PyCall.jl
PaddedViews.jl
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PyCall.jl | PaddedViews.jl | |
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28 | 2 | |
1,437 | 45 | |
1.1% | - | |
6.1 | 3.8 | |
about 1 month ago | 12 days ago | |
Julia | Julia | |
MIT 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.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
PyCall.jl
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I just started into Julia for ML
For point 3 you can use https://github.com/cjdoris/PythonCall.jl or https://github.com/JuliaPy/PyCall.jl (and their respective Python sister packages).
- The Mojo Programming Language: A Python Superset Drawing from Rust's Strengths
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Calling Chapel, Carbon, and zig code in Julia
PyCall.jl is really handy. Are there any similar projects for calling Chapel code, or Carbon/zig?
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Am I dumb in thinking I can use Rust as a Fast Python and leave it at that?
Julia and Python interop should not be a problem at all. Actually Julia has one of the best interops I’ve ever seen, so much that swift copied it. https://github.com/JuliaPy/PyCall.jl
- Which tools do you use for python + Data Science?
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I don't want to abandon Rust for Julia
One small note, julia also has great python interop via PyCall.jl
- Faster Python calculations with Numba: 2 lines of code, 13× speed-up
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Interoperability in Julia
It is possible to call Python from Julia using PyCall. Then to install PyCall, run the command in the Julia REPL.
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Why is Python so used in the machine learning?
That said, you can run python modules in Julia. So you can just export your code as a module and then use it in Julia via the PyCall package. short description here github here <— you’d just add the pacakge via the really nice package manager built into julia, but for link for more detailed documentation
- Use rust code in Python with pyo3
PaddedViews.jl
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Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
As sibling posts have pointed out, you can in fact do all of those things:
1. You can write a getproperty method for a tuple. It is considered to be type piracy and thus runs the risk of colliding with someone else's definition, but the language absolutely lets you do it.
2. You can broadcast over the fields of a NamedTuple by defining appropriate methods. Again, it's type piracy, so take that into consideration but the language lets you do this easily.
3. The https://github.com/JuliaArrays/PaddedViews.jl package implements exactly what you're saying Julia won't let you do.
If anything, Julia errs on the side of allowing you to do too many things! There are very few things the language says really won't let you do.
What are some alternatives?
py2many - Transpiler of Python to many other languages
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.
julia - The Julia Programming Language
Dash.jl - Dash for Julia - A Julia interface to the Dash ecosystem for creating analytic web applications in Julia. No JavaScript required.
Revise.jl - Automatically update function definitions in a running Julia session
Genie.jl - 🧞The highly productive Julia web framework
StatsPlots.jl - Statistical plotting recipes for Plots.jl
fast-ruby - :dash: Writing Fast Ruby :heart_eyes: -- Collect Common Ruby idioms.
org-mode - This is a MIRROR only, do not send PR.
are-we-fast-yet - Are We Fast Yet? Comparing Language Implementations with Objects, Closures, and Arrays