I found this, which dives deeper into the impact of inefficient software.

https://eco.kde.org/handbook/#look-to-the-software

Isn’t that only microsoft exclusive and closed source? Also does compiling it really yield the same speed as C, it is garbage collected isn’t it?

I don’t know, I used gnome for a while and I just felt like I was using toy apps. But I think that comes down to personal preference. KDE definitly has the bigger apps like Kdenlive and Krita.

Yes, using them is probably the closest one can get to the macos ecosystem on Linux.

Yeah that point was not entirely accurate. What I meant was, that a np.array and a list don’t work together. Coming from julia and matlab it just does not make sense to me, why I can’t use a function written for a list for a np.array even if they basically represent the exaxt same thing.

Julia for example hast linalg as a module but functions work on lists with no problem.

But I took your advice to heart and installed a Linter

Python is strongly typed, but dynamically checked. Working with other languages I just found, that the type errors in python are the hardest to catch and to debug, but maybe I am just more used to othet languages

I see it’s use as language to write small scripts, I just don’t see much use besides that.

Here is a article talking about the speed of compiled python vs Julia. I don’t see why it is better to go to all these extra steps just to end up with something slower. https://www.matecdev.com/posts/julia-python-numba-cython.html

No I mean, Python is definitely the most used language in scientific computing, but yeah, I would use something else if I could.

Julia, R, Matlab, Mathematica and Fortran.

Yeah, ofc every language must have a type system, the problem is, that this is not enforced. I.e 3 == ‘3’ throws no error, when working with dataframes for example this can be a pain in the ass. But yeah, I don’t say that nobody should use Python (although the title is a bit dramatic) I just think that there are better alternatives out there.