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by theamk2 days ago |
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upvoteby Pinus1 days ago|
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Has the performance of sum on lists of lists in Python been fixed? It used to be pretty abysmal. But I suppose some would say that if you need to consider performance at all, you’re in the wrong language… :)
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upvoteby theamk1 days ago|
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wow, TIL!

    Python 3.13.5 (main, Jul 15 2026, 20:25:40) [GCC 14.2.0] on linux
    >>> x = [[n]*1000 for n in range(1000)]; import timeit, itertools, functools, operator
    >>> timeit.timeit("len(list(sum(x, [])))", number=10, globals=globals())
    13.009033881127834
    >>> timeit.timeit("len(list(list(functools.reduce(operator.add, x, []))))", number=10, globals=globals())
    12.941937348805368
    >>> timeit.timeit("len(list(itertools.chain.from_iterable(x)))", number=10, globals=globals())
    0.0706032607704401
    >>> timeit.timeit("out=[]; [out.extend(i) for i in x]; len(out)", number=10, globals=globals())
    0.06334403157234192
    >>> timeit.timeit("len([i for a in x for i in a])", number=10, globals=globals())
    0.1232151910662651
mutable is fastest, itertools is just a bit slower, list comprehension is 2x slower, both "sum(..., [])" and "reduce" are 200 times slower!
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upvoteby mahboi11 hours ago|
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Yeah this is the kind of reason people dislike reduce
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upvoteby rsfern2 days ago|
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For numerical code I like einops.reduce more than numpy/pytorch sum reductions because you can reduce over named dimensions. It’s much more readable than having to reason through axis indexing again every time you come back to the code
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