One reason is its just super easy for input output operations. ML is all about data and getting the data to the right place is really easy in python compared to some other languages..
Python is OOP; but the "classical" data-centric languages are actually all more or less in the FP space. (I count array languages and APL-likes to FP in this case).
Just an example: You don't have immutable data types by default in Python. This is actually a pretty bad default for data processing tasks.
You now say it's like that because Python has already quite some libs / frameworks in that direction.
This looks like circular reasoning.
Also the the "prototypical ML dude" comes form the math department. People with math background have a much easier time to grasp FP than procedural programming. FP is much more "natural" when you're used to math.
(Procedural programming says things like `x = x + 1`, but even my grandma would know that "this has no solution", or is likewise "plain wrong" ;-))