{"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":"5e59eb1e-2886-5454-a06f-c40e60e7d792","task_key":"default--v0~2e1~2e0~5fhf--5e59eb1e-2886-5454-a06f-c40e60e7d792","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 5e59eb1e-2886-5454-a06f-c40e60e7d792","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.model_selection import train_test_split\\ndef task_func(df):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.model_selection import train_test_split\\n\\n\\ndef task_func(df):\\n    \\\"\\\"\\\"\\n    Divide the given DataFrame into a training set and a test set (70%: 30% split), separate the \\\"target\\\" column and return the four resulting DataFrames.\\n\\n    Parameters:\\n    - df (pd.DataFrame): pandas DataFrame that contains a column named 'target'.\\n\\n    Returns:\\n    - tuple: A tuple containing four DataFrames: X_train, X_test, y_train, y_test.\\n\\n    Requirements:\\n    - pandas\\n    - sklearn\\n    \\n    Example:\\n    >>> np.random.seed(42)  # Ensure reproducibility\\n    >>> df = pd.DataFrame(np.random.randint(0, 100, size=(100, 5)), columns=list('ABCDE'))  # Explicitly using np and pd\\n    >>> df['target'] = np.random.randint(0, 2, size=100)  # Adding 'target' column using np\\n    >>> X_train, X_test, y_train, y_test = task_func(df)\\n    >>> print(X_train.shape)  # Expected shape of training data\\n    (70, 5)\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Divide the given DataFrame into a training set and a test set (70%: 30% split), separate the \\\"target\\\" column and return the four resulting DataFrames.\\nThe function should output with:\\n    tuple: A tuple containing four DataFrames: X_train, X_test, y_train, y_test.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.model_selection import train_test_split\\ndef task_func(df):\\n```\",\"libs\":\"['pandas', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}