[dagw]

Cython works great in conjunction with Numpy arrays and you can easily call numpy and scipy methods from within Cython. The big win comes when you have to do some operation to a numpy array that doesn't have a 'fast' path within numpy. If you ever find yourself in a situation where you have to loop over or apply any sort of custom operation to every element in a numpy array then Cython can be a huge win, especially since Cython also makes it possible to parallelise those loops.

The other place it shines is if you ever need to loop over an array of data that cannot easily be represented as numpy arrays, like strings or more complex structs. Here you can get significant speedups compared to python.

The third use of Cython I really like is with C and C++ interop. Sure there are lots of ways of calling C code from Python, but to me Cython is probably the quickest and cleanest.

Compared to Numba, it's harder to say. Numba, when it works, is easily as fast as Cython. However I find Numba hard to reason about and it's still a bit of a black box as to when and why it does and doesn't work. The nice thing about Cython is that it is pretty simple so you can easily reason about what it will do your code and how it will perform. It's been a long time since Cython 'surprised' me by performing much better or worse than I expected.

If you want to see Cython in action, take a look at the source code of scikit-image or scikit-learn. They implement many of their core algorithms in Cython