Accelerating Python with the Numba JIT Compiler | SciPy 2015 | Stanley Seibert 

You guys are doing a fantastic job with numba. Looking forward to numba becoming a part of cpython. Is that even possible?

I tried it on some code I found on github for processing video. It took 4 minutes per frame in pure python. With Numba I got it down to 20 seconds per frame so far. Mostly by just adding an @njit decorator on top of the functions, and separating loops into their own jitted functions. Unbelievable. It just needs better support for calls to third party non-numba libraries and classes. then I'm sold, I wouldn't use anything else. Right now if there are any complex classes or third party calls I can get stumped trying to 'Numbaify' it.

Numba works for a Raspberry Pi Model 3B+?

how can i do this loop with numba for i in range(n): for j in range(m): if T[i][j] == 255: nbr=nbr+1 return (nbr)

Numba is amazing

I'm impressed the video has no thumbs down

7:53 - booty shake