BigCodeBench / BigCodeBench v0.1.0_hf ba3599ce-ac08-5139-b14d-e5bbdeb17aff

complete prompt

import pandas as pd
from sklearn.preprocessing import StandardScaler

def task_func(df, cols):
    """
    Standardize specified numeric columns in a dataframe.

    Parameters:
    df (DataFrame): The dataframe.
    cols (list): The columns to standardize.

    Returns:
    DataFrame: The dataframe with standardized columns.

    Raises:
    ValueError: If 'df' is not a DataFrame, 'cols' is not a list, or columns in 'cols' don't exist in 'df'.

    Requirements:
    - pandas
    - sklearn.preprocessing.StandardScaler

    Example:
    >>> np.random.seed(0)
    >>> df = pd.DataFrame({'A': np.random.normal(0, 1, 1000), 'B': np.random.exponential(1, 1000)})
    >>> df = task_func(df, ['A', 'B'])
    >>> print(df.describe())
                      A             B
    count  1.000000e+03  1.000000e+03
    mean  -1.243450e-17 -1.865175e-16
    std    1.000500e+00  1.000500e+00
    min   -3.040310e+00 -1.024196e+00
    25%   -6.617441e-01 -7.183075e-01
    50%   -1.293911e-02 -2.894497e-01
    75%    6.607755e-01  4.095312e-01
    max    2.841457e+00  5.353738e+00
    """

instruct prompt

Standardize specified numeric columns in a dataframe.
The function should raise the exception for: ValueError: If 'df' is not a DataFrame, 'cols' is not a list, or columns in 'cols' don't exist in 'df'.
The function should output with:
    DataFrame: The dataframe with standardized columns.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.preprocessing import StandardScaler
def task_func(df, cols):

code prompt

Code

import pandas as pd
from sklearn.preprocessing import StandardScaler
def task_func(df, cols):

entry point

task_func

libs

• pandas
• sklearn