Python Polars: The Definitive Guide: Transforming, Analyzing, and Visualizing Data with a Fast and Expressive DataFrame API

This hands-on guide by Jeroen Janssens and Thijs Nieuwdorp introduces readers to Polars, a high-performance Python package designed for efficient data transformation, analysis, and visualization. The book is structured to assist both seasoned data professionals and newcomers to data science in mastering the library's expressive API and core concepts. While prior experience with pandas is not required, the text specifically aids those familiar with it to make a seamless transition. Through practical examples and real-world datasets available on GitHub, readers learn to process data from various sources, including CSV files, Parquet, spreadsheets, databases, and cloud storage. The core of the book focuses on the fundamental building blocks of Polars, particularly its powerful Expressions system. Readers will gain a solid understanding of how to construct queries and handle complex data types such as text, time series, and nested structures. The authors explain the distinction between eager and lazy APIs, providing guidance on when to utilize each for optimal results. Furthermore, the guide covers essential techniques for visualizing data using libraries like Altair, hvPlot, plotnine, and Great Tables, ensuring that users can effectively communicate their findings through clear and compelling graphics. Beyond the basics, the book explores advanced topics to extend and accelerate data workflows. It demonstrates how to customize Polars with user-defined Python functions and Rust plugins, allowing for tailored solutions to specific problems. Additionally, the text details how to leverage GPU acceleration to significantly boost performance for large-scale data tasks. By covering everything from initial data wrangling and exploratory analysis to building robust pipelines, this resource serves as a comprehensive manual for unlocking the full potential of Polars in real-world data science applications.