Python Polars: The Definitive Guide (8th Early Release)КНИГИ » ПРОГРАММИНГ
Название: Python Polars: The Definitive Guide: Transforming, Analyzing, and Visualizing Data with a Fast and Expressive DataFrame API (8th Early Release) Автор: Jeroen Janssens, Thijs Nieuwdorp Издательство: O’Reilly Media, Inc. Год: 2024-12-03 Страниц: 406 Язык: английский Формат: epub (true) Размер: 10.1 MB
Want to speed up your data analysis and work with larger-than-memory datasets? Python Polars offers a blazingly fast, multithreaded, and elegant API for data loading, manipulation, and processing. With this hands-on guide, you'll walk through every aspect of Polars and learn how to tackle practical use cases using real-world datasets.
Jeroen Janssens and Thijs Nieuwdorp from Xomnia in Amsterdam show you how this superfast DataFrame library is perfect for efficient data wrangling, ETL pipelines, and so much more. This book helps you quickly learn the syntax and understand Polars' underlying concepts. You don't need to have experience with Pandas or Spark, but if you do, this book will help you make a smooth transition.
Since Polars is built in Rust, and has language bindings for Python, R, SQL, jаvascript, and Julia, you might be wondering why we focus on its Python API. According to the 2024 Stack Overflow Developer Survey, Python is the most popular programming language among respondents who are learning to code and the fourth most popular among professional developers. This is not surprising, since Python is known for its simplicity, readability, and versatility and is widely used in Data Science, Machine Learning, web development, and more. This popularity is reflected in the Polars community, where the Python API is the most complete, most used, and most updated API. Furthermore, Python is widely regarded as the language of choice for data analysis and data processing, and most data scientists and data engineers are familiar with it.
With this definitive guide at your side, you'll be able to: Process larger-than-memory datasets at record speed Apply the eager, lazy, and streaming APIs of Polars and decide when to use them Transition smoothly from pandas or Spark to Polars Integrate Polars into your existing code base Work with Arrow and Parquet to efficiently read and write data Translate complex ETL tasks into efficient and elegant queries
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Python Polars: The Definitive Guide (8th Early Release) 
