I had considered Numba in the past but it just seemed not worth the overhead. A few talks from this year show that they have really expanded the library, to the point where much of the scientific python stack use it instead of Cython. It can target things like ARM devices and is more flexible in the types it can take (dicts!)
For reference
https://www.youtube.com/watch?v=cR8E70GTO8c
and
https://www.youtube.com/watch?v=6oXedk2tGfk
I think it's rather premature to say that the scientific Python stack is adopting Numba. None of the core projects like SciPy, pandas, and scikit-learn have been willing to swap out Cython for Numba. Cython is still dominant and I don't see that changing anytime soon.
Cognitive. Things like having to strip down abstractions and "write it like Fortran". The fact that it can deal with numpy arrays no problem and can actually deal with more common python objects like dicts means that there is less overhead