Book Image

Python Feature Engineering Cookbook

By : Soledad Galli
Book Image

Python Feature Engineering Cookbook

By: Soledad Galli

Overview of this book

Feature engineering is invaluable for developing and enriching your machine learning models. In this cookbook, you will work with the best tools to streamline your feature engineering pipelines and techniques and simplify and improve the quality of your code. Using Python libraries such as pandas, scikit-learn, Featuretools, and Feature-engine, you’ll learn how to work with both continuous and discrete datasets and be able to transform features from unstructured datasets. You will develop the skills necessary to select the best features as well as the most suitable extraction techniques. This book will cover Python recipes that will help you automate feature engineering to simplify complex processes. You’ll also get to grips with different feature engineering strategies, such as the box-cox transform, power transform, and log transform across machine learning, reinforcement learning, and natural language processing (NLP) domains. By the end of this book, you’ll have discovered tips and practical solutions to all of your feature engineering problems.
Table of Contents (13 chapters)

Extracting date and time parts from a datetime variable

The datetime variables can take dates, time, or date and time as values. The datetime variables are not used in their raw format to build machine learning algorithms. Instead, we create additional features from them, and, in fact, we can enrich the dataset dramatically by extracting information from the date and time.

The pandas Python library contains a lot of capabilities for working with date and time. But to access this functionality, the variables should be cast in a data type that supports these operations, such as datetime or timedelta. Often, the datetime variables are cast as objects, particularly when the data is loaded from a CSV file. Pandas' dt, which is the accessor object to the datetime properties of a pandas Series, works only with datetime data types; therefore, to extract date...