Python Feature Engineering Cookbook: Over 70 recipes for creating, engineering, and transforming features to build machine learning models

by Soledad Galli

Create end-to-end, reproducible feature engineering pipelines that can be deployed into production using open-source Python libraries 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. This book is for machine learning and data science students and professionals, as well as software engineers working on machine learning model deployment, who want to learn more about how to transform their data and create new features to train machine learning models in a better way.

386 pages, Paperback

Published October 31, 2022

Reviews

[Andrey Lukyanenko · Rating 4 out of 5]

Overall, this book presents a straightforward, hands-on approach to feature engineering in Python. It serves as a convenient reference for anyone who wants quick code examples tailored to specific tasks—especially beginners looking to build up their skills step by step.

The book is quite comprehensive, covering common feature engineering techniques such as handling missing data, encoding categorical variables, and scaling numerical features. The abundance of code snippets is a major plus; you can easily copy, adapt, and experiment with them in your own projects. I found the examples clear and practical, making it a solid resource for day-to-day data preprocessing work.

One small downside is that the reading experience sometimes feels akin to scrolling through a Jupyter notebook with added commentary. For some readers—particularly those who prefer a more narrative or conceptual flow—this might come across as slightly disjointed. However, if you enjoy following code-based demonstrations step by step, you may actually find this style convenient and engaging.

In summary, Python Feature Engineering Cookbook is a good pick for people new to data preprocessing or anyone wanting a quick “go-to” guide for different feature engineering tasks.

[Wallis Chan · Rating 5 out of 5]

Excellent. All the recipes in this book were well explained and easy to follow. There are tons of recipes, with many of them showing how to do certain things using 3 different libraries. It's a great reference book that I'll sure to return to very often.

[justin · Rating 4 out of 5]

Decent, probably geared more towards beginners and note that it uses feature engine, the authors own open source package (seems well maintained, so not a knock, but still worth pointing out) so the page count is a bit inflated since it repeats steps for multiple things.