I use Numba to write simple fast loops when vectorized code wouldn’t make much sense, but there’s a lot of stuff I can’t write. Yes, there is active development and much room for improvement (gufuncs can’t be called in jit functions yet, ufuncs don’t specialize as readily as jit), but a fundamental limitation is the need to adhere to Python and NumPy. For example, instead of named structs, Numpy arrays have unnamed structured dtypes (similar to Julia’s eltype). But dtypes aren’t bona fide Python types, so you can’t make an instance of one; you can index a structured array, but the element is an instance of numpy.void no matter the particular structure. Likewise, Numba is incapable of making a vectorized ufunc that dispatches on or returns a structured element, and there doesn’t seem to be any active attempt to make it work somehow (Github issue #5329).
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