Polars Cloud client 0.3.0 is released. You can now spawn >100k queries to a single cluster and we load balance them gracefully. Additionally, the query planning now is posted as a worker task and can be cancelled by the user. github.com/pola-rs/pola...

@decathlonfrance.bsky.social has adopted Polars across many workloads, reducing infrastructure complexity and overhead by running workloads on single machines instead of compute clusters. Learn more in the case study: pola.rs/posts/case-d...

Today we launch Polars Cloud and the Public Beta of our Distributed Engine. Read the post to get started! pola.rs/posts/polars...

It is almost time for the 3rd official Polars meetup! The next meetup will take place on the 17th of September in Munich, Germany. There will be a talk the Polars team and a community talk. It is also the perfect place to meet fellow Polars users. RSVP here: www.meetup.com/polars-meetu...

Are you looking to get started with Polars over the summer? We've partnered with @datacamp.bsky.social to create an interactive course that covers the fundamentals so you can write your next query with Polars. The course is free till the end of August: www.datacamp.com/courses/intr...

Polars 1.32, a thread...

The third Polars Meetup is confirmed! On 17 September, we will organize the next in-person meetup at @quantco.com in Munich, Germany. Topics: - Parquet reader improvements - Migrating pipelines using Dataframely, Quantco's open-sourced schema validation tool. RSVP: www.meetup.com/polars-meetu...

The videos of our recent San Francisco meetup are in! - Ritchie Vink covered the new streaming engine and shared updates on Polars Cloud and the upcoming distributed engine. - Vyas Ramasubramani shared how GPU accelerated Polars works. youtube.com/watch?v=fYi9... youtube.com/watch?v=fYi9S6

"Came for the speed, stayed for the syntax." Once users have learned our API, they love it. And it shows. Polars is the 3rd most admired rising tech in this years StackOverflow developer survey. survey.stackoverflow.co/2025/technol...

The pre-release of polars 1.32 is out. This is a big release with a lot of internal improvements. Please consider trying it so that we can patch any regression before we ship the release next week. github.com/pola-rs/pola...

✨ New Formulaic release (1.2), featuring native support for @pola.rs ! 🌊🦄 Powered by Narwhals

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 be talking about the internals of accelerating your Polars queries with the GPU engine. RSVP: lu.ma/60b6wfs8

🐍📰 How to Group Data Using Polars .group_by() Start using Polars .group_by() to make sense of your data. This tutorial shows you how to group, aggregate, and reveal hidden insights with hands-on examples #python

@richmeister.bsky.social has released the "Data Validation Libraries for Polars (2025 Edition)" guide! Reviews 5 #Python validation libraries that work with @pola.rs DataFrames: Pandera, Patito, Pointblank, Validoopsie, & Dataframely. Read it on the Pointblog: posit-dev.github.io/pointblank/b...

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. pola.rs/posts/uvm-la...

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. github.com/pola-rs/pola... Want something more with DataType expressions, here is the RFC: github.com/pola-rs/polars

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! pola.rs/posts/benchm...

We've partnered with @datacamp.bsky.social 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: www.datacamp.com/courses/intr...

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

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.

Metro.digital used Polars to simplify the KPI creation process across 19 countries, cutting down the processing time while reducing compute resource requirements. Dr. Patrick Bormann shares his experience and some of the optimization techniques he and his team used: pola.rs/posts/case-m...

Imagine a 98% cost reduction by switching to Polars! @jeroenjanssens.com and Thijs Nieuwdorp share how Xomnia did it for Alliander, thanks to a lazy and streamlined API and smart caching. Watch the @pola.rs meetup recording: www.youtube.com/watch?v=7DV6... #PythonProgramming #PythonDev

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 from their best practices to migrate your codebase to Polars: www.youtube.com/watch?v=7DV6...

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. Watch here: www.youtube.com/watch?v=Ndil...

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: www.meetup.com/polars-meetu...

Combining strings in @pola.rs is easy!

Computers are good at capturing timestamps, but they do it in Unix time, which is hard for humans to understand. @pola.rs allows you to convert Unix time to human-readable datetime. #100DaysOfPolars #datasky

When displaying budget figures, you may want to include a currency symbol. @pola.rs allows you to easily add any currency symbol to your figures. #100DaysOfPolars #datasky

Tabular #data is easier to process when columns contain singular values. But sometimes, data is stored as a list in one column. With @pola.rs, you can make every item in the list as it's own row. #100DaysOfPolars #datasky

Sometimes, you want the columns in your table to be in a specific order. But typing out the names can be tedious Luckily @pola.rs has a way that doesn't require typing names. #100DaysOfPolars #datasky

Always remove whitespace before doing a unique count of values to get an accurate count. In @pola.rs, there's a function to do just that! #100DaysOfPolars #datasky

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.

Pivot Dataframes with @pola.rs

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 www.meetup.com/polars-meetup/events/306569082/

📺🐍 Working With Python Polars

🐍📰 How to Work With Polars LazyFrames

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.

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.

🐻‍❄️ Under-appreciated @pola.rs feature: seamless expression composability 🎢 In Polars, `shift` and `rolling_mean` can be chained 💡 In SQL, you need a common table subexpression, because LAG isn't a scalar (i.e. elementwise) function

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...

Learning polars has been ... actually a joy? It just makes sense to my #rstats #dplyr trained data muscles #databs #python kevinheavey.github.io/modern-polars/

The expression `clip` is pretty straightforward: You provide a lower and an upper bound, and Polars makes sure all values fall within those bounds. If a value is too small/too large, it's replaced by the bound. Bounds can be literals, other columns, or arbitrary expressions.

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! ;) See the full release docs here: github.com/pola-rs/pola...

Join our webinar with NVIDIA on January 28 for an in-depth session on how the GPU engine works, from collecting your query to parallel execution on the GPU. Sign up at info.nvidia.com/nvidia-polar... See you there?

Polars 1.19 comes with support for arbitrary predicates in join_where. This means that inequality joins are now more flexible than ever! Here is a small example of something you couldn't do before:

Pyodide 0.27 has been released 🎉 This release includes some new data science and data engineering packages: PyArrow, @pola.rs, @duckdb.org. This means you can play around with these packages in your browser. Read everything here: blog.pyodide.org/posts/0.27-r...

Polars supports dynamic aggregations based on time windows via the function `group_by_dynamic`. To use it, you specify a date(time) column to group by, and then determine the windows over which values are aggregated. Note how data points can fall within multiple windows 👇

We finally support writing to cloud storage natively and seamlessly!

If you're learning @pola.rs, this graphic (and the accompanying blog post) are excellent reference material 👇 pola.rs/posts/unders...