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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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Handling Missing Data

In data analysis, data science, and data engineering, the majority of time is spent doing data manipulations and cleaning. Your data could be very messy in that it contains a lot of missing data that you need to treat with care. To compute whatever you need, you may need to identify missing data and decide what to do with it.

There are two approaches to handling missing data. One is to substitute missing data with alternate values. Another way is to simply drop records that contain missing data. However, your decision to handle missing data should align with your end goal. That helps identify the appropriate approach as well as values with which you may want to replace missing data.

We’ll cover null and Not a Number (NaN) values in this chapter. Polars treats them differently and NaN values are technically a type of floating point data rather than missing data. That also means that there are different methods and expressions for null and NaN.

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