Enable javascript in your browser for better experience. Need to know to enable it? Go here.
Last updated : Sep 27, 2023
NOT ON THE CURRENT EDITION
This blip is not on the current edition of the Radar. If it was on one of the last few editions, it is likely that it is still relevant. If the blip is older, it might no longer be relevant and our assessment might be different today. Unfortunately, we simply don't have the bandwidth to continuously review blips from previous editions of the Radar. Understand more
Sep 2023
Trial ?

Polars is an in-memory data frame library implemented in Rust. Unlike other data frames (such as pandas), Polars is multithreaded, supports lazy execution and is safe for parallel operations. The in-memory data is organized in the Apache Arrow format for efficient analytic operations and to enable interoperability with other tools. If you're familiar with pandas, you can quickly get started with Polars' Python bindings. We believe Polars, with Rust implementation and Python bindings, is a performant in-memory data frame for your analytical needs. Our teams continue to have a good experience with Polars which is why we're moving it to Trial.

Oct 2021
Assess ?

Polars is an in-memory data frame library implemented in Rust. Unlike other data frames (such as Pandas), Polars is multithreaded and safe for parallel operations. The in-memory data is organized in the Apache Arrow format for efficient analytic operations and to enable interoperability with other tools. If you're familiar with Pandas, you can quickly get started with Polars' Python bindings. We believe Polars, with Rust implementation and Python bindings, is a performant in-memory data frame to assess for your analytical needs.

Published : Oct 27, 2021

Download the PDF

 

 

English | Español | Português | 中文

Sign up for the Technology Radar newsletter

 

Subscribe now

Visit our archive to read previous volumes