 'B', 'C', 'D', 'E']. If some categories are missing in the DataFrame, \\n    they will be included in the plot with a count of zero.\\n    The x label of the plot is set to 'Category', the y label is set to 'Count', and the title is set to 'Distribution of {column}'.\\n    \\n    Parameters:\\n    - data (dict): A dictionary where the keys are the column names and the values are the column values.\\n    - column (str): The name of the column in the DataFrame that contains the categories.\\n    \\n    Returns:\\n    - matplotlib.axes._axes.Axes: The Axes object for the generated plot.\\n    \\n    Requirements:\\n    - pandas\\n    - numpy\\n    - matplotlib.pyplot\\n    \\n    Example:\\n    >>> data = {'Category': ['A', 'B', 'B', 'C', 'A', 'D', 'E', 'E', 'D']}\\n    >>> ax = task_func(data, 'Category')    \\n    >>> data = {'Type': ['A', 'A', 'C', 'E', 'D', 'E', 'D']}\\n    >>> ax = task_func(data, 'Type')\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Draw and return a bar chart that shows the distribution of categories in a specific column of a dictionary.\\nNote that: The categories are defined by the constant CATEGORIES, which is a list containing ['A', 'B', 'C', 'D', 'E']. If some categories are missing in the DataFrame, they will be included in the plot with a count of zero. The x label of the plot is set to 'Category', the y label is set to 'Count', and the title is set to 'Distribution of {column}'.\\nThe function should output with:\\n    matplotlib.axes._axes.Axes: The Axes object for the generated plot.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\ndef task_func(data, column):\\n```\",\"libs\":\"['pandas', 'matplotlib']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}