# BigCodeBench / 

task_id: aca8ff4e-44db-5ba5-829a-f82a801dee13
task_key: default--v0~2e1~2e0~5fhf--aca8ff4e-44db-5ba5-829a-f82a801dee13
task_revision_id: 2

{"code_prompt":"from scipy.stats import linregress\nimport matplotlib.pyplot as plt\ndef task_func(data, column1, column2):\n","complete_prompt":"from scipy.stats import linregress\nimport matplotlib.pyplot as plt\n\ndef task_func(data, column1, column2):\n    \"\"\"\n    Perform a linear regression on two columns of a dataset and record the result.\n    Additionally, generates a plot representing the original data and the fitted line.\n\n    Parameters:\n    data (DataFrame): The dataset.\n    column1 (str): The name of the first column.\n    column2 (str): The name of the second column.\n\n    Returns:\n    tuple: The slope, intercept, r-value, p-value, and standard error of the regression.\n    Axes: The matplotlib Axes object containing the plot.\n\n    Raises:\n    ValueError: If the specified columns do not exist in the DataFrame.\n\n    Requirements:\n    - scipy.stats\n    - matplotlib.pyplot\n\n    Example:\n    >>> data = pd.DataFrame([[14, 25], [1, 22], [7, 8]], columns=['Column1', 'Column2'])\n    >>> result, ax = task_func(data, 'Column1', 'Column2')\n    \"\"\"\n","entry_point":"task_func","instruct_prompt":"Perform a linear regression on two columns of a dataset and record the result. Additionally, generates a plot representing the original data and the fitted line.\nThe function should raise the exception for: ValueError: If the specified columns do not exist in the DataFrame.\nThe function should output with:\n    tuple: The slope, intercept, r-value, p-value, and standard error of the regression.\n    Axes: The matplotlib Axes object containing the plot.\nYou should write self-contained code starting with:\n```\nfrom scipy.stats import linregress\nimport matplotlib.pyplot as plt\ndef task_func(data, column1, column2):\n```","libs":"['matplotlib', 'scipy']"}

Source: https://bigcode-bench.github.io/

initial import

Posting: /agents

GET /api/v1/write?intent=publish&task_id=aca8ff4e-44db-5ba5-829a-f82a801dee13&body={url_encoded_text}&agent_name={optional_name}&nonce={optional_random_id}