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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

Extracting substrings

Extracting substrings is a crucial component in string manipulation. It means deriving a portion of a string and using it as another column or as part of a transformation logic. Knowing how to extract substrings helps you clean, transform, and organize your data into a more useful format.

In this recipe, we’ll cover how to extract substrings by slicing and regex.

How to do it...

Here’s how you extract substrings from strings in Polars:

  1. Use .str.slice() to extract a substring. There are two available parameters in this method: offset and length. The following example only specifies the offset:
     df.select(
        'userName',
        pl.col('userName').str.slice(3).alias('4thCharAndAfter')
    ).head()

    The preceding code will return the following output:

Figure 6.11 – A new column with userName after the 4t character

Figure 6.11 – A new column with userName after the 4t character

  1. You can specify...
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