{"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":"5445ab17-1c50-5594-b4f9-f164e6a8d383","task_key":"default--v0~2e1~2e0~5fhf--5445ab17-1c50-5594-b4f9-f164e6a8d383","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 5445ab17-1c50-5594-b4f9-f164e6a8d383","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport matplotlib.pyplot as plt\\n# Constants\\nCATEGORIES = [\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\", \\\"E\\\"]\\ndef task_func(data_list):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport matplotlib.pyplot as plt\\n\\n# Constants\\nCATEGORIES = [\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\", \\\"E\\\"]\\n\\n\\ndef task_func(data_list):\\n    \\\"\\\"\\\"\\n    Processes a list of category labels to create a histogram that visualizes their distribution.\\n    This histogram compares the distribution of a predefined set of categories (A, B, C, D, E)\\n    with any additional categories found in the input list.\\n\\n    Parameters:\\n    - data_list (list): A list containing category labels (strings).\\n\\n    Returns:\\n    - Axes object (matplotlib.axes._axes.Axes): The histogram displaying the distribution of categories.\\n\\n    Requirements:\\n    - pandas\\n    - matplotlib\\n\\n    Notes:\\n    - The function evaluates the distribution of predefined categories ('A', 'B', 'C', 'D', 'E') and checks for uniformity.\\n      If the distribution is not uniform, a warning message of \\\"The distribution of predefined categories is not uniform.\\\" is printed.\\n    - Categories in the data_list that are not among the predefined categories are identified and included in the histogram.\\n    - The ax.bar call in the function creates a bar plot on the axes object. It uses the following parameters:\\n        * all_categories: The categories to be displayed on the x-axis, including both predefined and extra categories.\\n        * category_counts.reindex(all_categories, fill_value=0): The counts of each category, where categories not found\\n          in the data_list are assigned a count of 0.\\n        * width=0.8: Sets the width of the bars in the bar plot.\\n        * align=\\\"center\\\": Aligns the bars with the center of the x-ticks.\\n\\n    Raises:\\n    - ValueError: If the input data_list is empty, the function raises a ValueError with the message \\\"The data list is empty.\\\"\\n      In this case, no histogram is generated and the function terminates.\\n\\n\\n    Example:\\n    >>> data = ['A', 'B', 'C', 'D', 'E', 'F', 'G']\\n    >>> ax = task_func(data)\\n    >>> ax.get_xticks()\\n    array([0., 1., 2., 3., 4., 5., 6.])\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Processes a list of category labels to create a histogram that visualizes their distribution. This histogram compares the distribution of a predefined set of categories (A, B, C, D, E) with any additional categories found in the input list.\\nNote that: Notes: The function evaluates the distribution of predefined categories ('A', 'B', 'C', 'D', 'E') and checks for uniformity. If the distribution is not uniform, a warning message of \\\"The distribution of predefined categories is not uniform.\\\" is printed. Categories in the data_list that are not among the predefined categories are identified and included in the histogram. The ax.bar call in the function creates a bar plot on the axes object. It uses the following parameters: * all_categories: The categories to be displayed on the x-axis, including both predefined and extra categories. * category_counts.reindex(all_categories, fill_value=0): The counts of each category, where categories not found in the data_list are assigned a count of 0. * width=0.8: Sets the width of the bars in the bar plot. * align=\\\"center\\\": Aligns the bars with the center of the x-ticks.\\nThe function should raise the exception for: ValueError: If the input data_list is empty, the function raises a ValueError with the message \\\"The data list is empty.\\\" In this case, no histogram is generated and the function terminates.\\nThe function should output with:\\n    Axes object (matplotlib.axes._axes.Axes): The histogram displaying the distribution of categories.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport matplotlib.pyplot as plt\\n# Constants\\nCATEGORIES = [\\\"A\\\", \\\"B\\\", \\\"C\\\", \\\"D\\\", \\\"E\\\"]\\ndef task_func(data_list):\\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":[]}