[targafarian]

Claims about Numba by the author seem a little unkind if not wrong to me.

Numba can handle vectorized (numpy behavior) directly in addition to explicit loops. The former is accelerated less in comparison to plain python calling numpy (since if you can use numpy operations directly, it's already really fast) but the numpy bits in Numba can also be automatically parallelized by Numba. Explicit loops in numba are accelerated hundreds of times over Python loops (and you can use e.g. prange to write parallel loops, too). Point is, the two paradigms can be mixed and matched at will within Numba.

It seems like every example is of cython, but then the author generalizes the conclusions to Numba as well. It would be much more "honest" to show side-by-side comparison of Numba, Cython, and Pythran, since these all have different syntaxes and are fairly different tools.

Another example is that you don't have to rewrite functions for different argument types in Numba, but you do in Cython (see "convolve_laplacian" example, which can work with a simple decorator as a numba function). There again, the impression is given that Numba suffers from the same issue as Cython (and as mentioned elsewhere in the comments here, it's possible that Cython has a way around this, but I don't know the details).