gopy
RCall.jl
gopy | RCall.jl | |
---|---|---|
5 | 8 | |
1,868 | 311 | |
1.1% | 0.6% | |
6.7 | 5.5 | |
3 days ago | 29 days ago | |
Go | Julia | |
BSD 3-clause "New" or "Revised" License | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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gopy
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Making Python 100x faster with less than 100 lines of Rust
I've used gopy[0] recently to access a go library in Python. It surprisingly Just Worked, but I was disappointed by some performance issues, like converting lists to slices.
[0] https://github.com/go-python/gopy
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Golang vs python for AI
the heavy lifting is done in native libraries and you get to experiment fast using an easy language. the combo is quite hard to beat. Now there is a missed opportunity to write such libraries in Go, but as I read here and there Go is hard to integrate well as a library. There is gopy but it's light years away from PyO3 for instance, I don't think it'll ever gain traction, but who knows.
- Is the statement true, that Python and its ecosystem lacks speed for mission-critical large-scale applications?
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I went about learning Rust
> So if you learn Go, you'll never be able to use it to interoperate with e.g. your Python program to speed it up.
Never done it myself, but:
https://www.ardanlabs.com/blog/2020/07/extending-python-with...
https://github.com/go-python/gopy
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Rust or C/C++ to learn as a secondary language?
Check out gopy for an easy way to extend your Python code with Go.
RCall.jl
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Makie, a modern and fast plotting library for Julia
I don't use it personally, but RCall.jl[1] is the main R interop package in Julia. You could call libraries that have no equivalent in Julia using that and write your own analyses in Julia instead.
[1] https://github.com/JuliaInterop/RCall.jl
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Making Python 100x faster with less than 100 lines of Rust
You can have your cake and eat it with the likes of
* PythonCall.jl - https://github.com/cjdoris/PythonCall.jl
* NodeCall.jl - https://github.com/sunoru/NodeCall.j
* RCall.jl - https://github.com/JuliaInterop/RCall.jl
I tend to use Julia for most things and then just dip into another language’s ecosystem if I can’t find something to do the job and it’s too complex to build myself
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Interoperability in Julia
To inter-operate Julia with the R language, the RCall package is used. Run the following commands on the Julia REPL
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Convert Random Forest from Julia to R
https://github.com/JuliaInterop/RCall.jl may help
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I'm considering Rust, Go, or Julia for my next language and I'd like to hear your thoughts on these
If you need to bindings to your existing R packages then Julia is the way. Check out RCall.jl
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translate R code to Julia code
I have no experience with R, but maybe this will be of use: https://github.com/JuliaInterop/RCall.jl
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Julia 1.6: what has changed since Julia 1.0?
You can use RCall to use R from Julia: https://github.com/JuliaInterop/RCall.jl
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Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
I worked with R and Python during the last 3 years but learning and dabbling with Julia since 0.6. Since the availability of [PyCall.jl] and [RCall.jl], the transition to Julia can already be easier for Python/R users.
I agree that most of the time data wrangling is super confortable in R due to the syntax flexibility exploited by the big packages (tidyverse/data.table/etc). At the same time, Julia and R share a bigger heritage from Lisp influence that with Python, because R is also a Lisp-ish language (see [Advanced R, Metaprogramming]). My main grip from the R ecosystem is not that most of the perfomance sensitive packages are written in C/C++/Fortran but are written so deeply interconnect with the R environment that porting them to Julia that provide also an easy and good interface to C/C++/Fortran (and more see [Julia Interop] repo) seems impossible for some of them.
I also think that Julia reach to broader scientific programming public than R, where it overlaps with Python sometimes but provides the Matlab/Octave public with an better alternative. I don't expected to see all the habits from those communities merge into Julia ecosystem. On the other side, I think that Julia bigger reach will avoid to fall into the "base" vs "tidyverse" vs "something else in-between" that R is now.
[PyCall.jl]: https://github.com/JuliaPy/PyCall.jl
[RCall.jl]: https://github.com/JuliaInterop/RCall.jl
[Julia Interop]: https://github.com/JuliaInterop
[Advanced R, Metaprogramming] by Hadley Wickham: https://adv-r.hadley.nz/metaprogramming.html
What are some alternatives?
PySCIPOpt - Python interface for the SCIP Optimization Suite
Makie.jl - Interactive data visualizations and plotting in Julia
Pulumi - Pulumi - Infrastructure as Code in any programming language. Build infrastructure intuitively on any cloud using familiar languages 🚀
org-mode - This is a MIRROR only, do not send PR.
prisma-engines - 🚂 Engine components of Prisma ORM
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
poly-match - Source for the "Making Python 100x faster with less than 100 lines of Rust" blog post
Revise.jl - Automatically update function definitions in a running Julia session
cpy3 - Go bindings to the CPython-3 API
cmssw - CMS Offline Software
PythonCall.jl - Python and Julia in harmony.
PyCall.jl - Package to call Python functions from the Julia language