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Nilshg

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Nilshg
·3 года назад·discuss
If possible I would encourage you to make an MWE and post it on the Julia Discourse - "Julia slower than R for optimization problem" or something like that, there's a good chance that the community will be able to eke out some more performance. Alternatively you might have hit on a case in which Julia itself is currently leaving performance on the table, which would still be helpful for the community to know as being slow is often considered a bug in Julia world.
Nilshg
·3 года назад·discuss
> Indeed DataFrames.jl isn't and won't be the fastest way to do many things

Agreed, and the DF.jl developers are aware and very open about this fact - the core design trades off flexibility and user friendliness over speed (while of course trying to be as performant as possible within those constraints).

One thing that hasn't been mentioned so far is InMemoryDatasets.jl, which as far as I know is the closest to polars in Julia-land in that it chooses a different point on the flexibility-performance curve more towards the performance end. It's not very widely used as far as I can tell but could be interesting for users who need more performance than DF.jl can deliver - some benchmarks from early versions suggested performance is on par with polars: https://discourse.julialang.org/t/ann-a-new-lightning-fast-p...
Nilshg
·3 года назад·discuss
To be clear on this: DataFrames, like most of the Julia ecosystem, follows SemVer. DataFrames 1.0 was released over two years ago (March 2021), and the API has been stable ever since.

Furthermore, Bogumil Kaminski, one of the main developers behind DataFrames, makes sure that the DataFrames tutorials he has created here (https://github.com/bkamins/Julia-DataFrames-Tutorial) are updated on every new release.