{"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":"efe12992-d8e4-5fb5-bc5c-02480e60bc35","task_key":"default--v0~2e1~2e0~5fhf--efe12992-d8e4-5fb5-bc5c-02480e60bc35","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf efe12992-d8e4-5fb5-bc5c-02480e60bc35","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nimport numpy as np\\nfrom sklearn.decomposition import PCA\\ndef task_func(array: list, random_seed: int = 42) -> (pd.DataFrame, np.ndarray):\\n\",\"complete_prompt\":\"import pandas as pd\\nimport numpy as np\\nfrom sklearn.decomposition import PCA\\n\\n\\ndef task_func(array: list, random_seed: int = 42) -> (pd.DataFrame, np.ndarray):\\n    \\\"\\\"\\\"\\n    Converts a 2D list into a pandas DataFrame and applies PCA for dimensionality reduction.\\n\\n    This function creates a DataFrame from the provided 2D list and then applies PCA to reduce the dataset\\n    to its two main components. The function uses a fixed random seed to ensure reproducibility.\\n\\n    Parameters:\\n    - array (list of list of int): A 2D list representing data rows and columns.\\n    - random_seed (int, optional): The seed for the random number generator. Default is 42.\\n\\n    Returns:\\n    - pd.DataFrame: The original data in DataFrame format.\\n    - np.ndarray: The data after PCA transformation.\\n\\n    Requirements:\\n    - pandas\\n    - numpy\\n    - sklearn.decomposition.PCA\\n\\n    Examples:\\n    >>> data = [[1,2,3,4,5], [6,7,8,9,10], [11,12,13,14,15]]\\n    >>> df, transformed = task_func(data)\\n    >>> print(df)\\n        0   1   2   3   4\\n    0   1   2   3   4   5\\n    1   6   7   8   9  10\\n    2  11  12  13  14  15\\n    >>> print(transformed[:, 0])\\n    [ 11.18033989  -0.         -11.18033989]\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Converts a 2D list into a pandas DataFrame and applies PCA for dimensionality reduction. This function creates a DataFrame from the provided 2D list and then applies PCA to reduce the dataset to its two main components. The function uses a fixed random seed to ensure reproducibility.\\nThe function should output with:\\n    pd.DataFrame: The original data in DataFrame format.\\n    np.ndarray: The data after PCA transformation.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nimport numpy as np\\nfrom sklearn.decomposition import PCA\\ndef task_func(array: list, random_seed: int = 42) -> (pd.DataFrame, np.ndarray):\\n```\",\"libs\":\"['pandas', 'numpy', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}