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While I'm profiling my Python application, I've found that len() is by all accounts an extravagant one when utilizing sets. See the underneath code: 

import cProfile

def lenA(s):

    for i in range(1000000):

        len(s);

def lenB(s):

    for i in range(1000000):

        s.__len__();

def main():

    s = set();

    lenA(s);

    lenB(s);

if __name__ == "__main__":

    cProfile.run("main()","stats");

As indicated by the profiler's details underneath, lenA() is by all accounts 14 times more slow than lenB()

ncalls  tottime  percall  cumtime  percall  filename:lineno(function)

      1    1.986    1.986    3.830    3.830  .../lentest.py:5(lenA)

1000000    1.845    0.000    1.845    0.000  {built-in method len}

      1    0.273    0.273    0.273    0.273  .../lentest.py:9(lenB)

Am I missing anything? Right now I use __len__() rather than len(), yet the code looks filthy :(

1 Answer

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by (24.6k points)

Clearly, len has some overhead and since it does a function call and makes an interpretation of AttributeError to TypeError. Additionally, set.__len__ is a particularly basic operation that it will undoubtedly be exceptionally quick in contrast with pretty much anything, yet I actually don't discover anything like the 14x distinction when utilizing timeit

In [1]: s = set()

In [2]: %timeit s.__len__()

1000000 loops, best of 3: 197 ns per loop

In [3]: %timeit len(s)

10000000 loops, best of 3: 130 ns per loop

You should in every case simply call len, not __len__. In the event that the call to len is the bottleneck in your program, you should reexamine its design, for example, cache measures someplace or ascertain them without calling len.

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