Tidier.jl
Clang.jl
Tidier.jl | Clang.jl | |
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5 | 2 | |
492 | 216 | |
4.7% | 0.0% | |
8.5 | 8.3 | |
7 days ago | 10 days ago | |
Julia | Julia | |
MIT License | MIT License |
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Tidier.jl
- Tidier.jl: Meta-package for data analysis in Julia, modeled after R tidyverse
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Julia 1.10 Released
btw, there has been a pretty nice effort of reimplementing the tidyverse in julia with https://github.com/TidierOrg/Tidier.jl and it seems to be quite nice to work with, if you were missing that from R at least
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Pandas vs. Julia – cheat sheet and comparison
Indeed DataFrames.jl isn't and won't be the fastest way to do many things. It makes a lot of trade offs in performance for flexibility. The columns of the dataframe can be any indexable array, so while most examples use 64-bit floating point numbers, strings, and categorical arrays, the nice thing about DataFrames.jl is that using arbitrary precision floats, pointers to binaries, etc. are all fine inside of a DataFrame without any modification. This is compared to things like the Pandas allowed datatypes (https://pbpython.com/pandas_dtypes.html). I'm quite impressed by the DataFrames.jl developers given how they've kept it dynamic yet seem to have achieved pretty good performance. Most of it is smart use of function barriers to avoid the dynamism in the core algorithms. But from that knowledge it's very clear that systems should be able to exist that outperform it even with the same algorithms, in some cases just by tens of nanoseconds but in theory that bump is always there.
In the Julia world the one which optimizes to be fully non-dynamic is TypedTables (https://github.com/JuliaData/TypedTables.jl) where all column types are known at compile time, removing the dynamic dispatch overhead. But in Julia the minor performance gain of using TypedTables vs the major flexibility loss is the reason why you pretty much never hear about it. Probably not even worth mentioning but it's a fun tidbit.
> For what it's worth, data.table is my favourite to use and I believe it has the nicest ergonomics of the three I spoke about.
I would be interested to hear what about the ergonomics of data.table you find useful. if there are some ideas that would be helpful for DataFrames.jl to learn from data.table directly I'd be happy to share it with the devs. Generally when I hear about R people talk about tidyverse. Tidier (https://github.com/TidierOrg/Tidier.jl) is making some big strides in bringing a tidy syntax to Julia and I hear that it has had some rapid adoption and happy users, so there are some ongoing efforts to use the learnings of R API's but I'm not sure if someone is looking directly at the data.table parts.
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Tidyverse 2.0.0
“Tidier.jl is a 100% Julia implementation of the R tidyverse mini-language in Julia.”
https://github.com/TidierOrg/Tidier.jl
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What's Julia's biggest weakness?
A recent package, Tidier.jl, is coming from a R package developer: https://github.com/kdpsingh/Tidier.jl
Clang.jl
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Julia 1.10 Released
Are there solid C interfaces that can be used?
A large part of why I started using Julia is because calling into other languages through the C FFI is pretty easy and efficient. Most of the wrappers are a single line. If there is not existing driver support, I would pass the C headers through Clang.jl, which automatically wraps the C API in a C header.
https://github.com/JuliaInterop/Clang.jl
I most recently did this with libtiff. Here is the Clang.jl code to generate the bindings. It's less than 30 lines of sterotypical code.
https://github.com/mkitti/LibTIFF.jl/tree/main/gen
The generated bindings with a few tweaks is here:
https://github.com/mkitti/LibTIFF.jl/blob/main/src/LibTIFF.j...
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A new C++ <-> Julia Wrapper: jluna
If you are interested in C++ interop you can also have a look at Clang.jl and CxxWrap.jl (the usual Julia package chaos applies, where the package mentioned in old talks and docs that you find on google is superseded by some others...)
What are some alternatives?
Julia-DataFrames-Tutorial - A tutorial on Julia DataFrames package
CxxWrap.jl - Package to make C++ libraries available in Julia
tidytable - Tidy interface to 'data.table'
jluna - Julia Wrapper for C++ with Focus on Safety, Elegance, and Ease of Use
py-shiny - Shiny for Python
Lux.jl - Explicitly Parameterized Neural Networks in Julia
DataFramesMeta.jl - Metaprogramming tools for DataFrames
Torch.jl - Sensible extensions for exposing torch in Julia.
julia - The Julia Programming Language
LibTIFF.jl - Clang.jl generated wrapper around Libtiff_jll.jl
db-benchmark - reproducible benchmark of database-like ops
threads - Threads for Lua and LuaJIT. Transparent exchange of data between threads is allowed thanks to torch serialization.