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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

Converting to and from a pandas DataFrame

Many of you have used pandas before, especially in your day-to-day work. Although pandas and Polars are often compared as one-or-the-other tools, you can use these tools to supplement each other. Polars allows you to convert between pandas and Polars DataFrames, which is exactly what we’ll cover in this recipe.

Getting ready

You need pandas and pyarrow installed for this recipe to work. Execute the following code to make sure that you have them installed:

pip install pandas pyarrow

How to do it...

Here’s how to convert to and from pandas DataFrames. We’ll first create a Polars DataFrame and then go through ways to convert back and forth between Polars and pandas:

  1. Create a Polars DataFrame from a Python dictionary:
    df = pl.DataFrame({
        'a': [1,2,3],
        'b': [4,5,6]
    })
    type(df)

    The preceding code will return the following output:

    >> polars...
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