[catblast]

> 100% of Python users find it to be fast enough for their use case.

This is unlikely and an odd thing to say especially in the context of a thread about people rewriting their software in a different stack in part because of python performance issues. There are plenty of people using python that feel the pain and need to spend resources on performance improvements.

The fact very many line of business apps will require more and more complicated hardware resources compared to using "boring shit" like .NET or Java, or Go (which is actually pretty boring, startup hype aside). I'm no huge fan of Java, but I don't feel any less productive in Kotlin than I do python, for many things it is even better. Python aside from a few things is still looks like a 1989 language with a few newer features - the language other than being basically binding lazy doesn't have many amazing tricks up its sleeve. Meanwhile 30 years of progress has been rolled into the mainstream of C#, Swift, and Kotlin.

> To compare speed of CPython with quality of anything is such a narrow view

I'm not saying you can't write quality software with python. What I was saying is the only niche that Python has maybe picked up a significant mindshare compared to alternatives is scientific computing. And I say this with no insult, but as a former grad student in the sciences and having written quite a bit of monstrous python - it's not a field of quality software engineering.

[Demiurge]

I've seen badly written scientific Python code, and I've seen very badly written enterprise Java code. I’ve also written probably more than hundred web apps using Django, that never needed to be written in Java. And Django is great work of software engineering, having not much to do with scientific software.

Some apps need to be re-written for performance reasons, or optimized, that is however, not the most common case.

For all the companies that are complaining about the performance issues, I suspect they are complaining about the change of their incentives or circumstances, unless they made an uninformed choice with choosing Python to begin with.

Which is the likely case?

[Demiurge]

The missing piece of the discussion here seems to be socio-political (human) aspect of computer language usage. When choosing a "slow" language, many other factors beside performance and ecosystem are considered. The primary usage of a computer language is actually communicating with the fellow human beings, and there is a huge cost and overhead associated with all the software writing and interpretation. That is why it baffles me when Python is dismissed entirely on a pure performance basis.

[cycomanic]

I would wager that in a large percentage of cases you would see the same or larger gains as you see from going to different languages you would see by going for better algorithms. You example of scientific computing (and point of software engineering is well taken) is a good one, because in scientific computing people tend to research and use the fastest algorithms and once you do python (+numpy) is often fast enough.

[pas]

Using numpy is fast, because it just uses the forever-optimized stuff bundled with it, and uses not much Python. But of course offers a handy interface.

Python suffers from the same problems as C in this regard. It's very powerful, easy to get going, because it doesn't force you into some "better" paradigm/architecture/ideology/thinking. (Like let's say mypy or Rust do.)

And that's okay. 80+% of Python scripts/programs are fine without that rigor, there are more important problems to worry about. (Like making the company profitable sooner instead of spending plus a few more months on figuring out the types/bindings for mypy/Rust.)