This is the code repository for [Data Analysis with Polars](?utm_source=github&utm_medium=repository&utm_campaign=<13-P ISBN>), published by Packt.
Get Up and Running with Polars to perform effective data analysis in Rust
Discover the power of Polars, the lightning-fast dataframe library, with ‘Data analysis with Polars’. This comprehensive guide covers everything you need to know about Polars: its interface, how Polars works under-the-scenes, and how to use it in practical applications.
This book covers the following exciting features:
- Learn 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 datalakes
- Discover to transform, group and combine data with parallel execution
- Learn advanced data transformations including window and array functions
- Discover how to query Polars using SQL
- Use Polars for streaming to handle data bigger than memory
- Apply your knowledge to 5 real-life financial and marketing case studies
If you feel this book is for you, get your copy today!
All of the code is organized into folders.
The code will look like the following:
<Any code block from the book>
Following is what you need for this book: If you are a data analyst wanting to analyze your data faster or analyze bigger datasets (or both!) then this is the book for you. Business analysts, data engineers and data scientists will also strongly benefit from this book. A basic knowledge of Python is necessary to fully benefit from this book. Any previous experience with data analysis in Pandas, Spark or SQL will help you get the most out of your reading.
With the following software and hardware list you can run all code files present in the book (Chapter 1-8).
| Chapter | Software required | OS required |
|---|---|---|
| 1-8 | Python | Windows, Mac OS X, and Linux (Any) |
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’Oréal: 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.