DUdunefox·Dec 25, 2020
Can recommend, it's pretty elegant, fun, and efficient. Also: https://github.com/JuliaPy/PyCall.jl "This package provides the ability to directly call and fully interoperate with Python from the Julia language."
In Ruby 3.0JuliaPy
PyCall.jl
Julia2013–2023
JuliaPy / PyCall.jl
PyCall.jl
Package to call Python functions from the Julia language
32Distinct people
54Mentions
0Explicit recommenders
1.5KGitHub stars
What to know before exploring
What it is
Package to call Python functions from the Julia language
GitHub
Where it is used
No documented product use was verified in the last source check.
What people are saying
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Also in these discussions
- juliainterop/rcall.jl · linked in 7 of the same discussions
- juliapy/pyplot.jl · linked in 3 of the same discussions
- juliainterop/cxx.jl · linked in 2 of the same discussions
Mentioned in the same Hacker News threads. That is not proof they are substitutes.
This week on Hacker News
Nov 8–Nov 15 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/15/2026, 2:59:58 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by year54 dated mentions
Observed mentions: 2013-04-02 07:32:02 UTC to 2023-05-08 19:51:51 UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| 2013 | 2013-01-01 00:00:00 UTC | 2014-01-01 00:00:00 UTC | 7 | 1 | 6 |
| 2014 | 2014-01-01 00:00:00 UTC | 2015-01-01 00:00:00 UTC | 7 | 0 | 7 |
| 2015 | 2015-01-01 00:00:00 UTC | 2016-01-01 00:00:00 UTC | 3 | 0 | 3 |
| 2016 | 2016-01-01 00:00:00 UTC | 2017-01-01 00:00:00 UTC | 0 | 0 | 0 |
| 2017 | 2017-01-01 00:00:00 UTC | 2018-01-01 00:00:00 UTC | 2 | 0 | 2 |
| 2018 | 2018-01-01 00:00:00 UTC | 2019-01-01 00:00:00 UTC | 5 | 1 | 4 |
| 2019 | 2019-01-01 00:00:00 UTC | 2020-01-01 00:00:00 UTC | 4 | 0 | 4 |
| 2020 | 2020-01-01 00:00:00 UTC | 2021-01-01 00:00:00 UTC | 8 | 0 | 8 |
| 2021 | 2021-01-01 00:00:00 UTC | 2022-01-01 00:00:00 UTC | 15 | 1 | 14 |
| 2022 | 2022-01-01 00:00:00 UTC | 2023-01-01 00:00:00 UTC | 1 | 0 | 1 |
| 2023 | 2023-01-01 00:00:00 UTC | 2024-01-01 00:00:00 UTC | 2 | 0 | 2 |
What Hacker News says
8 sourcesFor many blindspots you can use PyCall (https://github.com/JuliaPy/PyCall.jl).
In Between two Lisps> my conclusion is that Julia is a superior alternative to Python in all ways except one: ecosystem.
Fully agree, but with PyCall you can just use any Python package in Julia: https://github.com/JuliaPy/PyCall.jl
I do that regularly when I need something from Python and it works perfectly. So, Julias ecosystem is really a superset of Pythons when it comes to libraries.
In Julia: Dynamism and Performance Reconciled by Design (2018)Julia has an excellent interop story with many languages. Probably C, Fortran and Python are the most polished.
Check out https://github.com/JuliaPy/PyCall.jl
In Pytov – C-like syntax PythonI think this post: https://julialang.org/blog/2012/02/why-we-created-julia/ presents the aims of the Julia project at a high level quite well. That post was partly aspirational at the time, and partly already achieved.
Now, 8 years later, I'd say essentially everything there has been achieved. The most important difference between Julia now and Julia then is the gigantic, vibrant and interacting ecosystem that's sprung up (though share are other important technical differences too!)
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Here's the pitch I'd give:
Julia's value proposition is mostly geared towards people trying to do things that are just too difficult, awkward or slow in other languages. It's not just about speed, dynamism and friendly syntax. It's also about solving the expression problem and providing unprecedented composabilit
Read the rest
y.
So while it may not necessarily have great appeal to 'end users' (who are people just calling numpy/scipy/pandas/whatever functions the way they were meant to be called by the library writers), it does appeal to people who make the sort of things end users want so the community is currently swelling with very talented people making state of the art research libraries. The language is designed for 'composability', so that it's very easy to combine packages together in ways the authors didn't necessarily intend or forsee.
This approach is really starting to bear fruit. Our package ecosystem has some real, state of the art stuff not easily available in other languages, and it's often built in less time and with less resources. For 'end users', whether or not Julia makes sense for them really just depends on if the things they need are already available.
Another huge advantage is that even if you're not some super researcher developer, Julia substantially lowers the bar for mere mortals such as myself to do package development. The composability I mentioned above means that I can stand on the shoulders of giants as I implement my own stuff, plus the fact that the majority of libraries are written in pure julia means that I can easily "peek under the hood" to figure out how they work and learn from them. This is really special and can't be replicated in python, because 'peeking under the hood' invariably means reading very specialized C code, rather than Python code.
I think Julia will someday eat Python for scientific computing, but it may be a while before that becomes obvious to Python users. In the meantime, we have an excellent package for calling and running Python code from withing Julia, called PyCall.jl [1], so that can make the burden of switching even easier.
[1] https://github.com/JuliaPy/PyCall.jl
There's no need to rewrite anything: https://github.com/JuliaPy/PyCall.jl
But yeah, I won't start any new projects in Python.
In Deep learning to translate between programming languagesGood thing you don't have to: https://github.com/JuliaPy/PyCall.jl
In Deep learning to translate between programming languagesThe main package for calling Python is PyCall [1]. It uses Conda by default but you can specify your own python location.
[1] https://github.com/JuliaPy/PyCall.jl
In Coconut: Simple, elegant, Pythonic functional programming