{"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":"820a85ea-0497-5289-b58b-056842a9c702","task_key":"default--v0~2e1~2e0~5fhf--820a85ea-0497-5289-b58b-056842a9c702","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 820a85ea-0497-5289-b58b-056842a9c702","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy.stats import norm\\ndef task_func(mu=0, sigma=1):\\n\",\"complete_prompt\":\"import numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy.stats import norm\\n\\ndef task_func(mu=0, sigma=1):\\n    \\\"\\\"\\\"\\n    Draw and return a subplot of a normal distribution with the given mean and standard deviation,\\n    utilizing numpy's linspace to create an array of 100 linearly spaced numbers between\\n    `mu - 3*sigma` and `mu + 3*sigma`.\\n\\n    Parameters:\\n    mu (float): The mean of the distribution. Default is 0.\\n    sigma (float): The standard deviation of the distribution. Default is 1.\\n\\n    Returns:\\n    matplotlib.axes.Axes: The subplot representing the normal distribution.\\n\\n    Requirements:\\n    - numpy\\n    - matplotlib.pyplot\\n    - scipy.stats.norm\\n\\n    Example:\\n    >>> ax = task_func(mu=5, sigma=2)\\n    >>> ax\\n    <Axes: >\\n    >>> type(ax)\\n    <class 'matplotlib.axes._axes.Axes'>\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Draw and return a subplot of a normal distribution with the given mean and standard deviation, utilizing numpy's linspace to create an array of 100 linearly spaced numbers between `mu - 3*sigma` and `mu + 3*sigma`.\\nThe function should output with:\\n    matplotlib.axes.Axes: The subplot representing the normal distribution.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport matplotlib.pyplot as plt\\nfrom scipy.stats import norm\\ndef task_func(mu=0, sigma=1):\\n```\",\"libs\":\"['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":[]}