Quick Start#

If you’re new to Feature-engine this guide will get you started. Feature-engine transformers have the methods fit() and transform() to learn parameters from the data and then modify the data. They work just like any Scikit-learn transformer.


Feature-engine is a Python 3 package and works well with 3.7 or later. Earlier versions are not compatible with the latest versions of Python numerical computing libraries.

$ pip install feature-engine

Note, you can also install it with a _ as follows:

$ pip install feature_engine

Note that Feature-engine is an active project and routinely publishes new releases. In order to upgrade Feature-engine to the latest version, use pip as follows.

$ pip install -U feature-engine

If you’re using Anaconda, you can install the Anaconda Feature-engine package:

$ conda install -c conda-forge feature_engine

Once installed, you should be able to import Feature-engine without an error, both in Python and in Jupyter notebooks.

Example Use#

This is an example of how to use Feature-engine’s transformers to perform missing data imputation.

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

from feature_engine.imputation import MeanMedianImputer

# Load dataset
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),

# set up the imputer
median_imputer = MeanMedianImputer(
    imputation_method='median', variables=['LotFrontage', 'MasVnrArea']

# fit the imputer

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

fig = plt.figure()
ax = fig.add_subplot(111)
X_train['LotFrontage'].plot(kind='kde', ax=ax)
train_t['LotFrontage'].plot(kind='kde', ax=ax, color='red')
lines, labels = ax.get_legend_handles_labels()
ax.legend(lines, labels, loc='best')

Feature-engine with the Scikit-learn’s pipeline#

Feature-engine’s transformers can be assembled within a Scikit-learn pipeline. This way, we can store our entire feature engineering pipeline in one single object or pickle (.pkl). Here is an example of how to do it:

from math import sqrt
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

from sklearn.linear_model import Lasso
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.pipeline import Pipeline as pipe
from sklearn.preprocessing import MinMaxScaler

from feature_engine.encoding import RareLabelEncoder, MeanEncoder
from feature_engine.discretisation import DecisionTreeDiscretiser
from feature_engine.imputation import (

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

# drop some variables
    labels=['YearBuilt', 'YearRemodAdd', 'GarageYrBlt', 'Id'],

# make a list of categorical variables
categorical = [var for var in data.columns if data[var].dtype == 'O']

# make a list of numerical variables
numerical = [var for var in data.columns if data[var].dtype != 'O']

# make a list of discrete variables
discrete = [ var for var in numerical if len(data[var].unique()) < 20]

# categorical encoders work only with object type variables
# to treat numerical variables as categorical, we need to re-cast them
data[discrete]= data[discrete].astype('O')

# continuous variables
numerical = [
    var for var in numerical if var not in discrete
    and var not in ['Id', 'SalePrice']

# separate into train and test sets
X_train, X_test, y_train, y_test = train_test_split(
                                        data.drop(labels=['SalePrice'], axis=1),

# set up the pipeline
price_pipe = pipe([
    # add a binary variable to indicate missing information for the 2 variables below
    ('continuous_var_imputer', AddMissingIndicator(variables=['LotFrontage'])),

    # replace NA by the median in the 2 variables below, they are numerical
    ('continuous_var_median_imputer', MeanMedianImputer(
        imputation_method='median', variables=['LotFrontage', 'MasVnrArea']

    # replace NA by adding the label "Missing" in categorical variables
    ('categorical_imputer', CategoricalImputer(variables=categorical)),

    # disretise continuous variables using trees
    ('numerical_tree_discretiser', DecisionTreeDiscretiser(

    # remove rare labels in categorical and discrete variables
    ('rare_label_encoder', RareLabelEncoder(
        tol=0.03, n_categories=1, variables=categorical+discrete

    # encode categorical and discrete variables using the target mean
    ('categorical_encoder', MeanEncoder(variables=categorical+discrete)),

    # scale features
    ('scaler', MinMaxScaler()),

    # Lasso
    ('lasso', Lasso(random_state=2909, alpha=0.005))


# train feature engineering transformers and Lasso
price_pipe.fit(X_train, np.log(y_train))

# predict
pred_train = price_pipe.predict(X_train)
pred_test = price_pipe.predict(X_test)

# Evaluate
print('Lasso Linear Model train mse: {}'.format(
    mean_squared_error(y_train, np.exp(pred_train))))
print('Lasso Linear Model train rmse: {}'.format(
    sqrt(mean_squared_error(y_train, np.exp(pred_train)))))
print('Lasso Linear Model test mse: {}'.format(
    mean_squared_error(y_test, np.exp(pred_test))))
print('Lasso Linear Model test rmse: {}'.format(
    sqrt(mean_squared_error(y_test, np.exp(pred_test)))))
Lasso Linear Model train mse: 949189263.8948538
Lasso Linear Model train rmse: 30808.9153313591

Lasso Linear Model test mse: 1344649485.0641894
Lasso Linear Model train rmse: 36669.46256852136
plt.scatter(y_test, np.exp(pred_test))
plt.xlabel('True Price')
plt.ylabel('Predicted Price')

More examples#

More examples can be found in: