[fbdab103]

Today, there is a Python package for everything. The ecosystem is possibly best in class for having a library available that will do X. You cannot separate the language from the ecosystem. Being better, faster, and stronger means little if I have to write all of my own supporting libraries.

Also, few scientific programmers have any notion of what C or Fortran is under the hood. Most are happy to stand on the shoulders of giants and do work with their specialized datasets. Which for the vast majority of researchers are not big data. If the one-time calculation takes 12 seconds instead of 0.1 seconds is not a problem worth optimizing.

[koito17]

>Today, there is a Python package for everything.

The same could be said about CPAN and NPM. Yet Perl is basically dead and JavaScript isn't used for any machine learning tasks as far as I'm aware. WebAssembly did help bring a niche array of audio and video codecs to the ecosystem[1][2], something I'm yet to see from Python.

I don't use Python, but with what little exposure I've had to it at work, its overall sluggish performance and need to set up a dozen virtualenvs -- only to dockerize everything in cursed ways when deploying -- makes me wonder how or why people bother with it at all beyond some 5-line script. Then again, Perl used to be THE glue language in the past and mod_perl was as big as FastAPI, and Perl users would also point out how CPAN was unparalleled in breadth and depth. I wonder if Python will follow a similar fate as Perl. One can hope :-)

[1] https://github.com/phoboslab/jsmpeg

[2] https://github.com/brion/ogv.js/

[junipertea]

That’s a lot of opinions for so little exposure. There are a lot uses that don’t involve docker or a dozen virtual envs.

[chank]

Honestly, I use python everyday in the ML/AI space. If we're talking in that context they're pretty spot on about python, virtualenvs, and docker.

[insanitybit]

I've used Python quite a lot and their experience sounds about right.

[pnt12]

To counter these anecdotes, I used python for building web apis and only needed poetry to manage 1 virtual env, and containerizing with docker was straight forward.

[brrrrrm]

> JavaScript isn't used for any machine learning tasks as far as I'm aware

https://github.com/facebookresearch/shumai

[wizzwizz4]

> WebAssembly did help bring a niche array of audio and video codecs to the ecosystem

Python already has all those: the ctypes module is just as hard to use as WebAssembly, with a much lower barrier-to-entry.

[Yoric]

WebAssembly has the benefit of portability, though. Python on Windows... is still an open problem. Or even Python on dev-oriented distro vs. numsci-oriented distro.

[el_oni]

This is how I got into software development.

During my PhD I was running some simulations using poorly written python code. initially it would take several hours. In that time i could go to the lab, run some wetlab experiments and the results of my simulations would be there when i got back to the office. It was only taking python "home" and building some of my own projects that i learned how to 1. write more pythonic code and 2. write more performant code. Now i work for a software company.

If i'd have stayed in in academia I would probably still be writing quick and dirty code and not worrying about the runtime because as a researcher there is always something else you can be doing.

[onetonfoot]

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

[Terretta]

* NodeCall.jl - https://github.com/sunoru/NodeCall.jl

// just fixed missing 'l' in link