当可迭代包含数百万个元素时，是否有 zip(*iterable) 的替代方法？

Question

我遇到过这样的代码：

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]

而且我认为这段代码不是 Pythonic，因为它会重复自己。如果向Point 类添加另一个维度，则需要编写一个全新的循环，如下所示：

zs = [point.z for point in points]

所以我试着写这样的东西让它更 Pythonic：

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

如果添加新维度，没问题：

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

但是当有数百万个点时，这几乎比其他解决方案慢 10 倍，尽管它只有一个循环。我认为这是因为 * 运算符需要将数百万个参数解压缩到 zip 函数，这太可怕了。所以我的问题是：

有没有办法改变上面的代码，使其和以前一样快和Pythonic（不使用第三方库）？

Answer 1

zip(*iter) 的问题在于它会遍历整个可迭代对象并将结果序列作为 args 传递给 zip。

所以这些在功能上是相同的：

使用 *: xs, ys = zip(*[(p.x, p.y) for p in ((0,1),(0,2),(0,3))])

使用定位： xz, ys = zip((0,1),(0,2),(0,3))。

显然，如果有数百万个位置参数，这会很慢。

迭代器方法是唯一的解决方法。

我在网络上搜索了python itertools unzip。可悲的是，最接近的itertools 是tee。在上述要点的链接中，itertools.tee 的迭代器元组从 iunzip 的实现返回：https://gist.github.com/andrix/106334。

我必须把它转换成 python3：

from random import randint
import itertools
import time
from operator import itemgetter

def iunzip(iterable):
    """Iunzip is the same as zip(*iter) but returns iterators, instead of 
    expand the iterator. Mostly used for large sequence"""

    _tmp, iterable = itertools.tee(iterable, 2)
    iters = itertools.tee(iterable, len(next(_tmp)))
    return (map(itemgetter(i), it) for i, it in enumerate(iters))

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

points = [Point(randint(1, 10), randint(1, 10)) for _ in range(1000000)]
itime = time.time()
xs = [point.x for point in points]
ys = [point.y for point in points]
otime = time.time() - itime
itime += otime
print(f"original: {otime}")
xs, ys = zip(*[(p.x, p.y) for p in points])
otime = time.time() - itime
itime += otime
print(f"unpacking into zip: {otime}")
xs, ys = iunzip(((p.x, p.y) for p in points))
for _ in zip(xs, ys): pass
otime = time.time() - itime
itime += otime
print(f"iunzip: {otime}")

输出：

original: 0.1282501220703125
unpacking into zip: 1.286362886428833
iunzip: 0.3046858310699463

所以迭代器肯定比解包到位置参数更好。更不用说当我达到 1000 万点时，我的 4GB 内存被吃光了……但是，我不相信上面的 iunzip 迭代器是最优化的，因为它是一个内置的 python 考虑到像“原始”方法一样迭代两次以进行解压缩仍然是迄今为止最快的（尝试使用各种长度的点快约 4 倍）。

似乎iunzip 应该是一个东西。我很惊讶它不是 python 内置或 itertools 的一部分......

Answer 2

我刚刚测试了几种压缩Point 坐标的方法，并随着点数的增加寻找它们的性能。

以下是我用来测试的函数：

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')
    ]

使用 timeit 我用不同的点数对每个函数的执行进行计时，结果如下：

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%]

---

我们可以看到，“硬编码”解决方案与使用 gettattr 构建的理解解决方案之间的差距似乎随着点数的增加而不断缩小。

因此，对于大量的点，使用从坐标列表生成的推导可能是个好主意：

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

但是，对于少数点来说，这是最差的解决方案（至少从我测试的解决方案来看）。

---

有关信息，这是我用来运行此基准测试的代码：

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)