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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

Working with time in different time zones

Some organizations operate internationally; therefore, the information they collect about events may be recorded together with the time zone of the area where the event took place. To be able to compare events that occurred across different time zones, we first need to set all of the variables within the same zone. In this recipe, we will learn how to unify the time zones of a datetime variable and then learn how to reassign a variable to a different time zone using pandas. 

How to do it...

To proceed with the recipe, let's import pandas and then create a toy dataframe with two variables, each one containing a date and time in different time zones:

  1. Import pandas:
import...
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