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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

Creating lists

There are only two occasions when you will have columns of the List data type. One is when Polars recognizes the List data type upon reading data. Another is when you create a list column in your code. Whether it’s splitting a string into a list of strings or combining values from multiple columns, creating lists is the start of your complex analysis involving nested data structures.

In this recipe, we’ll cover how to create lists by splitting strings, grouping by columns, and combining multiple values into lists.

How to do it...

Here’s how to create lists:

  • Create lists from strings using .str.split() to split strings into lists:
    df.select(
        'tags',
        pl.col('tags').str.split('|').alias('tags in list')
    ).head()

    The preceding code will return the following output:

Figure 7.2 – The first five rows in the DataFrame with a list column

Figure 7.2 – The first five rows in the DataFrame...

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