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We are happy to share more about what we are building and our goal to run Polars on any dataset size! A managed Distributed Polars compute cluster to ensure a single DataFrame API for all your needs. pola.rs/posts/polars-c…

Today we added an kapa.ai (YC S23) LLM Plugin in our docs that has RAG access to our reference and user guide. This drastically improves Polars code generation! docs.pola.rs/api/python/sta…

The context filter lets you filter out rows from a dataframe based on some conditions. Within an aggregation, you can also use filter to filter values from aggregated groups. In this example we ignore unverified times when computing the current record.

New feature: query databases using just SQL, and get the results back as polars data dataframes. Learn more in this new video by BugBytes! youtube.com/watch?v=3y_4_Y…

marimo makes it easy to connect to your data wherever it is: query with SQL and get results back as a polars data dataframe. Shoutout to Shahmir Varqha and myles for bringing this feature to life. Stay tuned for more updates!

🐍📰 How to Work With Polars LazyFrames realpython.com/polars-lazyfra…

The expression over can be used to compute expressions within isolated groups. This means you can do computations per group without having to group first and then explode after. In this example, we rank swimmers based on their time, but within their race type.

Come join us at our first official Polars Meetup in Amsterdam! On April 3rd, we organize a hybrid event (including online Q&A during the talks) with a talk about how the new streaming engine operates and a community talk. Sign up today at meetup.com/polars-meetup/…

Dmitrii Kovanikov I've have people turn over unintelligible notebooks like 500+ lines of numpy operations, which I have replaced with ~10 lines of polars code, which runs as 1000x the speed. multiple times

Polars provides 3 functions you can use to generate temporal ranges: date_range, datetime_range, and time_range. These can be executed eagerly or lazily. You can also customize the interval between consecutive values and whether the start/end points are included.

This Thursday is our first (hybrid) meetup with two talks: - Introduction of the new streaming engine - Lessons learned from migrating a large code base to Polars First talks start at: 5:45 PM CEST | 11:45 AM EDT | 8:45 AM PDT RSVP to join: meetup.com/polars-meetup/…

Our book is out! 🥳 It’s official: 𝙋𝙮𝙩𝙝𝙤𝙣 𝙋𝙤𝙡𝙖𝙧𝙨: 𝙏𝙝𝙚 𝘿𝙚𝙛𝙞𝙣𝙞𝙩𝙞𝙫𝙚 𝙂𝙪𝙞𝙙𝙚 is now available in both ebook and print formats at your favorite bookstore! 📚 Grab your copy now: polarsguide.com

The recordings of our first Meetup are now available. Watch Orson Peters' talk to learn about the internals of the new streaming engine and its performance benefits. You can find the video here: youtube.com/watch?v=Ndil-e…

📁 Running into memory issues with Polars GPU at higher scale? cuDF Polars 24.12 introduces chunked Parquet reading + UVM, scaling smoothly past SF200 with improved throughput and stability. TechBlog ➡️ nvda.ws/4j4GBub

During our first Meetup, Jeroen and Thijs shared how they migrated a large codebase to Polars at a utility company and achieved a 98% cost reduction processing more data on smaller machines. Learn their best practices to migrate your codebase to Polars: youtube.com/watch?v=7DV6gC…

By customizing an MCP Server, you can meet modern Data Engineering requirements far more effectively. While LLMs are prone to hallucinations, an MCP Server helps curb those inaccuracies. I’ve added a new capability to the polars data MCP Server so it works and assists exactly

Imagine a 98% cost reduction by switching to Polars! Jeroen Janssens and Thijs Nieuwdorp share how Xomnia did it for Alliander, thanks to a lazy and streamlined API and smart caching. Watch the polars data recording: youtube.com/watch?v=7DV6gC… #PythonProgramming #PythonDev

Polars provides a number of xxx_horizontal operations. These expressions perform computations across columns. (Or along rows, depending on how you look at it.) If your horizontal operation isn’t implemented, you can use the general-purpose fold.

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.

🐍📰 How to Group Data Using Polars .group_by() realpython.com/polars-groupby/