{"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":"a29480f5-2028-5eb6-97f1-7a5ca929bac7","task_key":"default--v0~2e1~2e0~5fhf--a29480f5-2028-5eb6-97f1-7a5ca929bac7","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf a29480f5-2028-5eb6-97f1-7a5ca929bac7","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport bisect\\nimport statistics\\nimport matplotlib.pyplot as plt\\ndef task_func(data, value):\\n\",\"complete_prompt\":\"import numpy as np\\nimport bisect\\nimport statistics\\nimport matplotlib.pyplot as plt\\n\\n\\ndef task_func(data, value):\\n    \\\"\\\"\\\"\\n    Analyzes a list of numerical data, identifies values greater than the average,\\n    and counts how many values are greater than a specified value. Additionally, plots the\\n    histogram of the sorted numbers.\\n\\n    Parameters:\\n        data (list): A list of numerical data.\\n        value (float): A value to compare against the data.\\n\\n    Returns:\\n        numpy.ndarray: An array of values from the data that are greater than the average.\\n        int: The number of values in the data that are greater than the given value.\\n\\n    Requirements:\\n    - numpy\\n    - bisect\\n    - statistics\\n    - matplotlib.pyplot\\n\\n    Note:\\n    - If the data list is empty, the function returns an empty numpy.ndarray and a count of 0. This ensures\\n      the function's output remains consistent and predictable even with no input data.\\n\\n    Examples:\\n    >>> greater_avg, count = task_func([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 5)\\n    >>> greater_avg.tolist()\\n    [6, 7, 8, 9, 10]\\n    >>> count\\n    5\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Analyzes a list of numerical data, identifies values greater than the average, and counts how many values are greater than a specified value. Additionally, plots the histogram of the sorted numbers.\\nNote that: If the data list is empty, the function returns an empty numpy.ndarray and a count of 0. This ensures the function's output remains consistent and predictable even with no input data.\\nThe function should output with:\\n    numpy.ndarray: An array of values from the data that are greater than the average.\\n    int: The number of values in the data that are greater than the given value.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport bisect\\nimport statistics\\nimport matplotlib.pyplot as plt\\ndef task_func(data, value):\\n```\",\"libs\":\"['statistics', 'bisect', 'numpy', 'matplotlib']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}