I assume the parent comment was talking about the context of computations where numba is supposed to be a drop-in for wherever numpy is used.
And I agree that it's not actually usable everywhere, since the support for numpy's feature set is actually quite limited, especially around multidimensional arrays. I had to effectively rewrite my logic to make use of numba. Still it is pretty worth it imo, given how it can add parallelism for free. And conforming to numbas allowed subset of numpy usually results in simpler and more efficient code. In my case I ended up having to work around the lack of support for multidimensional arrays but ended up with a more efficient solution relying on low dimensional arrays being broadcasted, reducing a lot of duplicate computations
And I agree that it's not actually usable everywhere, since the support for numpy's feature set is actually quite limited, especially around multidimensional arrays. I had to effectively rewrite my logic to make use of numba. Still it is pretty worth it imo, given how it can add parallelism for free. And conforming to numbas allowed subset of numpy usually results in simpler and more efficient code. In my case I ended up having to work around the lack of support for multidimensional arrays but ended up with a more efficient solution relying on low dimensional arrays being broadcasted, reducing a lot of duplicate computations