{"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":"99138466-f5d3-5a4d-8daf-89a31b1dc21d","task_key":"default--v0~2e1~2e0~5fhf--99138466-f5d3-5a4d-8daf-89a31b1dc21d","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 99138466-f5d3-5a4d-8daf-89a31b1dc21d","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport scipy.stats as stats\\nimport matplotlib.pyplot as plt\\nimport random\\nfrom matplotlib.axes import Axes\\nclass ValueObject:\\n    value = 0\\n    def __init__(self, mu=0, std=1, seed=77):\\n        random.seed(seed)\\n        self.value = random.gauss(mu, std)\\ndef task_func(obj_list) -> Axes:\\n\",\"complete_prompt\":\"import numpy as np\\nimport scipy.stats as stats\\nimport matplotlib.pyplot as plt\\nimport random\\nfrom matplotlib.axes import Axes\\n\\n\\nclass ValueObject:\\n    value = 0\\n\\n    def __init__(self, mu=0, std=1, seed=77):\\n        random.seed(seed)\\n        self.value = random.gauss(mu, std)\\n\\n\\ndef task_func(obj_list) -> Axes:\\n    '''\\n    Draw the histogram and the custom normal distribution curve from the mean and standard deviation\\n    derived from the values of a list of ValueObjects and return the plotted Axes. For an empty list,\\n    the mean and the standard deviation is 0.\\n    \\n    Parameters:\\n    obj_list (list): The list of objects.\\n    attr (str): The attribute to plot.\\n\\n    Returns:\\n    Axes: The plotted Axes.\\n\\n    Requirements:\\n    - numpy\\n    - scipy.stats\\n    - matplotlib\\n    - random\\n\\n    Example:\\n    >>> obj_list = [ValueObject(mu=23, std=77), ValueObject(mu=23, std=77, seed=222), ValueObject(mu=23, std=77, seed=333)]\\n    >>> ax = task_func(obj_list)\\n    >>> type(ax)\\n    <class 'matplotlib.axes._axes.Axes'>\\n    '''\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Draw the histogram and the custom normal distribution curve from the mean and standard deviation derived from the values of a list of ValueObjects and return the plotted Axes. For an empty list, the mean and the standard deviation is 0.\\nThe function should output with:\\n    Axes: The plotted Axes.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport scipy.stats as stats\\nimport matplotlib.pyplot as plt\\nimport random\\nfrom matplotlib.axes import Axes\\nclass ValueObject:\\n    value = 0\\n    def __init__(self, mu=0, std=1, seed=77):\\n        random.seed(seed)\\n        self.value = random.gauss(mu, std)\\ndef task_func(obj_list) -> Axes:\\n```\",\"libs\":\"['random', '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":[]}