[mumblemumble]

I think that the nuance here was perfectly obvious, if not explicit, to everyone who wasn't busy burying it under a pile of whataboutism.

I would also say that I wouldn't be at all surprised if idiomatic Python is actually quite a bit faster than idiomatic Haskell in some interesting use cases. Which does not at all mean that the opposite is true. There are always interesting cases that make good fodder for "what about" comments, but getting carried away with them doesn't really make for particularly edifying discussion. Just mentally append "in my experience" to everyone's comments and move on.

[lmm]

> I would also say that I wouldn't be at all surprised if idiomatic Python is actually quite a bit faster than idiomatic Haskell in some interesting use cases. Which does not at all mean that the opposite is true.

I'd be utterly amazed. Haskell is orders of magnitude faster than Python for "typical" code (having to do a hash lookup for every method invocation ends up being really costly). It's not a slow language by any reasonable definition. And the fact that you're suggesting it is suggests that the nuances are not at all obvious.

(Not trying to hate on Python - performance is a lot less important than most people think it is - just trying to put Haskell's performance characteristics in a familiar context)

[mumblemumble]

It's not that much of a nuance, TBH. It's just that Python has largely become a high-level language for gluing together libraries that are mostly written in much faster AOT-compiled languages such as C, C++, Fortran, or even Cython. For example, I prefer to stick with Python for a lot of the number crunching things that I do because, while you certainly can beat numpy's performance in other languages, in practice it turns out that doing so is generally more work than it's worth. Especially if you're using the Intel distribution of numpy.

So, yeah, it's true, you do a hash lookup for every Python method invocation, and also you've got to worry about dynamic type checks for all the dynamically typed references. But the practical density of method invocations and dynamic type checks can be surprisingly low for a lot of Python's more interesting use cases.