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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

Time series forecasting with the functime library

Time series forecasting is a form of predictive analytics for time series data. It is to predict the future values based on historical data using statistical models. functime is a machine learning library for time series forecasting and feature extraction in Polars. It enables you to build time series forecasting models utilizing the Polars speed. functime is the Polars’ version of tsfresh, the popular time series feature extraction library.

In this recipe, we’ll cover how to build a simple time series forecasting model with the functime library, including feature extraction and plotting.

Important

As of the time of writing, Polars just upgraded to version 1.0.0 and functime has some compatibility issues with it. You may encounter an error after step 2; however, you can run those later steps with Polars version 0.20.31 (you’ll also need to change lf.collect_schema().names() to lf.columns for the code in...

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