{"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":"b5c462ae-3524-5865-b8d1-ae76fae05703","task_key":"default--v0~2e1~2e0~5fhf--b5c462ae-3524-5865-b8d1-ae76fae05703","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf b5c462ae-3524-5865-b8d1-ae76fae05703","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import inspect\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\ndef task_func(f_list):\\n\",\"complete_prompt\":\"import inspect\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\n\\ndef task_func(f_list):\\n    \\\"\\\"\\\"\\n    Analyzes a list of functions and draws a bar chart showing the number of arguments for each function.\\n    The function names are listed along the x-axis, and the number of arguments are represented as bars.\\n    This method showcases the integration of function introspection, data frame creation, and data visualization.\\n\\n    Parameters:\\n    f_list (list): List of functions to inspect.\\n\\n    Returns:\\n    pandas.DataFrame: Returns a DataFrame containing the function names and their respective number of arguments.\\n\\n    Raises:\\n    ValueError: if the input contains lambda function\\n\\n    Requirements:\\n    - inspect\\n    - matplotlib.pyplot\\n    - pandas\\n\\n    Examples:\\n    >>> def f(x): x*x\\n    >>> def g(x, y=2): return x*y\\n    >>> task_func([f, g])\\n                   Number of Arguments\\n    Function Name                     \\n    f                                1\\n    g                                2\\n    >>> lambda_func = lambda x: x * 2\\n    >>> task_func([f, lambda_func])\\n    Traceback (most recent call last):\\n    ...\\n    ValueError: The function should not be a lambda function.\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Analyzes a list of functions and draws a bar chart showing the number of arguments for each function. The function names are listed along the x-axis, and the number of arguments are represented as bars. This method showcases the integration of function introspection, data frame creation, and data visualization.\\nThe function should raise the exception for: ValueError: if the input contains lambda function\\nThe function should output with:\\n    pandas.DataFrame: Returns a DataFrame containing the function names and their respective number of arguments.\\nYou should write self-contained code starting with:\\n```\\nimport inspect\\nimport matplotlib.pyplot as plt\\nimport pandas as pd\\ndef task_func(f_list):\\n```\",\"libs\":\"['pandas', 'inspect', 'matplotlib']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}