Oct 20, 2023

pola.rs looks super neat:

Lightning-fast DataFrame library for Rust and Python

Polars is written in Rust, uncompromising in its choices to provide a feature-complete DataFrame API to the Rust ecosystem. Use it as a DataFrame library or as query engine backend for your data models.

Polars is built upon the safe Arrow2 implementation of the Apache Arrow specification, enabling efficient resource use and processing performance. By doing so it also integrates seamlessly with other tools in the Arrow ecosystem.

They are working on a webassembly version, but there's nothing released yet. Would love to use this for NBA Stats

here is an ipython notebook demonstrating usage of polars with a neat medicare data set. Shows off a handy feature of polars compared to pandas that it can filter the data on read, rather than having to load it all into memory first. Also uses Altair for visualization


This is a side-by-side comparison of the Polars and Pandas dataframe libraries, based on Modern Pandas by Tom Augsburger.

(In case you haven’t heard, Polars is a very fast and elegant dataframe libary that does the same kinds of things Pandas does.)

The bulk of this book is structured examples of idiomatic Polars and Pandas code, with commentary on the API and performance of both.

For the most part, I argue that Polars is “better” than Pandas, though I do try and make it clear when Polars is lacking a Pandas feature or is otherwise disappointing.

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