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Python Feature Engineering Cookbook

You're reading from  Python Feature Engineering Cookbook

Product type Book
Published in Jan 2020
Publisher Packt
ISBN-13 9781789806311
Pages 372 pages
Edition 1st Edition
Languages
Author (1):
Soledad Galli Soledad Galli
Profile icon Soledad Galli
Toc

Table of Contents (13) Chapters close

Preface 1. Foreseeing Variable Problems When Building ML Models 2. Imputing Missing Data 3. Encoding Categorical Variables 4. Transforming Numerical Variables 5. Performing Variable Discretization 6. Working with Outliers 7. Deriving Features from Dates and Time Variables 8. Performing Feature Scaling 9. Applying Mathematical Computations to Features 10. Creating Features with Transactional and Time Series Data 11. Extracting Features from Text Variables 12. 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 by 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, 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.

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