How can you get more performance from your existing data science infrastructure? What if a DataFrame library could take advantage of your machine’s available cores and provide built-in methods for handling larger-than-RAM datasets? This week on the show, Liam Brannigan is here to discuss Polars.

Liam is an experienced data scientist working in finance, technology, and environmental analysis. He’s recently started contributing to the documentation for Polars and developing a training course for the library.

We talk about the library’s overall speed and lack of additional dependencies. Liam explains the advantages of lazy vs eager mode and which to choose when performing data exploration or attempting to load a dataset larger than your RAM.

We also discuss potential barriers to switching to Polars from a pandas workflow. Across our conversation, we explore several other libraries and technologies, including Apache Arrow, DuckDB, query optimization, and the “rustification” of Python tools.

Course Spotlight: Graph Your Data With Python and ggplot

In this course, you’ll learn how to use ggplot in Python to build data visualizations with plotnine. You’ll discover what a grammar of graphics is and how it can help you create plots in a very concise and consistent way.

Show Topics:

00:00:00 – Introduction
00:02:06 – Liam’s background and intro to Polars
00:03:37 – Hurdles to switching to Polars
00:05:23 – Creating training resources
00:08:15 – No index
00:09:46 – Data science 2025 predictions
00:12:02 – Contributions to Polars
00:15:07 – Eager vs lazy mode & query optimization
00:19:25 – Sponsor: Anaconda Nucleus
00:20:00 – Apache Arrow and parquet
00:24:43 – DuckDB and column orientation
00:29:27 – The “rustification” of libraries
00:34:49 – Video Course Spotlight
00:36:16 – GPUs and memory requirements
00:45:49 – No additional library requirements
00:47:37 – Development of the ecosystem
00:51:33 – Chaining operations
00:53:39 – How can people follow your work?
00:54:51 – What are you excited about in the world of Python?
00:56:09 – What do you want to learn next?
00:56:58 – Thanks and goodbye

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