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BigCodeBench / BigCodeBench v0.1.0_hf e7245d3d-18ec-50ae-b1d0-b91d991c3f25

Problem

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

complete prompt

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler


def task_func(n_rows, scale_cols, columns=['A', 'B', 'C', 'D', 'E'], random_seed=None):
    """
    Generate a DataFrame with columns 'columns' and fill them with random
    values. Scale the columns at the provided indexes with sklearn StandardScaler.
    If scale_cols is empty no column is scaled
        
    Parameters:
    n_rows (int): The number of rows in the DataFrame.
    scale_cols (list of int): The indices of columns to be scaled. The indices are based on the predefined column names.
    columns (list of str, optional): The columns to be included in the DataFrame. Defaults to ['A', 'B', 'C', 'D', 'E'].
    random_seed (int): Seed used in rng. Default is None.

    Returns:
    DataFrame: The resulting DataFrame after scaling the selected columns.

    Requirements:
    - numpy
    - pandas
    - sklearn
    
    Example:
    >>> df = task_func(3, [1], columns=['test', 'scale'], random_seed=1)
    >>> print(df)
       test     scale
    0    37  1.162476
    1    72  0.116248
    2    75 -1.278724

    >>> df = task_func(5, [1, 2, 3], random_seed=12)
    >>> print(df)
        A         B         C         D   E
    0  75 -0.840307 -0.791926 -1.462784   3
    1  67  0.673481  1.517859 -0.855820  49
    2  52 -1.519967 -0.406962  1.177511  34
    3  75  0.611694 -1.121896  0.782984  13
    4  82  1.075099  0.802925  0.358109  35
    """

instruct prompt

Generate a DataFrame with columns 'columns' and fill them with random values. Scale the columns at the provided indexes with sklearn StandardScaler. If scale_cols is empty no column is scaled >>> df = task_func(5, [1, 2, 3], random_seed=12) >>> print(df) A         B         C         D   E 0  75 -0.840307 -0.791926 -1.462784   3 1  67  0.673481  1.517859 -0.855820  49 2  52 -1.519967 -0.406962  1.177511  34 3  75  0.611694 -1.121896  0.782984  13 4  82  1.075099  0.802925  0.358109  35
The function should output with:
    DataFrame: The resulting DataFrame after scaling the selected columns.
You should write self-contained code starting with:

Code

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
def task_func(n_rows, scale_cols, columns=['A', 'B', 'C', 'D', 'E'], random_seed=None):

code prompt

Code

import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
def task_func(n_rows, scale_cols, columns=['A', 'B', 'C', 'D', 'E'], random_seed=None):

entry point

task_func

libs

  • pandas
  • numpy
  • sklearn

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Source and history

task_id
e7245d3d-18ec-50ae-b1d0-b91d991c3f25
task_key
default--v0~2e1~2e0~5fhf--e7245d3d-18ec-50ae-b1d0-b91d991c3f25
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import