{"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":"cf95ec6c-43d1-5172-8995-c90eddf70af1","task_key":"default--v0~2e1~2e0~5fhf--cf95ec6c-43d1-5172-8995-c90eddf70af1","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf cf95ec6c-43d1-5172-8995-c90eddf70af1","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport random\\ndef task_func(num_rows=100, categories=[\\\"a\\\", \\\"b\\\", \\\"c\\\", \\\"d\\\", \\\"e\\\"], random_seed=42):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport random\\n\\n\\ndef task_func(num_rows=100, categories=[\\\"a\\\", \\\"b\\\", \\\"c\\\", \\\"d\\\", \\\"e\\\"], random_seed=42):\\n    \\\"\\\"\\\"\\n    Create a Pandas DataFrame with specified number of rows. Each row contains a randomly\\n    selected category from the provided categories list and a random integer between 1 and 100.\\n\\n    The function also generates a bar chart visualizing the counts of each category in the DataFrame\\n    and returns both the DataFrame and the bar chart.\\n\\n    Parameters:\\n    - num_rows (int): Number of rows in the DataFrame. Default is 100. Must be at least 1.\\n    - categories (list): List of categories to choose from. Default is ['a', 'b', 'c', 'd', 'e'].\\n    - random_seed (int): Seed for random number generation to ensure reproducibility. Default is 42.\\n\\n    Returns:\\n    - pd.DataFrame: A pandas DataFrame with randomly generated category data.\\n    - matplotlib.pyplot.Axes: A bar chart visualizing the category counts, with the title 'Category Counts'.\\n\\n    Raises:\\n    - ValueError: If num_rows is less than 1.\\n    \\n    Requirements:\\n    - pandas\\n    - random\\n\\n    Example:\\n    >>> df, ax = task_func(num_rows=5)\\n    >>> df\\n      Category  Value\\n    0        a     18\\n    1        a     95\\n    2        c     14\\n    3        b     87\\n    4        b     95\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Create a Pandas DataFrame with specified number of rows. Each row contains a randomly selected category from the provided categories list and a random integer between 1 and 100. The function also generates a bar chart visualizing the counts of each category in the DataFrame and returns both the DataFrame and the bar chart.\\nThe function should raise the exception for: ValueError: If num_rows is less than 1.\\nThe function should output with:\\n    pd.DataFrame: A pandas DataFrame with randomly generated category data.\\n    matplotlib.pyplot.Axes: A bar chart visualizing the category counts, with the title 'Category Counts'.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport random\\ndef task_func(num_rows=100, categories=[\\\"a\\\", \\\"b\\\", \\\"c\\\", \\\"d\\\", \\\"e\\\"], random_seed=42):\\n```\",\"libs\":\"['pandas', 'random']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}