BigCodeBench / BigCodeBench v0.1.0_hf 47631d8a-1940-5dc9-9023-5a8e511a496f

complete prompt

import pandas as pd
from sklearn.preprocessing import LabelEncoder


def task_func(data):
    """
    Transforms categorical data into a numerical format suitable for machine learning algorithms using sklearn's
    LabelEncoder. This function generates a DataFrame that pairs original categorical values with their numerical
    encodings.

    Parameters:
    data (list): List of categorical data to be encoded.

    Returns:
    DataFrame: A DataFrame with columns 'Category' and 'Encoded', where 'Category' is the original data and 'Encoded'
    is the numerical representation.

    Requirements:
    - pandas
    - sklearn

    Example:
    >>> df = task_func(['A', 'B', 'C', 'A', 'D', 'E', 'B', 'C'])
    >>> print(df.to_string(index=False))
    Category  Encoded
           A        0
           B        1
           C        2
           A        0
           D        3
           E        4
           B        1
           C        2
    """

instruct prompt

Transforms categorical data into a numerical format suitable for machine learning algorithms using sklearn's LabelEncoder. This function generates a DataFrame that pairs original categorical values with their numerical encodings.
The function should output with:
    DataFrame: A DataFrame with columns 'Category' and 'Encoded', where 'Category' is the original data and 'Encoded'
    is the numerical representation.
You should write self-contained code starting with:

Code

import pandas as pd
from sklearn.preprocessing import LabelEncoder
def task_func(data):

code prompt

Code

import pandas as pd
from sklearn.preprocessing import LabelEncoder
def task_func(data):

entry point

task_func

libs

• pandas
• sklearn