In a strange way, Python being so bad at interpreting bytecodes and limited by GIL, plus being good at interfacing with C cheaply (unlike Go and Java,) induced a programming style that is extremely suited for data-parallel computing which is the way to efficiently scale compute in today's SIMD/GPU world. If you wanted to be efficient, you had to prepare your data ahead of time and hand it off. Any intermediate interaction with that data would ruin your performance. That's mostly how efficient Python libraries and ecosystem are built.
Weakness may have turned into a strength.