In 1-2 weeks we land live query profiling in Polars Cloud.
See exactly how many rows are consumed and produced per operation. Which operation takes most runtime, and watch the data flow through live, like water. 😍
See exactly how many rows are consumed and produced per operation. Which operation takes most runtime, and watch the data flow through live, like water. 😍
February 2, 2026 at 2:43 PM
In 1-2 weeks we land live query profiling in Polars Cloud.
See exactly how many rows are consumed and produced per operation. Which operation takes most runtime, and watch the data flow through live, like water. 😍
See exactly how many rows are consumed and produced per operation. Which operation takes most runtime, and watch the data flow through live, like water. 😍
ClickBench now runs the Polars streaming engine.
Polars is the fastest solution on that benchmark on Parquet file(s) 😎
The speed is there. This year, we will tackle out of core (spill to disk) and distributed to truly tackle scale.
benchmark.clickhouse.com#system=-ahi|...
Polars is the fastest solution on that benchmark on Parquet file(s) 😎
The speed is there. This year, we will tackle out of core (spill to disk) and distributed to truly tackle scale.
benchmark.clickhouse.com#system=-ahi|...
January 14, 2026 at 11:13 AM
ClickBench now runs the Polars streaming engine.
Polars is the fastest solution on that benchmark on Parquet file(s) 😎
The speed is there. This year, we will tackle out of core (spill to disk) and distributed to truly tackle scale.
benchmark.clickhouse.com#system=-ahi|...
Polars is the fastest solution on that benchmark on Parquet file(s) 😎
The speed is there. This year, we will tackle out of core (spill to disk) and distributed to truly tackle scale.
benchmark.clickhouse.com#system=-ahi|...
The pre-release of Polars 1.36 is out. Please give it a try so that we can ensure a stable final release with minimal regressions.
It lands a lot of goodies:
- Extension types
- Lazy pivots
- Streaming group_by_dynamic
- Float16 support
- Nested .over() expressions
github.com/pola-rs/pola...
It lands a lot of goodies:
- Extension types
- Lazy pivots
- Streaming group_by_dynamic
- Float16 support
- Nested .over() expressions
github.com/pola-rs/pola...
Release Python Polars 1.36.0-beta.2 · pola-rs/polars
🏆 Highlights
Add Extension types (#25322)
🚀 Performance improvements
Reduce HuggingFace API calls (#25521)
Use strong hash instead of traversal for CSPE equality (#25537)
Fix panic in is_between...
github.com
December 2, 2025 at 1:07 PM
The pre-release of Polars 1.36 is out. Please give it a try so that we can ensure a stable final release with minimal regressions.
It lands a lot of goodies:
- Extension types
- Lazy pivots
- Streaming group_by_dynamic
- Float16 support
- Nested .over() expressions
github.com/pola-rs/pola...
It lands a lot of goodies:
- Extension types
- Lazy pivots
- Streaming group_by_dynamic
- Float16 support
- Nested .over() expressions
github.com/pola-rs/pola...
Polars 1.34.0 is out!
Any Polars query can be turned into a generator!
Aside from that Polars now properly supports decimal types, scan_iceberg is completely native, cross joins can maintain order and much more.
Changelogs here:
- github.com/pola-rs/pola...
- github.com/pola-rs/pola...
Any Polars query can be turned into a generator!
Aside from that Polars now properly supports decimal types, scan_iceberg is completely native, cross joins can maintain order and much more.
Changelogs here:
- github.com/pola-rs/pola...
- github.com/pola-rs/pola...
October 3, 2025 at 2:34 PM
Polars 1.34.0 is out!
Any Polars query can be turned into a generator!
Aside from that Polars now properly supports decimal types, scan_iceberg is completely native, cross joins can maintain order and much more.
Changelogs here:
- github.com/pola-rs/pola...
- github.com/pola-rs/pola...
Any Polars query can be turned into a generator!
Aside from that Polars now properly supports decimal types, scan_iceberg is completely native, cross joins can maintain order and much more.
Changelogs here:
- github.com/pola-rs/pola...
- github.com/pola-rs/pola...
Polars 1.32 is out and it lands a lot!
Let's go through a few:
1/4
Selectors are now implemented in Rust and we can finally select arbitrary nested types:
Let's go through a few:
1/4
Selectors are now implemented in Rust and we can finally select arbitrary nested types:
August 11, 2025 at 3:03 PM
Polars 1.32 is out and it lands a lot!
Let's go through a few:
1/4
Selectors are now implemented in Rust and we can finally select arbitrary nested types:
Let's go through a few:
1/4
Selectors are now implemented in Rust and we can finally select arbitrary nested types:
Join me the 24th in SF for a @pola.rs meetup!
I will be having a talk about Polars, Polars-Cloud and the upcoming distributed engine.
NVIDIA will also be doing a talk about their GPU acceleration with Polars-CuDF
Hope to see you there!
lu.ma/60b6wfs8
I will be having a talk about Polars, Polars-Cloud and the upcoming distributed engine.
NVIDIA will also be doing a talk about their GPU acceleration with Polars-CuDF
Hope to see you there!
lu.ma/60b6wfs8
Polars Meetup - Polars Cloud and Acceleration · Luma
Join the second edition of our Polars Meetup with talks from Ritchie Vink (Polars) and Vyas Ramasubramani (NVIDIA) to discuss accelerating and scaling…
lu.ma
July 14, 2025 at 5:42 PM
Join me the 24th in SF for a @pola.rs meetup!
I will be having a talk about Polars, Polars-Cloud and the upcoming distributed engine.
NVIDIA will also be doing a talk about their GPU acceleration with Polars-CuDF
Hope to see you there!
lu.ma/60b6wfs8
I will be having a talk about Polars, Polars-Cloud and the upcoming distributed engine.
NVIDIA will also be doing a talk about their GPU acceleration with Polars-CuDF
Hope to see you there!
lu.ma/60b6wfs8
No more `with pl.StringCache()`
Soon... 🌈
Soon... 🌈
July 4, 2025 at 3:05 PM
No more `with pl.StringCache()`
Soon... 🌈
Soon... 🌈
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/ses...
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/ses...
GP1085: Scaling DataFrames With Polars - Theme: undefined
Location: NVIDIA GTC PARIS - Pavillon 7 - June 12 1:00 PM 1:45 PM - CET | Resume: Room: N03
Polars is a query engine with a DataFrame frontend designed for fast, efficient data processing. This sess...
vivatechnology.com
June 10, 2025 at 1:58 PM
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/ses...
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/ses...
Reposted by Ritchie Vink
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.
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.
May 1, 2025 at 2:05 PM
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.
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.
Reposted by Ritchie Vink
** Sponsor announcement ** Polars is a Supporter of RustWeek!
Find out more about them here: pola.rs
Thank you @pola.rs for your support! 🙏
More info about RustWeek and tickets: rustweek.org
#rustweek #rustlang
Find out more about them here: pola.rs
Thank you @pola.rs for your support! 🙏
More info about RustWeek and tickets: rustweek.org
#rustweek #rustlang
April 18, 2025 at 12:07 PM
** Sponsor announcement ** Polars is a Supporter of RustWeek!
Find out more about them here: pola.rs
Thank you @pola.rs for your support! 🙏
More info about RustWeek and tickets: rustweek.org
#rustweek #rustlang
Find out more about them here: pola.rs
Thank you @pola.rs for your support! 🙏
More info about RustWeek and tickets: rustweek.org
#rustweek #rustlang
Already got all TPC-H queries running distributed!
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...
A managed Distributed Polars compute cluster to ensure a single DataFrame API for all your needs.
pola.rs/posts/polars...
Polars Cloud; the distributed Cloud Architecture to run Polars anywhere
DataFrames for the new era
pola.rs
February 12, 2025 at 2:16 PM
Already got all TPC-H queries running distributed!
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/pola...
Writing features are under development and will follow soon. Full release notes: github.com/pola-rs/pola...
January 25, 2025 at 11:47 AM
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/pola...
Writing features are under development and will follow soon. Full release notes: github.com/pola-rs/pola...
Reposted by Ritchie Vink
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/
kevinheavey.github.io/modern-polars/
Modern Polars
A side-by-side comparison of the Polars and Pandas libraries.
kevinheavey.github.io
January 23, 2025 at 12:02 AM
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/
kevinheavey.github.io/modern-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! ;)
See the full release docs here:
github.com/pola-rs/pola...
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...
Release Python Polars 1.20.0 · pola-rs/polars
⚠️ Deprecations
Make parameter of str.to_decimal keyword-only (#20570)
🚀 Performance improvements
Extend functionality on BitmapBuilder and use in Growables (#20754)
Specialize first/last agg fo...
github.com
January 19, 2025 at 8:57 AM
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...
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...
Reposted by Ritchie Vink
Recently I've been working on getting #polars running in #pyodide. This was a fun one, even requiring patches to LLVM's #wasm writer! Everything has now been upstreamed and earlier this week Pyodide v0.27.0 released, including a Wasm build of Polars usable in Pyodide, Shinylive and Quarto Live 🎉
![A screenshot of a Shinylive app using Polars code:
from shiny import App, render, ui
import polars as pl
from pathlib import Path
app_ui = ui.page_fluid(
ui.input_select("cyl", "Select Cylinders", choices=["4", "6", "8"]),
ui.output_data_frame("filtered_data")
)
def server(input, output, session):
df = pl.read_csv(Path(__file__).parent / "mtcars.csv")
@output
@render.data_frame
def filtered_data():
return (df
.filter(pl.col("cyl") == int(input.cyl()))
.select(["mpg", "cyl", "hp"]))
app = App(app_ui, server)](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:hnkswprmt42fkt2huzbkcpcx/bafkreifcr4vfugeei25pfex52gvvvxnukyy4sms4ytpk6isepn5c3j473i@jpeg)
January 4, 2025 at 11:59 AM
Reposted by Ritchie Vink
✨ New temporal feature in the next Polars release!
⏲️ dt.replace lets you replace components of Date / Datetime columns
⚡🦀 It's an expressified vectorised rustified version of the Python standard library datetime.replace
⏲️ dt.replace lets you replace components of Date / Datetime columns
⚡🦀 It's an expressified vectorised rustified version of the Python standard library datetime.replace
December 20, 2024 at 4:25 PM
✨ New temporal feature in the next Polars release!
⏲️ dt.replace lets you replace components of Date / Datetime columns
⚡🦀 It's an expressified vectorised rustified version of the Python standard library datetime.replace
⏲️ dt.replace lets you replace components of Date / Datetime columns
⚡🦀 It's an expressified vectorised rustified version of the Python standard library datetime.replace
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. 😮
December 18, 2024 at 1:32 PM
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. 😮
We finally support writing to cloud storage natively and seamlessly!
December 12, 2024 at 11:04 AM
We finally support writing to cloud storage natively and seamlessly!
Reposted by Ritchie Vink
Join us this Friday if you're eager to see what it can be like to design a recommender while limiting ourselves to just a DataFrame API. It is somewhat unconventional, but a great excuse to show off a Polars trick or two.
www.youtube.com/watch?v=U3Fi...
www.youtube.com/watch?v=U3Fi...
Making a recommender by just using Polars!
YouTube video by probabl
www.youtube.com
December 10, 2024 at 10:51 AM
Join us this Friday if you're eager to see what it can be like to design a recommender while limiting ourselves to just a DataFrame API. It is somewhat unconventional, but a great excuse to show off a Polars trick or two.
www.youtube.com/watch?v=U3Fi...
www.youtube.com/watch?v=U3Fi...
Reposted by Ritchie Vink
How to “expand” ranges like "3-5" across new rows with the values 3, 4, 5?
This comes straight from our Discord server (discord.com/invite/4UfP5...)
This comes straight from our Discord server (discord.com/invite/4UfP5...)
November 21, 2024 at 2:36 PM
How to “expand” ranges like "3-5" across new rows with the values 3, 4, 5?
This comes straight from our Discord server (discord.com/invite/4UfP5...)
This comes straight from our Discord server (discord.com/invite/4UfP5...)
Reposted by Ritchie Vink
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?
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?
November 20, 2024 at 11:14 AM
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?
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?