[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.