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@DataPolars

Dataframes powered by a multithreaded, vectorized query engine, written in Rust.

Amsterdam

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@DataPolars
Oct 27
Polars 2.1 is released! Three weeks after 2.0 we're shipping a big one. A few highlights: - Spill to disk for group-by and window functions - Recursive CTE's in SQL - Reading and writing encrypted Parquet - Better memory tracking for out-of-core - And over 90 performance
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@DataPolars
Oct 15
We ran Polars 2.0 SQL, DuckDB and DataFusion on an @nvidia Vera CPU and compared it to an Intel Xeon 6, m8id.metal-96xl. On the NVIDIA Vera, Polars ran 2 to 2.5x faster compared to Xeon, and 2 to 3x faster than the runner-up. pola.rs/posts/nvidia_v…
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@DataPolars
Oct 6
We just released Polars 2.0. It removed many of our legacy decisions makes the streaming engine our default and promotes SQL to a first class citizen within Polars. It comes with initial out-of-core (spill to disk) support, a new Map data type and a lot of performance
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@DataPolars
Sep 21
Polars 2.0.0rc2 has been released. We expect this to be the last release candidate and that Polars 2.0 will go live next week. Install now by running `pip install --pre polars` Link to the full release guide: docs.pola.rs/releases/upgra…
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@DataPolars
Sep 17
Profile any Polars query to view its progress and optimize performance with one line change: pl.Config.enable_monitoring() Read the full blog: pola.rs/posts/profile-…