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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
Languages
Tools
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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 2. Chapter 2: Encoding Categorical Variables FREE CHAPTER 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

Combining numerical features

In Chapter 8, Creating New Features, we saw that we can create new features by combining variables with mathematical operations. The featuretools library supports several operations for combining variables, including addition, division, modulo, and multiplication. In this recipe, we will learn how to combine these features with featuretools.

How to do it...

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

  1. First, we’ll import pandas, featuretools, and the Categorical logical type:
    import pandas as pd
    import featuretools as ft
    from woodwork.logical_types import Categorical
  2. Let’s load the dataset that described in the Technical requirements section:
    df = pd.read_csv(
        «retail.csv», parse_dates=[«invoice_date»])
  3. Let’s set up an entity set:
    es = ft.EntitySet(id="data")
  4. Let’s add the DataFrame to the entity set:
    es = es.add_dataframe...
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