I do think there’s more nuance to the matter for scientific software since choices are consistent even under perturbations of “what management will accept”. The nuance typically comes from how scientists are first exposed to programming at all, especially late in their career. It’s often a survey course like this so they can catch up and be productive members of their lab. And a course needs to fit in a semester, so any language that’s not Python, R, or MATLAB will be wholly inadequate for that purpose.

More than that even... I spent 4 semesters learning scientific computing in FORTRAN in school. As soon as I graduated, my go to language for prototyping stuff and frankly 99% of projects became and is still python (or c++ for microcontrollers).

The reasons are simple:

1. Lots more help and examples available online for python compared to FORTRAN.

2. Python is performant enough, 99% of the time if you make even a vague attempt to appropriately vectorize your code and/or use the right libraries and techniques.

3. Solving a well defined problem in any language is easy enough. What's hard is getting to that well defined problem, and that often involves playing and tweaking with things until you wrap your head around the problem space. Python saves me a lot of time so I can iterate faster by avoiding stupid errors and having much lower boilerplate overhead where stupid errors can really propagate into hard to follow error messages down the line.

Python just is a lot more intuitive. I don't have to waste nearly as much time on off-by-one and other stupid errors because my indexing was fucked up. So I can spend most my time on thinking about the stuff that really matters rather than implementation details.

That said, I can write some lean and mean FORTRAN if I really need to, and I'm ok with c++ when I need to be too. In reality though, most of my workload isn't that computationally intensive, and even when it is, most of the hard parts have been outsourced to c++ behind the scenes anyway. I can't even remember the last time when my computational need was bad enough that I considered writing my own fortran.

Microcontrollers are a different story. Trying to use python for that seems like a dumb idea, but I know MicroPython is a thing, though I'm skeptical of the whole concept to be honest. You're so close to the metal at that point that why would you want to abstract it away?

There are cases in ML where python isn't performant enough, so much so, that the state of the art for a while converged on how to circumvent Python. This happens when the model interacts with the outside world (environment) a lot or when you are limited to non vectorizable environments. Python simply adds too much friction to the mix.

It may vary from one discipline to the next. My background is in physics. Every physics student learned to program. In grad school, every physics thesis involved programming. Most of the scientists I know who use Python, switched from some other language, and learned it on the fly.

Looking back over the span of 4 decades, one big change is that there is just more stuff, and the specific programming language is just a small part of it. An advantage of learning something like Python is that you can do more with it, quickly, thanks to the libraries.

Well sure, I accept "I use this because that's what the helpful postdoc knows" as spiritually falling under "I already knew it" heading.