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  • Book Overview & Buying Polars Cookbook
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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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Joining DataFrames

A join operation is used to merge rows from two or more datasets by utilizing a shared column that establishes a relationship between them. You may already be familiar with the use and concept of joining, but it’s commonly used in any data processing tools such as SQL and other DataFrame libraries such as pandas and Spark.

In this recipe, we’ll look at how to apply join operations in Polars DataFrames.

Getting ready

We’ll continuously use the same data we’ve used in previous recipes in this chapter. Execute the following code to do the same process and rename the DataFrame accordingly:

from polars import selectors as cs
academic_df = (
    pl.read_csv('../data/academic.csv')
    .select(
        pl.col('year').alias('academic_year'),
        cs.numeric().cast(pl.Int64)
 ...
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Polars Cookbook
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