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BigCodeBench / BigCodeBench v0.1.0_hf a0566530-7f16-5f63-bd5b-a1ee91a86f92

Problem

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

complete prompt

import pandas as pd
import numpy as np
import statsmodels.api as sm


def task_func(
    array: list, random_seed: int = 0
) -> (pd.DataFrame, sm.regression.linear_model.RegressionResultsWrapper):
    """
    Generate a Pandas DataFrame from a 2D list and perform a multiple linear regression.

    The function first validates the input list, creates a DataFrame, separates independent and dependent variables,
    adds a constant to the model, and fits a linear regression using statsmodels.

    Parameters:
    - array (list of list of int): A 2D list where each sub-list represents a row of data.
                                   Each sub-list should have exactly 5 elements, where the first 4 elements are
                                   treated as independent variables ('A', 'B', 'C', 'D') and the last element is
                                   the dependent (Response) variable.

    - random_seed (int): A seed for reproducibility in numpy for statsmodels. Defaults to 0.

    Returns:
    - df (pd.DataFrame): DataFrame with columns 'A', 'B', 'C', 'D', 'Response'.
    - results (statsmodels.RegressionResults): Results of the linear regression.

    Requirements:
    - pandas
    - numpy
    - statsmodels.api.sm

    Example:
    >>> df, results = task_func([[1,2,3,4,5], [6,7,8,9,10]])
    >>> print(df)
       A  B  C  D  Response
    0  1  2  3  4         5
    1  6  7  8  9        10
    """

instruct prompt

Generate a Pandas DataFrame from a 2D list and perform a multiple linear regression. The function first validates the input list, creates a DataFrame, separates independent and dependent variables, adds a constant to the model, and fits a linear regression using statsmodels. - random_seed (int): A seed for reproducibility in numpy for statsmodels. Defaults to 0.
The function should output with:
    df (pd.DataFrame): DataFrame with columns 'A', 'B', 'C', 'D', 'Response'.
    results (statsmodels.RegressionResults): Results of the linear regression.
You should write self-contained code starting with:

Code

import pandas as pd
import numpy as np
import statsmodels.api as sm
def task_func(
    array: list, random_seed: int = 0
) -> (pd.DataFrame, sm.regression.linear_model.RegressionResultsWrapper):

code prompt

Code

import pandas as pd
import numpy as np
import statsmodels.api as sm
def task_func(
    array: list, random_seed: int = 0
) -> (pd.DataFrame, sm.regression.linear_model.RegressionResultsWrapper):

entry point

task_func

libs

  • pandas
  • numpy
  • statsmodels

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Source and history

task_id
a0566530-7f16-5f63-bd5b-a1ee91a86f92
task_key
default--v0~2e1~2e0~5fhf--a0566530-7f16-5f63-bd5b-a1ee91a86f92
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import