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Python Feature Engineering Cookbook

You're reading from   Python Feature Engineering Cookbook Over 70 recipes for creating, engineering, and transforming features to build machine learning models

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Product type Paperback
Published in Jan 2020
Publisher Packt
ISBN-13 9781789806311
Length 372 pages
Edition 1st 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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Table of Contents (13) Chapters Close

Preface 1. Foreseeing Variable Problems When Building ML Models 2. Imputing Missing Data FREE CHAPTER 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

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...

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