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Polars Cookbook

You're reading from   Polars Cookbook Over 60 practical recipes to transform, manipulate, and analyze your data using Python Polars 1.x

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Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781805121152
Length 394 pages
Edition 1st Edition
Languages
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Author (1):
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Yuki Kakegawa Yuki Kakegawa
Author Profile Icon Yuki Kakegawa
Yuki Kakegawa
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Toc

Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Getting Started with Python Polars FREE CHAPTER 2. Chapter 2: Reading and Writing Files 3. Chapter 3: An Introduction to Data Analysis in Python Polars 4. Chapter 4: Data Transformation Techniques 5. Chapter 5: Handling Missing Data 6. Chapter 6: Performing String Manipulations 7. Chapter 7: Working with Nested Data Structures 8. Chapter 8: Reshaping and Tidying Data 9. Chapter 9: Time Series Analysis 10. Chapter 10: Interoperability with Other Python Libraries 11. Chapter 11: Working with Common Cloud Data Sources 12. Chapter 12: Testing and Debugging in Polars 13. Index 14. Other Books You May Enjoy

Turning columns into rows

Unpivoting is a common operation done when reshaping or tidying your data. It helps turn and compact columns into rows. In other words, the unpivot operation converts your data from a wide format to a long format. This operation is also known as unpivot.

Both wide format and long format have their use cases. Some client tools prefer wide format while some prefer long format.

In this recipe, we’ll cover how to turn columns into rows using the unpivot operation.

How to do it...

Here’s how to turn columns into rows:

  1. Use the .unpivot() method to turn columns into rows:
    long_df = df.unpivot(
        index='academic_year',
            on=[
                'students',
                'us_students',
           ...
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