{"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":"1f93d4c2-229f-5de6-9fe0-61e2124d0d35","task_key":"default--v0~2e1~2e0~5fhf--1f93d4c2-229f-5de6-9fe0-61e2124d0d35","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 1f93d4c2-229f-5de6-9fe0-61e2124d0d35","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import numpy as np\\nimport pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(records: np.ndarray, random_seed: int = 0) -> pd.DataFrame:\\n\",\"complete_prompt\":\"import numpy as np\\nimport pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\n\\n\\ndef task_func(records: np.ndarray, random_seed: int = 0) -> pd.DataFrame:\\n    \\\"\\\"\\\"\\n    Randomly shuffle the given array's features, normalize its values, then convert to a DataFrame\\n    with shuffled feature names.\\n\\n    Parameters:\\n    - records (np.ndarray): A 2D numpy array with each row as a record and each column as a feature.\\n    - random_seed (int, optional): Seed for random operations to ensure reproducibility.\\n\\n    Returns:\\n    - pd.DataFrame: A pandas DataFrame containing the preprocessed data, with shuffled feature names.\\n\\n    Raises:\\n    - ValueError: If records is not 2D.\\n\\n    Requirements:\\n    - numpy\\n    - pandas\\n    - sklearn\\n\\n    Notes:\\n    - This function normalizes data by subtracting the mean and scaling to unit variance.\\n    - Feature names are of format f{n}; for example, if the records have 5 features, feature\\n      names will be [\\\"f1\\\", \\\"f2\\\", \\\"f3\\\", \\\"f4\\\", \\\"f5\\\"] shuffled.\\n\\n    Examples:\\n    >>> data = np.array([[1, 2, 3], [4, 5, 6]])\\n    >>> df = task_func(data, random_seed=42)\\n    >>> df.shape\\n    (2, 3)\\n    >>> df.columns\\n    Index(['f2', 'f3', 'f1'], dtype='object')\\n    >>> data = np.array([[-1, -2, -3, -4, -5], [0, 0, 0, 0, 0], [1, 2, 3, 4, 5]])\\n    >>> df = task_func(data, random_seed=24)\\n    >>> df\\n             f3        f1        f4        f5        f2\\n    0 -1.224745 -1.224745 -1.224745 -1.224745 -1.224745\\n    1  0.000000  0.000000  0.000000  0.000000  0.000000\\n    2  1.224745  1.224745  1.224745  1.224745  1.224745\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Randomly shuffle the given array's features, normalize its values, then convert to a DataFrame with shuffled feature names.\\nNote that: Notes: This function normalizes data by subtracting the mean and scaling to unit variance. Feature names are of format f{n}; for example, if the records have 5 features, feature names will be [\\\"f1\\\", \\\"f2\\\", \\\"f3\\\", \\\"f4\\\", \\\"f5\\\"] shuffled.\\nThe function should raise the exception for: ValueError: If records is not 2D.\\nThe function should output with:\\n    pd.DataFrame: A pandas DataFrame containing the preprocessed data, with shuffled feature names.\\nYou should write self-contained code starting with:\\n```\\nimport numpy as np\\nimport pandas as pd\\nfrom sklearn.preprocessing import StandardScaler\\ndef task_func(records: np.ndarray, random_seed: int = 0) -> pd.DataFrame:\\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":[]}