Proper `ndarray`/`tensor` support in next Polars release:

If you'd like your data science tool to support Polars, pandas, cuDF or Modin you might want to try out Narwhals. lnkd.in/exBH5csa I really like the idea and am trying to understand the "underwater unicorn magic" So in the meantime I painted a narwhal. Human for scale.

The first beta pre-release of Polars 1.0 is released! Install it with `pip install -U --pre polars ` and give it a spin. If you find any troubles in the coming week, we can fix them for 1.0. Release notes: github.com/pola-rs/polars… The upgrade guide will follow later next week.

Our new Polars streaming engine is still a way out, but it will be able to run any kind of expression. Combining reductions, elementwise functions, filters, sorts in a single context will all be handled. I can look at this picture in awe. 🤩

Polars 1.7 is out with super fast parallel non-equi joins! There is a new `join_where` method, where you can pass your (non)-equi predicates and Polars will choose the fastest join algorithm based on the predicates given. Full changelog: github.com/pola-rs/polars…

Thanks for having me on the SuperDataScience podcast. 🐻‍❄️ Following Marco Gorelli on the topic of Polars and Narwhals, I will talk about what Polars is and what we are building. youtube.com/watch?v=ubqF0y…

If you’re a #Python user familiar with #Pandas #Spark or Dask, this workshop is for you okt.to/oAzM9D! Discover how polars data can process data at lightning speed with its Rust-based engine.  #xebia #DataProcessing #Polars #DataScience #Rust #DeepLearning

I just love Polars so much

Why is there a `struct` data type? A single expression produces a single column, so expressions like `value_counts` need to output structs to map the values to their counts. With that said, do you understand why `.struct.unnest` doesn't break the 1 expr = 1 column principle?

Fighting for against the algorithm here

We finally support writing to cloud storage natively and seamlessly!

We removed serde from our Series struct and saw a significant drop in Polars' binary size (of all features activated). The amount of codegen is huge. 😮

💸 Reduced geocoding costs by 98%? We did that! Our custom Polars plugins helped a client save tens of thousands annually. Find out what we can do for YOUR data challenges! Read more buff.ly/3BLOAvB #DataFrames #OpenSource #Polars

This weeks Polars release has a huge improvement for window functions. They can be an order of magnitude faster. And we can run 20/22 TPC-H queries on the new streaming engine and all on Polars cloud. More will follow soon! ;) Full release docs here: github.com/pola-rs/polars…

This weeks Polars release we shipped initial Unity Catalog support. This makes integration with Databricks much smoother. Writing features are under development and will follow soon. Full release notes: github.com/pola-rs/polars…

The conclusion says the 20x isn't possible based on microbenchmarks. Completely ignoring query optimization once you stitch those operations together.

Already got all TPC-H queries running distributed!

Building Modern Data Applications with MCP Server - polars data On the left, Claude is still constrained to Pandas‑style APIs for Polars (rolling(window_size)) On the right, my custom MCP Server empowers Claude to tap into the full Polars ecosystem—providing comprehensive

Polars has gotten 4x faster than Polars! 🚀 In the last months, the team has worked incredibly hard on the new-streaming engine and the results pay off. It is incredibly fast, and beats the Polars in-memory engine by a factor of 4 on a 96vCPU machine.

This Thursday I will join Lawrence Mitchell from NVIDIA on the podium during the NVIDIA GTC in Paris. We'll discuss how we made Polars work on the GPU and how it will scale to multi-GPU in the future. On se voit là-bas ! vivatechnology.com/sessions/sessi…