{"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":"74545691-5418-58b3-afea-8cb14bb75255","task_key":"default--v0~2e1~2e0~5fhf--74545691-5418-58b3-afea-8cb14bb75255","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 74545691-5418-58b3-afea-8cb14bb75255","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport numpy as np\\nfrom random import choice\\n# Constants\\nDATA_TYPES = [str, int, float, list, tuple, dict, set]\\ndef task_func(rows, columns):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport numpy as np\\nfrom random import choice\\n\\n# Constants\\nDATA_TYPES = [str, int, float, list, tuple, dict, set]\\n\\n\\ndef task_func(rows, columns):\\n    \\\"\\\"\\\"\\n    Generates a DataFrame with a specified number of rows and columns, populated with randomly generated data.\\n    Each column's data type is randomly selected from a set of Python data types,\\n    including primitive and complex structures.\\n\\n    Parameters:\\n    rows (int): Number of rows in the generated DataFrame.\\n    columns (int): Number of columns in the generated DataFrame. Each column is assigned a random data type.\\n\\n    DataFrame: A DataFrame in which each column's data type could be one of the following,\\n    with random content generated accordingly:\\n    - str: Random strings of 5 lowercase alphabetic characters.\\n    - int: Random integers from 0 to 9.\\n    - float: Random floats derived by converting integers from 0 to 9 into float.\\n    - list: Lists of random length (1 to 5) containing integers from 0 to 9.\\n    - tuple: Tuples of random length (1 to 5) containing integers from 0 to 9.\\n    - dict: Dictionaries with a random number (1 to 5) of key-value pairs, keys and values are integers from 0 to 9.\\n    - set: Sets of random size (1 to 5) containing unique integers from 0 to 9.\\n\\n    Returns:\\n    pd.DataFrame: A DataFrame with the specified number of rows and columns named 'col0', 'col1', etc., containing randomly generated data.\\n\\n    Requirements:\\n    - pandas\\n    - numpy\\n    - random\\n\\n    Example:\\n    >>> df = task_func(2, 3)\\n    >>> print(df.shape)\\n    (2, 3)\\n    >>> isinstance(df, pd.DataFrame)\\n    True\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Generates a DataFrame with a specified number of rows and columns, populated with randomly generated data. Each column's data type is randomly selected from a set of Python data types, including primitive and complex structures. DataFrame: A DataFrame in which each column's data type could be one of the following, with random content generated accordingly: - str: Random strings of 5 lowercase alphabetic characters. - int: Random integers from 0 to 9. - float: Random floats derived by converting integers from 0 to 9 into float. - list: Lists of random length (1 to 5) containing integers from 0 to 9. - tuple: Tuples of random length (1 to 5) containing integers from 0 to 9. - dict: Dictionaries with a random number (1 to 5) of key-value pairs, keys and values are integers from 0 to 9. - set: Sets of random size (1 to 5) containing unique integers from 0 to 9.\\nThe function should output with:\\n    pd.DataFrame: A DataFrame with the specified number of rows and columns named 'col0', 'col1', etc., containing randomly generated data.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport numpy as np\\nfrom random import choice\\n# Constants\\nDATA_TYPES = [str, int, float, list, tuple, dict, set]\\ndef task_func(rows, columns):\\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":"noindex","subproblems":[],"grids":[]}