um_kod_zm_kat_2.py

# Import libraries
import numpy as np
import pandas as pd

# Display all columns
pd.set_option('display.max_columns', None)

# Import Houseprice data from GitHub
df = pd.read_csv('https://github.com/jurand71/datasets/raw/master/HouseSalePriceCompetition/houseprice.csv')

# Determine categorical variables in the dataset
categorical_variables = [var for var in df.columns if df[var].dtype == 'O']

# Let's explore the cardinality in variables
categories = {}
for cat_variable in categorical_variables:
    categories[df[cat_variable].name] = list(df[cat_variable].unique())

# Three variables were chosen from categorical variables for OneHotEncoder
usecols = ['HeatingQC','KitchenQual','CentralAir']
df = df[usecols]

# Import OneHotEncoder class
from sklearn.preprocessing import OneHotEncoder
enc = OneHotEncoder(categories='auto',
                    drop='first', # to return k-1, drop=false to return k dummies
                    sparse=False,
                    handle_unknown='error') # helps deal with rare labels)
enc.fit(df.fillna('Missing'))

# Learned categories in dataset
enc.categories_

# Transform encoding for dataset
enc_data = enc.transform(df.fillna('Missing'))

# Convert array to dataframe
pd.DataFrame(enc_data).head()

# Retrieve the feature names
enc.get_feature_names_out()