Data Analysis with Polars
Get up and running with Polars to perform effective data analysis with Rust
Will be published approx. on 31. July 2026
Book
Paperback/Softback
978-1-83763-910-6 (ISBN)
Description
Leverage Polars, the lightning-fast DataFrame library, to take your Python data analysis skills to the next level
Key Features
Speed up your data transformations in Python and handle larger-than-memory datasets
Get up to speed with the Polars library, including its fundamentals and advanced data transformations
Apply Polars to five real-world business cases, including financial and marketing analysis
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionDiscover the power of Polars, the lightning-fast DataFrame library, for fast data manipulation and analysis. This comprehensive guide provides you with in-depth insights into Polars: its interface, how Polars works behind the scenes, and how to use it in practical applications.
You'll start with the fundamentals of Polars, such as reading and writing files and databases, and transforming data. Next, you'll progress to advanced topics such as window functions, time-series analysis, text transformations, processing larger-than-memory datasets, and testing data pipelines. You'll also learn how to leverage SQL with Polars. The second section of the book guides you through five case studies step by step, allowing you to apply your learning to business problems. Through the real-life case studies, you'll find out how to perform financial and marketing analyses, such as price-volume-mix analysis, market basket analysis, customer segmentation, product recommendations, and text analysis.
By the end of this book, you'll have gained the knowledge and skills you need to advance your Python data analysis skills using Polars and take your career to the next level.What you will learn
Find out why Polars is faster and how it benefits from Arrow and Rust
Understand Polars query optimizations and the lazy API
Connect Polars to your files, databases, and data lakes
Discover how to transform, group, and combine data with parallel execution
Get up to speed with advanced data transformations, including window and array functions
Explore how to query Polars using SQL
Extend Polars functionality with custom functions and namespaces
Perform exploratory data analysis and visualization using Polars and Altair
Who this book is forIf you're a data analyst looking to analyze your data faster or analyze bigger datasets (or both), then this book is for you. Business analysts, data engineers, and data scientists will also benefit from this book. Basic knowledge of Python is necessary to get the most out of this book. Additionally, any previous experience with data analysis in Pandas, Spark, or SQL will be helpful.
Key Features
Speed up your data transformations in Python and handle larger-than-memory datasets
Get up to speed with the Polars library, including its fundamentals and advanced data transformations
Apply Polars to five real-world business cases, including financial and marketing analysis
Purchase of the print or Kindle book includes a free PDF eBook
Book DescriptionDiscover the power of Polars, the lightning-fast DataFrame library, for fast data manipulation and analysis. This comprehensive guide provides you with in-depth insights into Polars: its interface, how Polars works behind the scenes, and how to use it in practical applications.
You'll start with the fundamentals of Polars, such as reading and writing files and databases, and transforming data. Next, you'll progress to advanced topics such as window functions, time-series analysis, text transformations, processing larger-than-memory datasets, and testing data pipelines. You'll also learn how to leverage SQL with Polars. The second section of the book guides you through five case studies step by step, allowing you to apply your learning to business problems. Through the real-life case studies, you'll find out how to perform financial and marketing analyses, such as price-volume-mix analysis, market basket analysis, customer segmentation, product recommendations, and text analysis.
By the end of this book, you'll have gained the knowledge and skills you need to advance your Python data analysis skills using Polars and take your career to the next level.What you will learn
Find out why Polars is faster and how it benefits from Arrow and Rust
Understand Polars query optimizations and the lazy API
Connect Polars to your files, databases, and data lakes
Discover how to transform, group, and combine data with parallel execution
Get up to speed with advanced data transformations, including window and array functions
Explore how to query Polars using SQL
Extend Polars functionality with custom functions and namespaces
Perform exploratory data analysis and visualization using Polars and Altair
Who this book is forIf you're a data analyst looking to analyze your data faster or analyze bigger datasets (or both), then this book is for you. Business analysts, data engineers, and data scientists will also benefit from this book. Basic knowledge of Python is necessary to get the most out of this book. Additionally, any previous experience with data analysis in Pandas, Spark, or SQL will be helpful.
More details
Language
English
Place of publication
Birmingham
United Kingdom
Target group
Professional and scholarly
Dimensions
Height: 235 mm
Width: 191 mm
ISBN-13
978-1-83763-910-6 (9781837639106)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Persons
Luca Zanna is a Data Engineer and Data Analyst with over 15 years of experience. He started his career as financial data analyst after a Master in Management and passing the Certified Public Accountant (CPA) exam. Luca spent a decade working on financial analysis systems at L'Oreal: developing the systems and training financial analysts across Europe and Asia.
Currently, Luca helps companies with building data infrastructure to better leverage their data. Luca is also a corporate teacher for topics such as data analysis, SQL, Python, and cloud data engineering. Alexander Beedie has been a software engineer working on some of the most complex trading and risk management platforms in investment banking technology for nearly 20 years, and wrote one of the world's first production DataFrame libraries back in 2008 for use in JPMorgan's Athena platform. Specialising in the development of novel high-performance data APIs, he currently works at the Abu Dhabi Investment Authority as a Quantitative Research & Development Lead and is also one of the Core developers of Polars. He holds a Master's degree in Computer Science and a Bachelor's degree in Astrophysics, both from University College London (UCL). Jung Hoon Son, M.D. is a physician biomedical informaticist and data scientist dedicated to leveraging data-powered insights to advance healthcare. As a Knowledge and Solutions Architect at a leading neuroscience-focused biopharma company, he applies his medical expertise, data engineering, data visualization, and analytics skills to drive drug discovery and development. Dr. Son has authored scientific publications in the fields of genomics, informatics, nephrology, pathology, and ophthalmology. He has presented at numerous conferences and holds patents for machine learning applications in healthcare. Dr. Son received his undergraduate degree from Cornell University in chemistry and psychology before earning his medical degree from UMDNJ-New Jersey Medical School. He then completed his pathology residency program and biomedical informatics postdoctoral fellowship at Columbia University.
Currently, Luca helps companies with building data infrastructure to better leverage their data. Luca is also a corporate teacher for topics such as data analysis, SQL, Python, and cloud data engineering. Alexander Beedie has been a software engineer working on some of the most complex trading and risk management platforms in investment banking technology for nearly 20 years, and wrote one of the world's first production DataFrame libraries back in 2008 for use in JPMorgan's Athena platform. Specialising in the development of novel high-performance data APIs, he currently works at the Abu Dhabi Investment Authority as a Quantitative Research & Development Lead and is also one of the Core developers of Polars. He holds a Master's degree in Computer Science and a Bachelor's degree in Astrophysics, both from University College London (UCL). Jung Hoon Son, M.D. is a physician biomedical informaticist and data scientist dedicated to leveraging data-powered insights to advance healthcare. As a Knowledge and Solutions Architect at a leading neuroscience-focused biopharma company, he applies his medical expertise, data engineering, data visualization, and analytics skills to drive drug discovery and development. Dr. Son has authored scientific publications in the fields of genomics, informatics, nephrology, pathology, and ophthalmology. He has presented at numerous conferences and holds patents for machine learning applications in healthcare. Dr. Son received his undergraduate degree from Cornell University in chemistry and psychology before earning his medical degree from UMDNJ-New Jersey Medical School. He then completed his pathology residency program and biomedical informatics postdoctoral fellowship at Columbia University.
Content
Table of Contents
Introduction to Polars
A Primer on Data Analysis with Polars
Intermediate Data Transformation with Polars
Advanced Data Transformation with Polars
Polars Input and Output
Polars SQL
Data Visualization with Altair
Performance, testing, and extending polars
Introduction to Polars
A Primer on Data Analysis with Polars
Intermediate Data Transformation with Polars
Advanced Data Transformation with Polars
Polars Input and Output
Polars SQL
Data Visualization with Altair
Performance, testing, and extending polars