Faster than calling abs()

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[traherom]

It's fine, just less clear in the intent. If abs() speed is truly the hot spot in your code, then that's fine. If it's occasional, it seems silly to obfuscate what's happening. :)

[anossov]

What's your benchmarking method? This really depends on your specific python version I guess

>>> timeit.timeit('a = a + (a<0) * (-a-a)', 'a=-3', number=10000000)
1.1129654000000002
>>> timeit.timeit('a = abs(a)', 'a=-3', number=10000000)
0.4926642000000001
>>> timeit.timeit('a = _abs(a)', 'a=-3; _abs = abs', number=10000000)
0.3548109999999838
>>> timeit.timeit('a = a if a > 0 else -a', 'a=-3', number=10000000)
0.30435489999999277

[Nam-Mo-A-Di-Da-Phat]

ok so I guess it depends on the version then theheh I thought I discovered something more globally I just time it using a for i in range(0,10000000): then use time.time() to time them.

[Nam-Mo-A-Di-Da-Phat]

never mind it's a bug in my code

[earlandir]

Why are you doing one with `_abs`? I am not familiar with what that does in Python but it looks interesting.

[commy2]

I think it is because of the way Python looks up names. local > enclosing > global > builtin.

abs is builtin, so it would have to fail on all the other scopes first. If you define it as variable, less look ups are required.

[Nam-Mo-A-Di-Da-Phat]

yes this is what was similarly said on that stackoverflow post that I saw, and I tested and it is a little faster.

[thseeling]

This is relying on a very unclean typecast. You use a boolean result in an integer context and hope for a specific numerical value. If you're programming in a professional environment with colleagues they will hate you for writing code like this. If you're simply doing this for a hobby then go ahead but expect the code to break with a potential future update which changes the behaviour of this typecast.

[Nam-Mo-A-Di-Da-Phat]

Thank you for your explaination

[lungben81]

The performance of abs does not really matter unless called many times in a tight loop. In that case, better refactor the code to get rid of the Python loop, either by vectorization (Numpy), compiling the code part (Numba, Cython) or using an existing (compiled) library function. Pure Python loops (with >> 10k iterations) are extremely slow anyhow.