I actually find numpy to be fairly consistent and intuitive. Usually the code ends up being faster and more concise. I love the masking ability of the ndarrays as well as the support for broadcasting operations.

Yeah, I'm scratching my head over that comment as well.

Any slightly complicated indexing of array in numpy is a pain. I've spent hours reading the numpy documentation for indexing. After that project I've completely forgotten how to do indexing in numpy. I expect I'll spend more hours reading the same documentation if I ever go back to numpy. Compared to Matlab, it is ridiculously complicated.

I don't see how this doesn't lead to a LOT of silently broken code ...

In [3]: x = np.array([1,2,3])

In [4]: x[0:500] Out[4]: array([1, 2, 3])

Maybe "abominable" is an exaggeration...

However, after translating some exercices from Octave to python/numpy, I have experienced a great deal of frustration. A simple three-line solution in Octave becomes a fifteen-line behemoth that does not even work consistently across different versions of the system.

Can you give a specific example?