{"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":"1081e435-e5b9-5fd1-acc2-8b138f2220ac","task_key":"default--v0~2e1~2e0~5fhf--1081e435-e5b9-5fd1-acc2-8b138f2220ac","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 1081e435-e5b9-5fd1-acc2-8b138f2220ac","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import matplotlib.pyplot as plt\\nimport numpy as np\\nimport pandas as pd\\nfrom scipy import stats\\ndef task_func(a, b):\\n\",\"complete_prompt\":\"import matplotlib.pyplot as plt\\nimport numpy as np\\nimport pandas as pd\\nfrom scipy import stats\\n\\n\\ndef task_func(a, b):\\n    \\\"\\\"\\\"\\n    Calculate the Pearson correlation coefficient of two lists, generate a Pandas DataFrame from these lists, and then draw a scatter plot with a regression line.\\n\\n    Parameters:\\n    a (list): A list of numbers.\\n    b (list): Another list of numbers.\\n\\n    Requirements:\\n    - numpy\\n    - pandas\\n    - scipy\\n    - matplotlib.pyplot\\n\\n    Returns:\\n    - tuple: Contains two elements:\\n        - float: The Pearson correlation coefficient.\\n        - matplotlib.axes.Axes: The Axes object of the plotted scatter plot with a regression line.\\n\\n\\n    Example:\\n    >>> correlation, ax = task_func([1, 2, 3, 4, 5], [2, 4, 6, 8, 10])\\n    >>> isinstance(correlation, float) and isinstance(ax, matplotlib.axes.Axes)\\n    True\\n    >>> round(correlation, 1)\\n    1.0\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Calculate the Pearson correlation coefficient of two lists, generate a Pandas DataFrame from these lists, and then draw a scatter plot with a regression line.\\nThe function should output with:\\n    tuple: Contains two elements:\\n    float: The Pearson correlation coefficient.\\n    matplotlib.axes.Axes: The Axes object of the plotted scatter plot with a regression line.\\nYou should write self-contained code starting with:\\n```\\nimport matplotlib.pyplot as plt\\nimport numpy as np\\nimport pandas as pd\\nfrom scipy import stats\\ndef task_func(a, b):\\n```\",\"libs\":\"['pandas', 'numpy', 'matplotlib', 'scipy']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}