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.
I wasn't comparing numba to numpy. I was comparing to python code where you don't care about performance. The main reason I don't find numba appealing is that Julia gives you numba like performance while allowing you to use structs (think classes) to organize stuff.
This doesn't really make sense. If you don't care about performance then you have no use for Numba.
Also, the comment you replied to was explicitly comparing Numba to vectorized Python, so you should not abandon that comparison in your reply without saying so.
I think the point is that one wants to write code that is similar to regular non-performance sensitive python code, with classes and everything, and still have it be fast.
> Also, the comment you replied to was explicitly comparing Numba to vectorized Python, so you should not abandon that comparison in your reply without saying so.
The comment you replied to explicitly says "regular python".
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.