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BigCodeBench / BigCodeBench v0.1.0_hf 40f607bf-a565-5b38-9dbc-d086ed9f1464

Problem

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

complete prompt

import heapq
from sklearn.preprocessing import StandardScaler

def task_func(df, col1, col2, N=10):
    """
    Standardize two columns ('col1' and 'col2') in the DataFrame, find the biggest differences between the individual 
    elements of the standardized columns, and return the indices of the N largest differences.
    
    Parameters:
    df (pandas.DataFrame): A DataFrame with at least two numerical columns.
    col1, col2 (str): Names of the columns to compare.
    N (int, optional): Number of indices to return. Default is 10.
    
    Returns:
    list[int]: The indices of the N largest differences.
    
    Raises:
    ValueError: If specified columns are not in the provided DataFrame.

    Requirements:
    - heapq
    - sklearn.preprocessing
    
    Example:
    >>> df = pd.DataFrame({
    ...     'col1': [99, 86, 90, 70, 86, 95, 56, 98, 80, 81, 1, 2],
    ...     'col2': [21, 11, 21, 1, 26, 40, 4, 50, 34, 37, 3, 4]
    ... })
    >>> indices = task_func(df, 'col1', 'col2', N=6)
    >>> print(indices)     
    [3, 1, 11, 10, 7, 0]

    >>> df = pd.DataFrame({
    ...     'a': [1, 2, 3, 4],
    ...     'b': [1, 2, 3, 5]
    ... })
    >>> indices = task_func(df, 'a', 'b')
    >>> print(indices)   
    [2, 3, 0, 1]
    """

instruct prompt

Standardize two columns ('col1' and 'col2') in the DataFrame, find the biggest differences between the individual elements of the standardized columns, and return the indices of the N largest differences. >>> df = pd.DataFrame({ ...     'a': [1, 2, 3, 4], ...     'b': [1, 2, 3, 5] ... }) >>> indices = task_func(df, 'a', 'b') >>> print(indices) [2, 3, 0, 1]
The function should raise the exception for: ValueError: If specified columns are not in the provided DataFrame.
The function should output with:
    list[int]: The indices of the N largest differences.
You should write self-contained code starting with:

Code

import heapq
from sklearn.preprocessing import StandardScaler
def task_func(df, col1, col2, N=10):

code prompt

Code

import heapq
from sklearn.preprocessing import StandardScaler
def task_func(df, col1, col2, N=10):

entry point

task_func

libs

  • sklearn
  • heapq

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Source and history

task_id
40f607bf-a565-5b38-9dbc-d086ed9f1464
task_key
default--v0~2e1~2e0~5fhf--40f607bf-a565-5b38-9dbc-d086ed9f1464
task_revision_id
1
config / split
default / v0.1.0_hf

Official source

initial import