{"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":"71e6af56-7ab4-5dc2-a15c-ed7e45c0ceb9","task_key":"default--v0~2e1~2e0~5fhf--71e6af56-7ab4-5dc2-a15c-ed7e45c0ceb9","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 71e6af56-7ab4-5dc2-a15c-ed7e45c0ceb9","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport pandas as pd\\n# Constants\\nCOLUMNS = ['Column1', 'Column2', 'Column3', 'Column4', 'Column5']\\ndef task_func(length, min_value = 0, max_value = 100):\\n\",\"complete_prompt\":\"import numpy as np\\nimport pandas as pd\\n\\n# Constants\\nCOLUMNS = ['Column1', 'Column2', 'Column3', 'Column4', 'Column5']\\n\\ndef task_func(length, min_value = 0, max_value = 100):\\n    \\\"\\\"\\\"\\n    Randomly generate a pandas DataFrame with specified ranges and length, and calculate the cumulative distribution function (CDF).\\n\\n    Parameters:\\n    length (int): The length of the DataFrame to be generated.\\n    min_value (int, optional): The minimum value for random data generation. Default is 0.\\n    max_value (int, optional): The maximum value for random data generation. Default is 100.\\n\\n    Returns:\\n    DataFrame: A pandas DataFrame with the calculated cumulative distribution function (CDF).\\n\\n    Note:\\n    - DataFrame columns are defined by the COLUMNS constant.\\n\\n    Requirements:\\n    - numpy\\n    - pandas\\n    - matplotlib.pyplot\\n\\n    Example:\\n    >>> np.random.seed(0)\\n    >>> cdf = task_func(100, 0, 1)\\n    >>> print(len(cdf))\\n    1\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Randomly generate a pandas DataFrame with specified ranges and length, and calculate the cumulative distribution function (CDF).\\nNote that: DataFrame columns are defined by the COLUMNS constant.\\nThe function should output with:\\n    DataFrame: A pandas DataFrame with the calculated cumulative distribution function (CDF).\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport pandas as pd\\n# Constants\\nCOLUMNS = ['Column1', 'Column2', 'Column3', 'Column4', 'Column5']\\ndef task_func(length, min_value = 0, max_value = 100):\\n```\",\"libs\":\"['pandas', 'numpy']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}