The PowerTransformer() applies power or exponential transformations to numerical variables.

Let’s load the house prices dataset and separate it into train and test sets (more details about the dataset here).

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split

from feature_engine import transformation as vt

# Load dataset
data = data = pd.read_csv('houseprice.csv')

# Separate into train and test sets
X_train, X_test, y_train, y_test =  train_test_split(
            data.drop(['Id', 'SalePrice'], axis=1),
            data['SalePrice'], test_size=0.3, random_state=0)

Now we want to apply the square root to 2 variables in the dataframe:

# set up the variable transformer
tf = vt.PowerTransformer(variables = ['LotArea', 'GrLivArea'], exp=0.5)

# fit the transformer

The transformer does not learn any parameters. So we can go ahead and transform the variables:

# transform the data
train_t= tf.transform(X_train)
test_t= tf.transform(X_test)

Finally, we can plot the original variable distribution:

# un-transformed variable

And now the distribution after the transformation:

# transformed variable

More details#

You can find more details about the PowerTransformer() here:

For more details about this and other feature engineering methods check out these resources: