{"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":"noindex","profile":{"resources":[],"task_format":"","scoring":"","metric":"","size":"","answer_access":"","license":"","citation":"","maintainer":"","released":"","why_hard":"","related":[]}},"task_id":"d6ae4652-c761-5145-a1d0-fecf7d470573","task_key":"default--v0~2e1~2e0~5fhf--d6ae4652-c761-5145-a1d0-fecf7d470573","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf d6ae4652-c761-5145-a1d0-fecf7d470573","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport pandas as pd\\ndef task_func(country_dict):\\n\",\"complete_prompt\":\"import numpy as np\\nimport pandas as pd\\n\\n\\ndef task_func(country_dict):\\n    \\\"\\\"\\\"\\n    Generates a DataFrame representing the GDP for a predefined set of countries based on their presence in the p\\n    rovided dictionary. The GDP values are simulated with random integers to model economic data.\\n\\n    Parameters:\\n    country_dict (dict): A dictionary mapping individual names to country names. The country names must correspond to\\n    the predefined set of countries: ['USA', 'UK', 'China', 'Japan', 'Australia'].\\n\\n    Returns:\\n    DataFrame: A pandas DataFrame with each country's name from the input as the index and a randomly generated GDP\\n    value as the column. GDP values range between 1,000,000,000 and 100,000,000,000.\\n\\n    Requirements:\\n    - numpy\\n    - pandas\\n\\n    Example:\\n    >>> np.random.seed(0)\\n    >>> country_dict = {'John': 'USA', 'Alice': 'UK', 'Bob': 'China', 'Charlie': 'Japan', 'David': 'Australia'}\\n    >>> df = task_func(country_dict)\\n    >>> df.loc['USA']\\n    GDP    55085855791\\n    Name: USA, dtype: int64\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generates a DataFrame representing the GDP for a predefined set of countries based on their presence in the p rovided dictionary. The GDP values are simulated with random integers to model economic data.\\nThe function should output with:\\n    DataFrame: A pandas DataFrame with each country's name from the input as the index and a randomly generated GDP\\n    value as the column. GDP values range between 1,000,000,000 and 100,000,000,000.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport pandas as pd\\ndef task_func(country_dict):\\n```\",\"libs\":\"['pandas', 'numpy']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}