{"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":"953acc58-1fd1-5244-9366-6d97c12de0d5","task_key":"default--v0~2e1~2e0~5fhf--953acc58-1fd1-5244-9366-6d97c12de0d5","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 953acc58-1fd1-5244-9366-6d97c12de0d5","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport numpy as np\\nCATEGORIES = [\\\"Electronics\\\", \\\"Clothing\\\", \\\"Home Decor\\\", \\\"Automotive\\\", \\\"Books\\\"]\\ndef task_func(s1, s2):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport numpy as np\\n\\n\\nCATEGORIES = [\\\"Electronics\\\", \\\"Clothing\\\", \\\"Home Decor\\\", \\\"Automotive\\\", \\\"Books\\\"]\\n\\n\\ndef task_func(s1, s2):\\n    \\\"\\\"\\\"\\n    Compares and visualizes the sales data of two stores for predefined categories.\\n    The function generates a bar plot for categories where both stores have sales exceeding a specified threshold.\\n    The Euclidean distance between the two series is also computed.\\n    \\n    Parameters:\\n    s1 (pd.Series): Sales data for store 1, indexed by categories.\\n    s2 (pd.Series): Sales data for store 2, indexed by categories.\\n\\n    Returns:\\n    matplotlib.axes.Axes or None: A bar plot for categories where both stores' sales exceed the threshold of 200,\\n    or None if no such categories exist.\\n    float: The Euclidean distance between the two series or 0.0 if no categories meet the threshold.\\n\\n    Requirements:\\n    - pandas\\n    - numpy\\n\\n    Example:\\n    >>> np.random.seed(seed=32)\\n    >>> s1 = pd.Series(np.random.randint(100, 500, size=5), index=CATEGORIES)\\n    >>> s2 = pd.Series(np.random.randint(150, 600, size=5), index=CATEGORIES)\\n    >>> ax, edit_distance = task_func(s1, s2)\\n    >>> ax.get_title()\\n    'Sales Comparison Above Threshold in Categories'\\n    >>> edit_distance\\n    387.5590277622236\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Compares and visualizes the sales data of two stores for predefined categories. The function generates a bar plot for categories where both stores have sales exceeding a specified threshold. The Euclidean distance between the two series is also computed.\\nThe function should output with:\\n    matplotlib.axes.Axes or None: A bar plot for categories where both stores' sales exceed the threshold of 200,\\n    or None if no such categories exist.\\n    float: The Euclidean distance between the two series or 0.0 if no categories meet the threshold.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport numpy as np\\nCATEGORIES = [\\\"Electronics\\\", \\\"Clothing\\\", \\\"Home Decor\\\", \\\"Automotive\\\", \\\"Books\\\"]\\ndef task_func(s1, s2):\\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":[]}