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Polars Cookbook

Polars Cookbook

By : Yuki Kakegawa
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Polars Cookbook

Polars Cookbook

5 (5)
By: Yuki Kakegawa

Overview of this book

The Polars Cookbook is a comprehensive, hands-on guide to Python Polars, one of the first resources dedicated to this powerful data processing library. Written by Yuki Kakegawa, a seasoned data analytics consultant who has worked with industry leaders like Microsoft and Stanford Health Care, this book offers targeted, real-world solutions to data processing, manipulation, and analysis challenges. The book also includes a foreword by Marco Gorelli, a core contributor to Polars, ensuring expert insights into Polars' applications. From installation to advanced data operations, you’ll be guided through data manipulation, advanced querying, and performance optimization techniques. You’ll learn to work with large datasets, conduct sophisticated transformations, leverage powerful features like chaining, and understand its caveats. This book also shows you how to integrate Polars with other Python libraries such as pandas, numpy, and PyArrow, and explore deployment strategies for both on-premises and cloud environments like AWS, BigQuery, GCS, Snowflake, and S3. With use cases spanning data engineering, time series analysis, statistical analysis, and machine learning, Polars Cookbook provides essential techniques for optimizing and securing your workflows. By the end of this book, you'll possess the skills to design scalable, efficient, and reliable data processing solutions with Polars.
Table of Contents (15 chapters)
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Getting Started with Python Polars

This chapter will look at the fundamentals of Python Polars. We will learn some of the key features of Polars at a high level in order to understand why Polars is fast and efficient for processing data. We will also cover how to apply basic operations on DataFrame, Series, and LazyFrame utilizing Polars expressions. These are all essential bits of knowledge and techniques to start utilizing Polars in your data workflows.

This chapter contains the following recipes:

  • Introducing key features in Polars
  • The Polars DataFrame
  • Polars Series
  • The Polars LazyFrame
  • Selecting columns and filtering data
  • Creating, modifying, and deleting columns
  • Understanding method chaining
  • Processing larger-than-RAM datasets

After going through all of these, you’ll have a good understanding of what makes Polars unique, as well as how to apply essential data operations in Polars.

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Polars Cookbook
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