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Python Feature Engineering Cookbook

You're reading from   Python Feature Engineering Cookbook A complete guide to crafting powerful features for your machine learning models

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781835883587
Length 396 pages
Edition 3rd Edition
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Author (1):
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Soledad Galli Soledad Galli
Author Profile Icon Soledad Galli
Soledad Galli
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Toc

Table of Contents (14) Chapters Close

Preface 1. Chapter 1: Imputing Missing Data FREE CHAPTER 2. Chapter 2: Encoding Categorical Variables 3. Chapter 3: Transforming Numerical Variables 4. Chapter 4: Performing Variable Discretization 5. Chapter 5: Working with Outliers 6. Chapter 6: Extracting Features from Date and Time Variables 7. Chapter 7: Performing Feature Scaling 8. Chapter 8: Creating New Features 9. Chapter 9: Extracting Features from Relational Data with Featuretools 10. Chapter 10: Creating Features from a Time Series with tsfresh 11. Chapter 11: Extracting Features from Text Variables 12. Index 13. Other Books You May Enjoy

Capturing the elapsed time between datetime variables

We can extract powerful features from each datetime variable individually, as we did in the previous two recipes. We can create additional features by combining multiple datetime variables. A common example consists of extracting the age at the time of an event by comparing the date of birth with the date of the event.

In this recipe, we will learn how to capture the time between two datetime variables by utilizing pandas and feature-engine.

How to do it...

To proceed with this recipe, we’ll create a DataFrame containing two datatime variables:

  1. Let’s begin by importing pandas, numpy, and datetime:
    import datetime
    import numpy as np
    import pandas as pd
  2. We’ll start by creating two datetime variables with 20 values each; the values start from 2024-05-17 and increase in intervals of 1 hour for the first variable, and 1 month for the second. Then, we‘ll capture the variables in a DataFrame...
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