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pythonperformancetypingdynamic-typing

Why Python works faster when declaring integer variable with int()?

While programming a algorithm that makes use of only using integer arithmetic I notice that Python wasn't taking advantage of it.

So I tried the following code to see the "explicitly" declaration effect

import time

repeat = 1000000

start = time.time()
x = 0
for i in range(repeat):
    x += 1
no_type_time = time.time() - start

start = time.time()
y = int(0)
for i in range(repeat):
    y += 1
int_time = time.time() - start

print('{} - No type'.format(no_type_time))
print('{} - Int'.format(int_time))

The code output was the following:

0.0692429542542 - No type
0.0545210838318 - Int

I assume it has something to do with Python being a dynamic typed language. But when I try to find out the type of the variables, using type(x) and type(y) both output int. Which is curious, because I also ran some tests using x = float(0) and the result is very close to the one with no type "declaration".

I'd like to know why it happens and if possible to get some reference from Python documentation explaining it.

Solution

  • From the precision on the floats in your str.format output (12 significant digits), we can see that you're probably on Python 2.

    Python 2 creates an explicit list of a million ints when you run range(repeat), which is slow. It also keeps the memory for all of those ints, so range(repeat) is less slow the second time. This is most likely the source of the timing difference, not anything to do with calling int.

    On Python 2, it is almost always better to use xrange instead of range. xrange generates ints on demand, avoiding the cost in memory and allocation time of generating a whole list up front:

    for i in xrange(repeat):
    do_stuff()