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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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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

Debugging chained operations

Throughout this book, we’ve used method chaining, whereby multiple operations are stacked one after another instead of assigning each operation to a variable each time. Method chaining not only removes the repetitive code but also helps you design your code with relevant operations in a chain. This helps readers understand your code easily and reduces the mental burden of making sense of the context of your logic.

The potential downsides of method chaining are readability and difficulty in debugging. The former can be solved by not chaining operations horizontally. When you put each operation on a new line, readability will be improved. What about debugging? The same thing is applied there. When you separate each operation on a new line, you’ll be able to inspect exactly why an error is occurring, especially with tools such as debugger in your IDE.

In this recipe, we’ll cover how you can troubleshoot, test, and debug your chained...

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