{"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":"378c6a04-496a-533c-80b4-9df7ea10fd82","task_key":"default--v0~2e1~2e0~5fhf--378c6a04-496a-533c-80b4-9df7ea10fd82","task_revision_id":"2","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 378c6a04-496a-533c-80b4-9df7ea10fd82","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import pandas as pd\\nfrom sklearn.cluster import KMeans\\nimport matplotlib.pyplot as plt\\ndef task_func(df, n_clusters=3, random_state=0):\\n\",\"complete_prompt\":\"import pandas as pd\\nfrom sklearn.cluster import KMeans\\nimport matplotlib.pyplot as plt\\n\\ndef task_func(df, n_clusters=3, random_state=0):\\n    \\\"\\\"\\\"\\n    Convert the 'date' column of a DataFrame to ordinal, perform KMeans clustering on 'date' and 'value' columns, and plot the clusters.\\n\\n    Parameters:\\n        df (pandas.DataFrame): The DataFrame with columns 'group', 'date', and 'value'.\\n        n_clusters (int): The number of clusters for KMeans. Defaults to 3.\\n        random_state (int): Random state for KMeans to ensure reproducibility. Defaults to 0.\\n\\n\\n    Returns:\\n        matplotlib.axes.Axes: The Axes object containing the scatter plot of the clusters.\\n\\n    Required names:\\n        x: 'Date (ordinal)'\\n        ylabel: 'Value'\\n        title: 'KMeans Clustering of Value vs Date'\\n    \\n    Raises:\\n        ValueError: If the DataFrame is empty or lacks required columns.\\n\\n    Requirements:\\n        - pandas\\n        - sklearn.cluster\\n        - matplotlib.pyplot\\n\\n    Example:\\n        >>> df = pd.DataFrame({\\n        ...     \\\"group\\\": [\\\"A\\\", \\\"A\\\", \\\"A\\\", \\\"B\\\", \\\"B\\\"],\\n        ...     \\\"date\\\": pd.to_datetime([\\\"2022-01-02\\\", \\\"2022-01-13\\\", \\\"2022-02-01\\\", \\\"2022-02-23\\\", \\\"2022-03-05\\\"]),\\n        ...     \\\"value\\\": [10, 20, 16, 31, 56],\\n        ... })\\n        >>> ax = task_func(df)\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Convert the 'date' column of a DataFrame to ordinal, perform KMeans clustering on 'date' and 'value' columns, and plot the clusters. Required names: x: 'Date (ordinal)' ylabel: 'Value' title: 'KMeans Clustering of Value vs Date'\\nThe function should raise the exception for: ValueError: If the DataFrame is empty or lacks required columns.\\nThe function should output with:\\n    matplotlib.axes.Axes: The Axes object containing the scatter plot of the clusters.\\nYou should write self-contained code starting with:\\n```\\nimport pandas as pd\\nfrom sklearn.cluster import KMeans\\nimport matplotlib.pyplot as plt\\ndef task_func(df, n_clusters=3, random_state=0):\\n```\",\"libs\":\"['pandas', 'matplotlib', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"noindex","subproblems":[],"grids":[]}