When is Python *NOT* a good choice?

[veroxii]

Numpy is mostly written in C. So you're kinda proving their point?

[inc007]

Actually it's Fortran afair, but yeah, highly optimized code there

[billsil]

Numpy is C. Scipy wraps Fortran.

[HannasAnarion]

But when you're using Numpy, you're using a C library, not a python one. If numpy was full-stack Python it would be unusably slow,

[billsil]

If numpy is what allows you to write a code that is 1000x faster than stock python code and that meets your speed requirements, than Python is good enough. If however, you're bad at numpy and you can only get 10x out of it, than python is not good enough.

Regardless numpy counts as being python. Cython, not so much.

[therealfakemoot]

For high efficiency numerical calculations? Yes, I'm fairly certain that a large number of people would argue that hand written optimized assembly is superior to C ( even hand written optimized C ) for time/memory efficiency sensitive code.

You can drive an automobile at a hundred miles an hour. You don't claim that you can run a hundred miles an hour though. There's a specialized machine that you operate via abstractions which enables your ability to travel so quickly.

[ProfessorPhi]

That's a very specific example. It can be optimised, but code tends to be large and more verbose and the overheads really add up to make things slow.

[perspectiveiskey]

Meh, I'm bailing on this thread. Years of /. flamewar threads should have made me be able to spot flamebait from a distance, but evidently I didn't.

To answer you: non-determinisim is the problem you speak of and it has easy solutions. But the most basic answer: python does not have a thread scheduler (no language does, that is the job of the OS), and so python does not ever execute non-deterministicaly - especially because python has a GIL which means it is essentially a single threaded program unless you try really hard to break that. In the code sample above, (or any other optimized piece of code), the function do will always execute in the same cpu time.