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BigCodeBench / BigCodeBench v0.1.0_hf 8d9a6ea2-a611-5ec6-8cc7-a5ef25aa2763

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


def task_func(file_path, output_path=None, sort_key='title', linear_regression=False, x_column=None, y_column=None):
    """
    Sorts a CSV file by a specific column key using pandas, and optionally writes the sorted data to another CSV file.
    Can also fit a linear regression model to specified columns if required.

    Parameters:
    file_path (str): The path to the input CSV file. This parameter is required.
    output_path (str): The path where the sorted CSV will be saved. If not provided, the function won't save the sorted dataframe.
    sort_key (str): The column name used as a key to sort the CSV file. Defaults to 'title'.
    linear_regression (bool): If True, fits a linear regression model to the specified columns. Defaults to False.
    x_column (str): The name of the column to use as the predictor variable for linear regression.
    y_column (str): The name of the column to use as the response variable for linear regression.

    Returns: 
    DataFrame, str, or LinearRegression model: The sorted pandas DataFrame if 'output_path' is None and
    'linear_regression' is False, otherwise the path to the saved output file. If 'linear_regression' is True,
    returns the fitted model.

    Raises:
    Exception: If there is an error in reading, sorting the data, or fitting the model.
    If the specified columns for linear regression do not exist in the dataframe, a ValueError with "Specified columns for linear regression do not exist in the dataframe" message is also raised.

    
    Requirements:
    - pandas
    - scikit-learn

    Example:
    >>> model = task_func('data.csv', sort_key='title', linear_regression=True, x_column='age', y_column='salary')
    >>> # Returns a fitted LinearRegression model based on 'age' and 'salary' columns.

    
    """

instruct prompt

Sorts a CSV file by a specific column key using pandas, and optionally writes the sorted data to another CSV file. Can also fit a linear regression model to specified columns if required.
The function should raise the exception for: Exception: If there is an error in reading, sorting the data, or fitting the model. If the specified columns for linear regression do not exist in the dataframe, a ValueError with "Specified columns for linear regression do not exist in the dataframe" message is also raised.
The function should output with:
    DataFrame, str, or LinearRegression model: The sorted pandas DataFrame if 'output_path' is None and
    'linear_regression' is False, otherwise the path to the saved output file. If 'linear_regression' is True,
    returns the fitted model.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.linear_model import LinearRegression
def task_func(file_path, output_path=None, sort_key='title', linear_regression=False, x_column=None, y_column=None):

code prompt

Code

import pandas as pd
from sklearn.linear_model import LinearRegression
def task_func(file_path, output_path=None, sort_key='title', linear_regression=False, x_column=None, y_column=None):

entry point

task_func

libs

  • pandas
  • sklearn

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Source and history

task_id
8d9a6ea2-a611-5ec6-8cc7-a5ef25aa2763
task_key
default--v0~2e1~2e0~5fhf--8d9a6ea2-a611-5ec6-8cc7-a5ef25aa2763
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import