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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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Data Transformation Techniques

In this chapter, we will look at how aggregations, window functions, and User-Defined Functions (UDFs) are essential tools in data analysis, data science, and data engineering workflows. We’ll also cover how we can use SQL in Python Polars.

We will understand how aggregations involve combining and summarizing data to gain insights. They are commonly used in data analysis to perform operations such as sum, average, count, or maximum on a dataset. They help summarize your data and compute the necessary parts to further your data transformations.

We will also understand how window functions, on the other hand, allow you to perform calculations across a specific window or subset of data within a dataset. They are valuable in data analysis for tasks such as ranking and identifying trends within a partition of data.

Furthermore, we will learn about UDFs that provide flexibility by allowing you to define custom functions to process and transform...

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Polars Cookbook
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