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I dropped dplyr in favor of data.table and never looked back.
https://github.com/eddelbuettel/gsir-te
I'm guessing Polars and Ballista (https://github.com/ballista-compute/ballista) have different goals, but I don't know enough about either to say what those might be. Does anyone know enough about either to explain the differences?
Not that I am a heavy DataFrame user, but I have felt more at home with the comparatively light-weight TypeTables [1]. My understanding is that the rather complicated DataFrame ecosystem in Julia [2] mostly stems from whether tables should be immutable and/or typed. As far as I am aware there has not been any major push at the compiler level to speed up untyped code yet – although there should be plenty of room for improvements – which I suspect would benefit DataFrames greatly.
[1]: https://github.com/JuliaData/TypedTables.jl
[2]: https://typedtables.juliadata.org/stable/man/table/#datafram...
>Rust DataFrame implementation, built on Apache Arrow
https://github.com/nevi-me/rust-dataframe
A bit less mature/feature-complete than polars last time I looked. Does not seem to do anything with on-disk spillover from what I can see. But if you wanted to use Arrow to do that, nevi-me's crate may be a good place to start.
data.table is basically a highly optimized C library
https://github.com/Rdatatable/data.table