CPython in particular exposes so much detail about its internal implementation that other implementations essentially have to choose between compatibility and performance. Contrast this with, say, JavaScript, which is implemented according to a language standard and which, despite the many issues with the language, is still implemented by three distinct groups, all reasonably performant, yet all by and large compatible.
I don’t mind those, tbh (maybe because I came to Python from C and Lisp).
Contrary, I dislike NumPy’s decision to make reduce/accumulate methods of operations (e.g. np.add.reduce and np.mul.accumulate), not higher order functions or methods of NumPy arrays.
map() and filter() work on any iterable, so it's not clear which type you'd expect them to be methods on (keeping in mind that the notion of "iterable" in Python is expressed through duck typing, so there's no interface like say .NET IEnumerable where they would naturally belong).
len() guarantees that what you get back is an integer. If some broken type implements __len__() poorly by returning something else, len() will throw TypeError. You can argue that this is out of place for Python given it overall loose duck typing behavior, but it can still be convenient to rely on certain fundamental functions always behaving well. Similar reasoning goes for str(), repr(), type() and some others.
One can reasonably argue that Python should have something similar to C# extension methods - i.e. a way to define a function that can be called like a method, but which doesn't actually belong to any particular type - and then all those things would be such functions. But, given the facilities that Python does have, the design of those functions makes sense.
