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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 NumPy arrays

NumPy can also work with Polars. You can convert back and forth between Polars and NumPy. Also, in cases where Polars lacks a particular function, we have the option to utilize NumPy instead while benefiting from efficient columnar operations via the NumPy API.

In this recipe, we’ll cover how to convert from and to NumPy arrays, as well as how to use NumPy functions directly in Polars.

Getting ready

You need NumPy installed for this recipe:

pip install numpy

How to do it...

Here’s how you can utilize NumPy in Polars. We’ll first create a NumPy array and then convert back and forth between Polars and NumPy.

  1. Create a NumPy array:
     arr = np.array([[1,2,3], [4,5,6]])
  2. Create a Polars DataFrame from the NumPy array:
    df = pl.from_numpy(arr, schema=['a', 'b'], orient='col')
    df

    The preceding code will return the following output:

Figure 10.1 – A Polars DataFrame created from a NumPy array

Figure 10.1 –...

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