Ruby has such a nice holistic consistency to it. With a few exceptions, it feels like it was conceived of by one person with a core idea in mind. Python feels like a mess.
SWIG[0] makes working with C libraries trivial for over a dozen programming languages; Perl, Python, and Ruby included.
And those reasons are?
IMHO, both approaches have merit depending on the situation. I lean towards using SWIG until and unless the situation warrants a hand-written solution due to the boilerplate nature of these types of libraries.
This is just my personal opinion with no data to back it up, but I suspect that Python "won" because it has excellent Windows support, while Ruby doesn't. Even a decade ago, Python's website offered an official native Windows installer [0], while Ruby's website [1] still points you to a third-party installer, which doesn't even have native support since it uses MSYS2 [2].
Most non-developers use Windows, so if you're choosing the first language to teach a large group of people, good Windows support is fairly important. Python being the "default" introductory language gave it a huge number of users, then I suspect that everything flowed down from there.
[0]: https://web.archive.org/web/20160824235759/https://www.pytho...
I think you might be able to go an additional decade backwards. Back in college most of my friends were on windows and one of them was using python for class projects.
Yeah, Python has had good Windows support at least 15 [0] or 25 years [1], depending on how you count it.
> Back in college most of my friends were on windows and one of them was using python for class projects.
Well it's always been possible to install Ruby on Windows too, it's just that Python supports it so much better.
I also wonder how many people gave up on Python just because the installer doesn't put it in your PATH. You'd install Python then no python, wtf. Ok so https://discuss.python.org/t/python-command-not-found/22255 ... Then you fix it and it runs the wrong version of Python.
Ruby's most important error is that it does not support namespaces. This by itself makes it a far less scalable language than Python.
Likewise, it made classes incredibly easy to extend, which led to a monkey patching bonanza and far too much magic everywhere (Rails being by far the worst offender but not the only one). It meant having to keep too much stuff in your head and needing deep framework/library familiarity just to be able to understand basic code.
In the end, I feel like the incredible flexibility was Ruby's undoing, not just lack of library availability for a specific popular application. I was a Ruby zealot at one point but it began to lose its lustre not because it wasn't beautiful in theory, but because it was inconvenient in practice. In some ways, Python's restrictiveness became its greatest attribute. Then Python 3 helped to fix a lot of the inconsistency.
Both Python and Ruby have a consistent logic to them, just not consistent with each other. Just as all attributes are methods in Ruby, so all methods area attributes in Python, etc. Things get much easier in either language if you stop fighting their internal logic.
In an age where people are still standing by C over memory-safe systems programming languages, I feel quite comfortable depending Python for the great many things that Python is good at.
If you've ever read through FORTRAN code from a mathematics department or MATLAB/C/C++ from (non-software) engineering disciplines, then you probably understand why productionizing a jupyter notebook is definitely not the worst of all possible worlds.
That said, I find it the nicest, cleanest option of the three. I still wouldn't use it for large and complex projects. I really like it for stuff where one might otherwise use shellscript. It's way way better than shellscript... except if it's all about files and running external commands.
This is exactly what I remember being said about C (which I agree with) and often given as a reason why higher level languages like Python or Java have so many protections against things C/C++ allowed (memory management being the biggest one of course). Very funny, and I assume not coincidental, to read this about Python in the modern programming landscape.
For little utilities, it’s faster than a lot of alternatives - just start the interpreter, no compilation needed.
It’s all relative, but if you view it as replacing bash scripts for renaming files or running other tools, it’s 100x better.
Have you tried using `uv`'s newer tools? They help a lot e.g. with linting speed, lock management, package dependency separation, correct python version mgmt and no need to fudge with venv.
The fact that Python has become the language of choice for machine learning and data science is not a language issue.
We open sourced the implementation https://github.com/janushendersonassetallocation/loman
import numpy==1.5.4
And it's weird how you import files. They're dot-separated packages that resemble file structure but not exactly. NodeJS has a self-explanatory require("./foo.js") or "../foo.js". The newer JS `import` syntax is annoyingly different from `require` but not terrible.
https://packaging.python.org/en/latest/specifications/inline...
My CLI tools publish from Github to PyPI so that I can run tools with just `uvx sql-agent-cli` or `uvx dlna-here. Nothing for me to handle downloading (directly myself), no environment to manually setup, portable (Linux, Windows, Mac, ARM, x86). Easy for agents to run from a skill.md file without any other prereq than uv.
Really useful library ecosystem to leverage. No more shell scripts, or TS/JS/PHP backend services. I've even used Python on devices I've built around Raspberry Pi Zero 2 boards.
You didn't use Perl before? https://xkcd.com/353/
At least it’s Python/Jupyter and not R, SAS, or MATLAB.
> I often work with data scientists and have to productionize their jupyter notebooks
I'm not a huge Python fan, despite working with it fulltime, but this feels like mixing correlation and causation. Data scientists would not be writing good, optimized code in any language.
> and it’s way too easy to do the wrong thing
is there another programming language where you believe a data scientist is going to have an easier time writing correct code than Python? Do you think C or Rust or JavaScript or C# make it harder to do the wrong thing?
I'm bringing Y2K back next!