{"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":"60a08104-c68a-5ab6-b8ad-265e1772a589","task_key":"default--v0~2e1~2e0~5fhf--60a08104-c68a-5ab6-b8ad-265e1772a589","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 60a08104-c68a-5ab6-b8ad-265e1772a589","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport numpy as np\\nimport random\\nfrom random import randint, seed\\n# Constants\\nCATEGORIES = ['Electronics', 'Clothing', 'Home & Kitchen', 'Books', 'Toys & Games']\\ndef task_func(mystrings, n_products, seed=0):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport numpy as np\\nimport random\\nfrom random import randint, seed\\n\\n# Constants\\nCATEGORIES = ['Electronics', 'Clothing', 'Home & Kitchen', 'Books', 'Toys & Games']\\n\\ndef task_func(mystrings, n_products, seed=0):\\n    \\\"\\\"\\\"\\n    Create a product catalog DataFrame where each row represents a product with the following columns:\\n    - 'Product Name': The name of the product with spaces replaced by underscores.\\n    - 'Category': The category to which the product belongs.\\n    - 'Price': The price of the product, generated randomly based on a normal distribution with a mean of 50 and a standard deviation of 10.\\n    \\n    Parameters:\\n    mystrings (list of str): List of product names.\\n    n_products (int): Number of products to generate in the catalog.\\n\\n    Returns:\\n    pd.DataFrame: A pandas DataFrame containing the product catalog information.\\n\\n    Requirements:\\n    - pandas\\n    - numpy\\n    - random.randint\\n    - random.seed\\n\\n    Constants:\\n    - CATEGORIES: A list of categories used to randomly assign a category to each product.\\n\\n    Examples:\\n    >>> task_func(['Mobile Phone', 'T Shirt', 'Coffee Maker', 'Python Book', 'Toy Car'], 2)\\n       Product Name        Category  Price\\n    0   Python_Book           Books  67.64\\n    1  Mobile_Phone  Home & Kitchen  54.00\\n    >>> task_func(['Laptop', 'Sweater'], 1)\\n      Product Name Category  Price\\n    0      Sweater    Books  67.64\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Create a product catalog DataFrame where each row represents a product with the following columns: - 'Product Name': The name of the product with spaces replaced by underscores. - 'Category': The category to which the product belongs. - 'Price': The price of the product, generated randomly based on a normal distribution with a mean of 50 and a standard deviation of 10. Constants: - CATEGORIES: A list of categories used to randomly assign a category to each product.\\nThe function should output with:\\n    pd.DataFrame: A pandas DataFrame containing the product catalog information.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport numpy as np\\nimport random\\nfrom random import randint, seed\\n# Constants\\nCATEGORIES = ['Electronics', 'Clothing', 'Home & Kitchen', 'Books', 'Toys & Games']\\ndef task_func(mystrings, n_products, seed=0):\\n```\",\"libs\":\"['pandas', 'numpy', 'random']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}