Polars Cookbook

Description

Leverage Polars, a lightning-fast DataFrame library, to transform your Python-based data science projects with efficient data wrangling and manipulation Key Features • Unlock the power of Python Polars for faster and more efficient data analysis workflows • Master the fundamentals of Python Polars with step-by-step recipes • Discover data manipulation techniques to apply across multiple data problems Book Description 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. Who is this book for? This book is for data analysts, data scientists, and data engineers who want to learn how to use Polars in their workflows. Working knowledge of the Python programming language is required. Experience working with a DataFrame library such as pandas or PySpark will also be helpful. What you will learn • Read from different data sources and write to various files and databases • Apply aggregations, window functions, and string manipulations • Perform common data tasks such as handling missing values and performing list and array operations • Discover how to reshape and tidy your data by pivoting, joining, and concatenating • Analyze your time series data in Python Polars • Create better workflows with testing and debugging