Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Polars Cookbook

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

Arrow left icon
Product type Paperback
Published in Aug 2024
Publisher Packt
ISBN-13 9781805121152
Length 394 pages
Edition 1st Edition
Languages
Arrow right icon
Author (1):
Arrow left icon
Yuki Kakegawa Yuki Kakegawa
Author Profile Icon Yuki Kakegawa
Yuki Kakegawa
Arrow right icon
View More author details
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

Reshaping and Tidying Data

When analyzing and transforming data, it’s not always the case that the data is in the best shape possible for your purpose. Your data may not be as clean or organized as you hoped. It may not even have the necessary column attributes for your analysis. This is where the concept of reshaping and tidying data comes into play. Reshaping means transforming data so that it suits a particular analysis you’re trying to conduct. Tidying data refers to the process of organizing and structuring data in a clean and consistent way so that it is easy to work with and analyze. The process of reshaping and tidying data involves operations such as pivoting, unpivoting, stacking, and joining.

In this chapter, we’re going to cover the following main topics:

  • Turning columns into rows
  • Turning rows into columns
  • Joining DataFrames
  • Concatenating DataFrames
  • Other techniques for reshaping data
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image