{"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":"16b46455-ce0a-5a6e-bf33-da87777619da","task_key":"default--v0~2e1~2e0~5fhf--16b46455-ce0a-5a6e-bf33-da87777619da","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 16b46455-ce0a-5a6e-bf33-da87777619da","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport numpy as np\\ndef task_func(file_path, num_rows, data_dimensions=5, random_seed=None):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport numpy as np\\n\\n\\ndef task_func(file_path, num_rows, data_dimensions=5, random_seed=None):\\n    \\\"\\\"\\\"\\n    Creates a CSV file on a given file path with random numeric data. \\n    The number of rows in the CSV file is determined by the 'num_rows' parameter, \\n    and the number of columns (features) is determined by the 'data_dimensions' parameter.\\n    Columns are named following the convention: 'Feature_x', where x is the number of the \\n    feature column starting at 1.\\n\\n    Parameters:\\n    file_path (str): The file path where the CSV file should be created.\\n    num_rows (int): The number of rows of random data to generate.\\n    data_dimensions (int, optional): The number of columns (features) in the CSV file. Defaults to 5.\\n    random_seed (int, optional): Seed used in rng. Defaults to None.\\n    \\n    Returns:\\n    str: The file path of the generated CSV file.\\n\\n    Requirements:\\n    - pandas\\n    - numpy\\n\\n    Example:\\n    >>> task_func('/tmp/data.csv', 100)\\n    '/tmp/data.csv'\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Creates a CSV file on a given file path with random numeric data. The number of rows in the CSV file is determined by the 'num_rows' parameter, and the number of columns (features) is determined by the 'data_dimensions' parameter. Columns are named following the convention: 'Feature_x', where x is the number of the feature column starting at 1.\\nThe function should output with:\\n    str: The file path of the generated CSV file.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport numpy as np\\ndef task_func(file_path, num_rows, data_dimensions=5, random_seed=None):\\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":[]}