[IanOzsvald]

Really Numba will speed up numpy and some scipy (there's partial API coverage) and math based pure python. I think it is unlikely it'd be used away from math problems. As another commenter mentioned it can be used to accelerate numpy-array based Pandas (but not the newer Arrow based arrays), and again that's for numeric work.

[a-dub]

numpy and scipy are largely just wrappers around native libraries that are already optimized machine code. numba is good for writing loops or doing other things you can pretty much imagine as simple C code.

while it is pretty cool, it's also a bit awkward thinking about machine structures and machine types in high level python. there are some gotchas with respect to the automatic type inference.

[IanOzsvald]

Don't forget that a sequence of numpy operations will likely each allocate their own temporary memory. Numba can often fuse these together, so although the implementation behind numpy is compiled C you end up with fewer memory allocations and less memory pressure, so you still get your results even faster. Numba also offers the OpenMP parallel tools too. I have a nice sequence of simulations in my Higher Performance Python course going from raw python through numpy then to Numba showing how this all comes together. Just try having a function with a=np.fn1(x); b=np.fn2(a); c=np.fn3(b) etc and compile it with @jit and you should get a performance impact. Maybe you can also turn on the OpenMP parallelizer too.