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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 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

Counting characters, words, and vocabulary

One of the salient characteristics of text is its complexity. Long descriptions are more likely to contain more information than short descriptions. Texts rich in different, unique words are more likely to be richer in detail than texts that repeat the same words over and over. In the same way, when we speak, we use many short words such as articles and prepositions to build the sentence structure, yet the main concept is often derived from the nouns and adjectives we use, which tend to be longer words. So, as you can see, even without reading the text, we can start inferring how much information the text provides by determining the number of words, the number of unique words (non-repeated occurrences of a word), the lexical diversity, and the length of those words. In this recipe, we will learn how to extract these features from a text variable using pandas.

Getting ready

We are going to use the 20 Newsgroup dataset that comes with...

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