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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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Interoperability with Other Python Libraries

Although Polars is an awesome tool that’s fast and efficient, there are times when interoperating with other tools or libraries is crucial in your data projects. The good news is that there are libraries out there already that can work with Polars. In Chapter 9, Time Series Analysis, you‘ve already seen that it works well with the functime and plotly libraries. Polars can also work with other Python libraries such as pandas, NumPy, PyArrow, and DuckDB to name a few. As Polars matures more as a tool, there will be more libraries and tools, making the integration and interoperability between Polars and the whole Python data ecosystem more seamless. For instance, having a seamless integration with other Python libraries benefits Polars by providing functionalities it doesn’t yet have. It’ll give you more options for how you implement your solution.

By the end of this chapter, you’ll gain an understanding...

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