Polars: Blazingly Fast DataFrames in Rust and Python
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- čas přidán 29. 08. 2024
- This talk will introduce Polars a blazingly fast DataFrame library written in Rust on top of Apache Arrow. Its a DataFrame library that brings exploratory data analysis closer to the lessons learned in database research.
CPU's today's come with many cores and with their superscalar designs and SIMD registers allow for even more parallelism. Polars is written from the ground up to fully utilize the CPU's of this generation.
Besides blazingly fast algorithms, cache efficient memory layout and multi-threading, it consist of a lazy query engine, allowing Polars to do several optimizations that may improve query time and memory usage.
Read more:
github.com/pol... www.ritchievin...
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Great video. I have been playing with Polars, Python, and Rust, and it is a fantastic technology.
Also, I think Polars' Python facade with Rust backend / clean API is a great model for other data-centric technologies that need performance and scripting facade interfaces.
Big thanks to the Polars team for this gem.
Learn rust, python and rust synergies will be all over the place in data science soon. Great work with Polars
Why do you think that? Can you elaborate more please? Thanks!
Can you please provide some insight? I am thinking of learning rust and I wish to enter this field.
Polars is just great! After trying it I won't go back to pandas.
Haven't been so excited about data analysis for a while. Now comes the chore of converting pandas to polars in a 15k line project.
For a lot of my job its not possible to use eagerly evaluated DFs, polars is a fantastic option.
Really great crate and great video, thanks.
I'm going to try this out. Thanks
I wonder if this could ever be used with multiple machines similar to spark. Polars looks very fast and with the addition of distributed compute it would be very powerful. Perhaps it was not designed to allow that though.
Polars uses all logical cores of your machine in parallel. Doesn't that mean distributed compute?
@@user-qm6up7kz4n I am asking specifically about cross machine distribution. For example if we had multiple 8-core machines (like VMS in aws) available, can we distribute polars work across those vms instead of being limited to a single machine and its cores.
@@milo4433 aah I see the difference now
@@milo4433 yea you totally could but would need to do that yourself, not sure the crate supports it out of the box
Wouldn't be a load balancer be enough to make it happen?
great API
Nice presentation
This is great, can we use polars on a databricks cluster?
Sure, it terms of python, its just another library so can use it very conveniently!!!
Does polars with with scikit?
I think they work most of the time. The good think in that if something fails, you can pass polars to numpy very easily
Super confused. I thought Polars was deprecating in favor of Arrow2?
need pandas compatible api, just get fast conversion from pandas
It has methods to read and export to Pandas out of the books. It can also write or read Delta tables (locally or in S3).