Book Image

Python Feature Engineering Cookbook - Second Edition

By : Soledad Galli
Book Image

Python Feature Engineering Cookbook - Second Edition

By: Soledad Galli

Overview of this book

Feature engineering, the process of transforming variables and creating features, albeit time-consuming, ensures that your machine learning models perform seamlessly. This second edition of Python Feature Engineering Cookbook will take the struggle out of feature engineering by showing you how to use open source Python libraries to accelerate the process via a plethora of practical, hands-on recipes. This updated edition begins by addressing fundamental data challenges such as missing data and categorical values, before moving on to strategies for dealing with skewed distributions and outliers. The concluding chapters show you how to develop new features from various types of data, including text, time series, and relational databases. With the help of numerous open source Python libraries, you'll learn how to implement each feature engineering method in a performant, reproducible, and elegant manner. By the end of this Python book, you will have the tools and expertise needed to confidently build end-to-end and reproducible feature engineering pipelines that can be deployed into production.
Table of Contents (14 chapters)

Extracting features from date and time

In Chapter 6, Extracting Features from Date and Time, we created various features from the date and time parts of datetime variables using pandas and Feature-engine. We can also extract multiple features from datetime variables, automatically utilizing Featuretools.

Featuretools supports the creation of various features from datetime variables using its datetime transform primitives. These primitives include the creation of features such as year, month, and day, and also other features such as is it lunch time or is it weekday?


For more details on the features that can be created using datetime variables, visit

In this recipe, we will automatically create multiple features from a datetime variable with Featuretools.

How to do it...

Let’s begin by importing the libraries and getting the dataset ready:

  1. First, we’...