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BigCodeBench / BigCodeBench v0.1.0_hf 81442ff4-7d5c-5ffa-abc9-ca1007eadfae

Problem

Answer published by the source. Consult the official source to check your work against its answer.

complete prompt

import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt


def task_func(df1, df2, features=["feature1", "feature2", "feature3"], target="target"):
    """
    Perform linear regression analysis with specified characteristics and targets.
    The function should merge two dataframes based on the 'id' column, perform
    linear regression using columns specified in features to predict the target,
    and plot the residuals.

    Parameters:
    - df1 (DataFrame): The first dataframe containing columns 'id' and the features specified.
    - df2 (DataFrame): The second dataframe containing columns 'id' and target.
    - features (list of str, optional): List of feature column names. Default is ['feature1', 'feature2', 'feature3'].
    - target (str, optional): Name of the target column. Default is 'target'.

    Returns:
    dict: A dictionary containing:
        - 'coefficients': Regression coefficients (list).
        - 'intercept': Regression intercept (float).
        - 'residuals_plot': A matplotlib Axes object representing the residuals plot, with the title 'Residuals Plot', x-axis label 'Predicted Values', and y-axis label 'Residuals'.

    Requirements:
    - pandas
    - sklearn.linear_model.LinearRegression
    - matplotlib.pyplot

    Example:
    >>> df1 = pd.DataFrame({'id': [1, 2, 3], 'feature1': [1.2, 3.4, 5.6], 'feature2': [2.3, 4.5, 6.7], 'feature3': [3.4, 5.6, 7.8]})
    >>> df2 = pd.DataFrame({'id': [1, 2, 3], 'target': [4.5, 6.7, 8.9]})
    >>> result = task_func(df1, df2)
    >>> result['coefficients']
    [0.3333333333333334, 0.33333333333333354, 0.3333333333333335]
    >>> type(result['residuals_plot'])
    <class 'matplotlib.axes._axes.Axes'>
    """

instruct prompt

Perform linear regression analysis with specified characteristics and targets. The function should merge two dataframes based on the 'id' column, perform linear regression using columns specified in features to predict the target, and plot the residuals.
The function should output with:
    dict: A dictionary containing:
    'coefficients': Regression coefficients (list).
    'intercept': Regression intercept (float).
    'residuals_plot': A matplotlib Axes object representing the residuals plot, with the title 'Residuals Plot', x-axis label 'Predicted Values', and y-axis label 'Residuals'.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
def task_func(df1, df2, features=["feature1", "feature2", "feature3"], target="target"):

code prompt

Code

import pandas as pd
from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
def task_func(df1, df2, features=["feature1", "feature2", "feature3"], target="target"):

entry point

task_func

libs

  • pandas
  • matplotlib
  • sklearn

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Source and history

task_id
81442ff4-7d5c-5ffa-abc9-ca1007eadfae
task_key
default--v0~2e1~2e0~5fhf--81442ff4-7d5c-5ffa-abc9-ca1007eadfae
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import