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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 FREE CHAPTER 2. Chapter 2: Encoding Categorical Variables 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

Working with time in different time zones

Some organizations operate internationally; therefore, the information they collect about events may be recorded alongside the time zone of the area where the event took place. To be able to compare events that occurred across different time zones, we typically have 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 how to reassign a variable to a different time zone using pandas.

How to do it...

To proceed with this recipe, we’ll create a sample DataFrame containing two variables in different time zones:

  1. Let’s import pandas:
    import pandas as pd
  2. Let’s create a DataFrame containing one variable with values in different time zones:
    df = pd.DataFrame()
    df['time1'] = pd.concat([
        pd.Series(
            pd.date_range(
           ...
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