polars data

@DataPolars

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

Amsterdam

pola.rs

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polars data
@DataPolars
Sep 2
We are happy to announce the first pre-release of Polars 2.0. Polars 2.0 will bring the streaming engine as a default and improves overall strictness of Polars. Give it a spin and share your thoughts. We expect the final Polars 2.0 release in 3-4 weeks. pola.rs/posts/announci…
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polars data
@DataPolars
Aug 28
The Polars GPU engine now runs on a new streaming backend, built with NVIDIA on their RapidsMPF library. • Queries are no longer bound by GPU memory. Data flows through the query graph in morsels that spill to host memory when device memory gets tight. • The same query scales
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polars data
@DataPolars
Aug 26
We've released Python Polars 1.44. Some of the highlights: • Schema evolution when writing to Iceberg sink_iceberg() gained schema_mode="merge", so appending a frame that has a new column extends the table schema instead of raising. Rows written earlier read back as null for
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polars data
@DataPolars
Aug 25
As datasets grow, your tools can limit what analysis is possible. That causes teams to slows down, or forces them to work with samples rather than the whole dataset. BMLL Technologies tackled this issue by migrating their market data workloads to Polars, resulting in a 48x
polars data
@DataPolars
Aug 20
You can turn a column into labeled buckets. With if/elif/else logic takes one chained expression: pl.when().then().otherwise(). Conditions are checked top to bottom, and the first match wins, exactly like Python's if/elif/else. Chain as many .when() branches as you need, and