We're hosting our second meetup in San Francisco on July 24th. Ritchie Vink will introduce Polars Cloud, the platform to scale Polars remotely. Vyas Ramasubramani from @nvidia will talk about the internals the Polars GPU engine. RSVP:

RT @realpython: 🐍📰 How to Group Data Using Polars .group_by(). Start using Polars .group_by() to make sense of your data. This tutorial sho….

The recent introduction of Unified Virtual Memory (UVM) for GPUs allows users to process data larger than the GPU's VRAM. Learn how UVM works in more detail and how to optimize the configuration for your use cases, including performance implications.

RT @holanda_pe: Starting from v1.4, DuckDB will also be able to produce Polars Lazy DataFrames. These include both projection and filter pu….

RT @marimo_io: Using @DataPolars? LazyFrames query plans now get a slick preview

Polars 1.31 is released. With as highlight DataType expressions✨. These allow you to get the datatype of an expression dynamically. See the full changelog here. Want something more with DataType expressions, here is the RFC:.

We've updated our benchmarks run. It has been more than a year since we ran them. Since then we've designed and implemented a complete novel streaming engine that can deal with Polars' data model. The future of Polars looks bright and very, very fast!.

We've partnered with @DataCamp to create an interactive Polars course. Learn the fundamentals and get familiar with our API through hands-on exercises. The course is available for everyone and free until the end of August. Start the free course here:

RT @realpython: 🐍📰 How to Group Data Using Polars .group_by()

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.

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.

RT @posit_pbc: Imagine a 98% cost reduction by switching to Polars!. @jeroenhjanssens and Thijs Nieuwdorp share how @Xomnia_BV did it for @….

RT @Ubunta: By customizing an MCP Server, you can meet modern Data Engineering requirements far more effectively. While LLMs are prone to h….

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:

RT @NVIDIAAIDev: 📁 Running into memory issues with Polars GPU at higher scale?. cuDF Polars 24.12 introduces chunked Parquet reading + UVM,….

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:

RT @jeroenhjanssens: Our book is out! 🥳. It’s official: 𝙋𝙮𝙩𝙝𝙤𝙣 𝙋𝙤𝙡𝙖𝙧𝙨: 𝙏𝙝𝙚 𝘿𝙚𝙛𝙞𝙣𝙞𝙩𝙞𝙫𝙚 𝙂𝙪𝙞𝙙𝙚 is now available in both ebook and print format….

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:

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.

RT @r0b0t_sp1der: @ChShersh I've have people turn over unintelligible notebooks like 500+ lines of numpy operations,. which I have replaced….