{"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":"3098b704-c932-5dd5-b112-647ceb22c33e","task_key":"default--v0~2e1~2e0~5fhf--3098b704-c932-5dd5-b112-647ceb22c33e","task_revision_id":"1","upstream_id":"","short_description":"BigCodeBench v0.1.0_hf 3098b704-c932-5dd5-b112-647ceb22c33e","config":"default","split":"v0.1.0_hf","body":"{\"code_prompt\":\"import matplotlib.pyplot as plt\\nfrom sklearn.linear_model import LinearRegression\\ndef task_func(df, x_column, y_column):\\n\",\"complete_prompt\":\"import matplotlib.pyplot as plt\\nfrom sklearn.linear_model import LinearRegression\\n\\n\\ndef task_func(df, x_column, y_column):\\n    \\\"\\\"\\\"\\n    Draws a scatter plot for the specified columns from a pandas DataFrame and fits a linear regression model to the data.\\n\\n    Parameters:\\n    df (DataFrame): The input pandas DataFrame.\\n    x_column (str): The column name for the x-axis. Data contained in column must be numeric.\\n    y_column (str): The column name for the y-axis. Data contained in column must be numeric.\\n\\n    Returns:\\n    matplotlib.axes._axes.Axes: The Axes object containing the scatter plot and the linear regression line.\\n\\n    Requirements:\\n    - matplotlib\\n    - sklearn\\n\\n    Notes:\\n    - After plotting the scatterplot, this function overlays the predicted regression line on top in red on the same Axes.\\n\\n    Example:\\n    >>> import pandas as pd\\n    >>> df = pd.DataFrame({'A': [1, 2, 3], 'B': [2, 3, 4]})\\n    >>> ax = task_func(df, 'A', 'B')\\n    >>> type(ax)\\n    <class 'matplotlib.axes._axes.Axes'>\\n    \\\"\\\"\\\"\\n\",\"entry_point\":\"task_func\",\"instruct_prompt\":\"Draws a scatter plot for the specified columns from a pandas DataFrame and fits a linear regression model to the data.\\nNote that: Notes: After plotting the scatterplot, this function overlays the predicted regression line on top in red on the same Axes.\\nThe function should output with:\\n    matplotlib.axes._axes.Axes: The Axes object containing the scatter plot and the linear regression line.\\nYou should write self-contained code starting with:\\n```\\nimport matplotlib.pyplot as plt\\nfrom sklearn.linear_model import LinearRegression\\ndef task_func(df, x_column, y_column):\\n```\",\"libs\":\"['matplotlib', 'sklearn']\"}","display_format":"code","language":"","answer_status":"published","assets":[],"source_url":"https://bigcode-bench.github.io/","history":"initial import","indexing_mode":"index","subproblems":[],"grids":[]}