Is there an alternative for zip(*iterable) when the iterable consists of millions of elements?

Question

I have come across a code like this:

from random import randint

class Point:
    def __init__(self, x, y):
        self.x = x
        self.y = y

points = [Point(randint(1, 10), randint(1, 10)) for _ in range(10)]
xs = [point.x for point in points]
ys = [point.y for point in points]

And I think this code is not Pythonic because it repeats itself. If another dimension is added to Point class, a whole new loop needs to be written like:

zs = [point.z for point in points]

So I tried to make it more Pythonic by writing something like this:

xs, ys = zip(*[(point.x, point.y) for point in p])

If a new dimension is added, no problem:

xs, ys, zs = zip(*[(point.x, point.y, point.z) for point in p])

But this is almost 10 times slower than the other solution when there are millions of points, although it has only one loop. I think it is because * operator needs to unpack millions of arguments to the zip function which is horrible. So my question is:

Is there a way to change the code above so that it is as fast as before and Pythonic (without using 3rd party libraries)?

Answer 1

I just tested several ways of zipping Point coordinates and looked for their performance with increasing number of points.

Below are the functions I used to test:

def hardcode(points):
    # a hand crafted comprehension for each coordinate
    return [point.x for point in points], [point.y for point in points]


def using_zip(points):
    # using the "problematic" qip function
    return zip(*((point.x, point.y) for point in points))


def loop_and_comprehension(points):
    # making comprehension from a list of coordinate names
    zipped = []
    for coordinate in ('x', 'y'):
        zipped.append([getattr(point, coordinate) for point in points])
    return zipped


def nested_comprehension(points):
    # making comprehension from a list of coordinate names using nested
    # comprehensions
    return [
        [getattr(point, coordinate) for point in points]
        for coordinate in ('x', 'y')
    ]

Using timeit I timed execution of each function with different number of points and here are the results:

comparing processing times using 10 points and 10000000 iterations
hardcode................. 14.12024447 [+0%]
using_zip................ 16.84289724 [+19%]
loop_and_comprehension... 30.83631476 [+118%]
nested_comprehension..... 30.45758349 [+116%]

comparing processing times using 100 points and 1000000 iterations
hardcode................. 9.30594717 [+0%]
using_zip................ 13.74953714 [+48%]
loop_and_comprehension... 19.46766583 [+109%]
nested_comprehension..... 19.27818860 [+107%]

comparing processing times using 1000 points and 100000 iterations
hardcode................. 7.90372457 [+0%]
using_zip................ 12.51523594 [+58%]
loop_and_comprehension... 18.25679913 [+131%]
nested_comprehension..... 18.64352790 [+136%]

comparing processing times using 10000 points and 10000 iterations
hardcode................. 8.27348382 [+0%]
using_zip................ 18.23079485 [+120%]
loop_and_comprehension... 18.00183383 [+118%]
nested_comprehension..... 17.96230063 [+117%]

comparing processing times using 100000 points and 1000 iterations
hardcode................. 9.15848662 [+0%]
using_zip................ 22.70730675 [+148%]
loop_and_comprehension... 17.81126971 [+94%]
nested_comprehension..... 17.86892597 [+95%]

comparing processing times using 1000000 points and 100 iterations
hardcode................. 9.75002857 [+0%]
using_zip................ 23.13891725 [+137%]
loop_and_comprehension... 18.08724660 [+86%]
nested_comprehension..... 18.01269820 [+85%]

comparing processing times using 10000000 points and 10 iterations
hardcode................. 9.96045920 [+0%]
using_zip................ 23.11653558 [+132%]
loop_and_comprehension... 17.98296033 [+81%]
nested_comprehension..... 18.17317708 [+82%]

comparing processing times using 100000000 points and 1 iterations
hardcode................. 64.58698246 [+0%]
using_zip................ 92.53437881 [+43%]
loop_and_comprehension... 73.62493845 [+14%]
nested_comprehension..... 62.99444739 [-2%]

---

We can see that the gap between the "harcoded" solution and the solutions with comprehensions built with gettattr seems to constantly reduce as the number of points grows.

So, for a very big number of points it could be a good idea to use generated comprehensions from a list of coordinates:

[[getattr(point, coordinate) for point in points]
 for coordinate in ('x', 'y')]

However, for a small number of points this is the worst solution (from the ones I tested at least).

---

For information, here is the code I used to run this benchmark:

import timeit


...


def compare(nb_points, nb_iterations):
    reference = None
    points = [Point(randint(1, 100), randint(1, 100))
              for _ in range(nb_points)]
    print("comparing processing times using {} points and {} iterations"
          .format(nb_points, nb_iterations))

    for func in (hardcode, using_zip, loop_and_comprehension, nested_comprehension):
        duration = timeit.timeit(lambda: func(points), number=nb_iterations)

        print('{:.<25} {:0=2.8f} [{:0>+.0%}]'
              .format(func.__name__, duration,
                      0 if reference is None else (duration / reference - 1)))

        if reference is None:
            reference = duration

    print("-" * 80)



compare(10, 10000000)
compare(100, 1000000)
compare(1000, 100000)
compare(10000, 10000)
compare(100000, 1000)
compare(1000000, 100)
compare(10000000, 10)
compare(100000000, 1)