I don't think that's true. You lose some functionality, but since you can call Numba on select critical methods, it's not so bad. You can often sequester your performance-critical logic into some Numba-decorated methods, and your business elements that call out to the rest of your ecosystem are not decorated with Numba.

Or to put it another way-- if I'm using vectorized code in Numpy I can't deal with the external ecosystem from within my vectorized code either.

> The code is fast, but you lose the ability to organize your data the way you would in regular python,

Can you clarify what you mean by this? I lose the ability to organize my data the way I'd like if I'm vectorizing my code too.

Not true at all for me, because in my situation there are some functions which have tight loops that take 99% or more of the execution time. Put the numba decorator in front of them and the code is 100+X faster. Clearly it's not that simple for everyone. But for some people, it can be. So it doesn't make sense to call it a panacea, and it also doesn't make sense to say it doesn't work. It works for some people, not for others.