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BigCodeBench / BigCodeBench v0.1.0_hf 28b80076-9ea5-53cd-bb2e-581da124d0ae

Problem

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

complete prompt

import pandas as pd
import statsmodels.api as sm


def task_func(df: pd.DataFrame, height: int, weight: int, columns: list) -> sm.regression.linear_model.RegressionResultsWrapper:
    """
    Performs an OLS linear regression on a subset of the provided DataFrame. The subset is created by filtering rows 
    where the value in the second column of 'columns' is greater than 'height' and the value in the third column is 
    less than 'weight'. The first column in 'columns' is used as the dependent variable / target (y), and the rest as independent 
    variables (X) in the regression.

    If df is empty, or if no rows match the conditions None is returned.


    Parameters:
    - df (pd.DataFrame): The DataFrame to analyze.
    - height (int): The threshold to filter rows based on the second column in 'columns'.
    - weight (int): The threshold to filter rows based on the third column in 'columns'.
    - columns (list of str): A list of column names to use, where the first is the dependent variable.

    Returns:
    - sm.regression.linear_model.RegressionResultsWrapper: The result of the OLS regression, or None if no rows meet the criteria or DataFrame is empty.

    Requirements:
    - pandas
    - statsmodels

    Example:
    >>> df = pd.DataFrame({'Age': [30, 40], 'Height': [60, 70], 'Weight': [100, 150]})
    >>> model = task_func(df, 50, 120, ['Age', 'Height', 'Weight'])

    >>> df = pd.DataFrame(np.random.randint(10,98,size=(100, 3)), columns=['Age', 'Height', 'Weight'])
    >>> model = task_func(df, 45, 72, columns=['Age', 'Height', 'Weight'])

    """

instruct prompt

Performs an OLS linear regression on a subset of the provided DataFrame. The subset is created by filtering rows where the value in the second column of 'columns' is greater than 'height' and the value in the third column is less than 'weight'. The first column in 'columns' is used as the dependent variable / target (y), and the rest as independent variables (X) in the regression. If df is empty, or if no rows match the conditions None is returned. >>> df = pd.DataFrame(np.random.randint(10,98,size=(100, 3)), columns=['Age', 'Height', 'Weight']) >>> model = task_func(df, 45, 72, columns=['Age', 'Height', 'Weight'])
The function should output with:
    sm.regression.linear_model.RegressionResultsWrapper: The result of the OLS regression, or None if no rows meet the criteria or DataFrame is empty.
You should write self-contained code starting with:

Code

import pandas as pd
import statsmodels.api as sm
def task_func(df: pd.DataFrame, height: int, weight: int, columns: list) -> sm.regression.linear_model.RegressionResultsWrapper:

code prompt

Code

import pandas as pd
import statsmodels.api as sm
def task_func(df: pd.DataFrame, height: int, weight: int, columns: list) -> sm.regression.linear_model.RegressionResultsWrapper:

entry point

task_func

libs

  • pandas
  • statsmodels

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Source and history

task_id
28b80076-9ea5-53cd-bb2e-581da124d0ae
task_key
default--v0~2e1~2e0~5fhf--28b80076-9ea5-53cd-bb2e-581da124d0ae
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import