列表理解和功能比“ for循环”快吗?

问题:列表理解和功能比“ for循环”快吗?

在Python的性能方面,是一个列表理解或功能,如map()filter()reduce()比for循环快?从技术上讲,为什么它们以C速度运行,而for循环以python虚拟机速度运行

假设在我正在开发的游戏中,我需要使用for循环绘制复杂而庞大的地图。这个问题肯定是相关的,例如,如果列表理解确实确实更快,那么它将是避免滞后的更好选择(尽管代码具有视觉上的复杂性)。

In terms of performance in Python, is a list-comprehension, or functions like map(), filter() and reduce() faster than a for loop? Why, technically, they run in a C speed, while the for loop runs in the python virtual machine speed?.

Suppose that in a game that I’m developing I need to draw complex and huge maps using for loops. This question would be definitely relevant, for if a list-comprehension, for example, is indeed faster, it would be a much better option in order to avoid lags (Despite the visual complexity of the code).


回答 0

以下是粗略的准则和基于经验的有根据的猜测。您应该timeit或配置您的具体用例以获得确切的数字,并且这些数字有时可能与以下内容不一致。

列表理解通常比精确等效的for循环(实际上是构建列表)要快一点,这很可能是因为它不必append在每次迭代时都查找列表及其方法。但是,列表理解仍然会执行字节码级别的循环:

>>> dis.dis(<the code object for `[x for x in range(10)]`>)
 1           0 BUILD_LIST               0
             3 LOAD_FAST                0 (.0)
       >>    6 FOR_ITER                12 (to 21)
             9 STORE_FAST               1 (x)
            12 LOAD_FAST                1 (x)
            15 LIST_APPEND              2
            18 JUMP_ABSOLUTE            6
       >>   21 RETURN_VALUE

由于创建和扩展列表的开销很大,因此使用列表推导代替构建列表的循环,无意义地累积无意义的值的列表然后将其扔掉通常会较慢。列表理解并不是比一个好的旧循环本质上更快的魔术。

至于功能列表处理功能:虽然这些都是用C语言编写,并可能超越Python编写的相同的功能,它们是不是一定是最快的选择。如果该函数也是用C编写的,则可以提高速度。但是在大多数情况下,使用lambda(或其他Python函数)重复设置Python堆栈框架等开销会消耗掉所有的节省。在没有函数调用的情况下,简单地在线完成相同的工作(例如,用列表理解代替mapor filter)通常会稍快一些。

假设在我正在开发的游戏中,我需要使用for循环绘制复杂而庞大的地图。这个问题肯定是相关的,例如,如果列表理解确实确实更快,那么它将是避免滞后的更好选择(尽管代码具有视觉上的复杂性)。

很有可能,如果用良好的非“优化” Python编写时这样的代码还不够快,那么就没有足够的Python级微优化可以使它足够快了,您应该开始考虑降级为C。微观优化通常可以大大加快Python代码的速度,对此(绝对值)的限制很低。而且,即使在达到极限之前,咬住子弹并写一些C语言也变得更具成本效益(加速15%,加速300%)。

The following are rough guidelines and educated guesses based on experience. You should timeit or profile your concrete use case to get hard numbers, and those numbers may occasionally disagree with the below.

A list comprehension is usually a tiny bit faster than the precisely equivalent for loop (that actually builds a list), most likely because it doesn’t have to look up the list and its append method on every iteration. However, a list comprehension still does a bytecode-level loop:

>>> dis.dis(<the code object for `[x for x in range(10)]`>)
 1           0 BUILD_LIST               0
             3 LOAD_FAST                0 (.0)
       >>    6 FOR_ITER                12 (to 21)
             9 STORE_FAST               1 (x)
            12 LOAD_FAST                1 (x)
            15 LIST_APPEND              2
            18 JUMP_ABSOLUTE            6
       >>   21 RETURN_VALUE

Using a list comprehension in place of a loop that doesn’t build a list, nonsensically accumulating a list of meaningless values and then throwing the list away, is often slower because of the overhead of creating and extending the list. List comprehensions aren’t magic that is inherently faster than a good old loop.

As for functional list processing functions: While these are written in C and probably outperform equivalent functions written in Python, they are not necessarily the fastest option. Some speed up is expected if the function is written in C too. But most cases using a lambda (or other Python function), the overhead of repeatedly setting up Python stack frames etc. eats up any savings. Simply doing the same work in-line, without function calls (e.g. a list comprehension instead of map or filter) is often slightly faster.

Suppose that in a game that I’m developing I need to draw complex and huge maps using for loops. This question would be definitely relevant, for if a list-comprehension, for example, is indeed faster, it would be a much better option in order to avoid lags (Despite the visual complexity of the code).

Chances are, if code like this isn’t already fast enough when written in good non-“optimized” Python, no amount of Python level micro optimization is going to make it fast enough and you should start thinking about dropping to C. While extensive micro optimizations can often speed up Python code considerably, there is a low (in absolute terms) limit to this. Moreover, even before you hit that ceiling, it becomes simply more cost efficient (15% speedup vs. 300% speed up with the same effort) to bite the bullet and write some C.


回答 1

如果查看python.org上信息,则可以看到以下摘要:

Version Time (seconds)
Basic loop 3.47
Eliminate dots 2.45
Local variable & no dots 1.79
Using map function 0.54

但是您确实应该详细阅读以上文章,以了解造成性能差异的原因。

我也强烈建议您使用timeit对代码计时。在一天的结束时,可能会出现例如for在满足条件时需要跳出循环的情况。它可能比调用找出结果更快map

If you check the info on python.org, you can see this summary:

Version Time (seconds)
Basic loop 3.47
Eliminate dots 2.45
Local variable & no dots 1.79
Using map function 0.54

But you really should read the above article in details to understand the cause of the performance difference.

I also strongly suggest you should time your code by using timeit. At the end of the day, there can be a situation where, for example, you may need to break out of for loop when a condition is met. It could potentially be faster than finding out the result by calling map.


回答 2

你具体问左右map()filter()reduce(),但我相信你想了解一般的函数式编程。在对一组点中所有点之间的距离进行计算的问题上进行了自我测试之后,结果表明,函数编程(使用starmap内置itertools模块中的函数)比for循环要慢一些(使用1.25倍的时间)。事实)。这是我使用的示例代码:

import itertools, time, math, random

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

point_set = (Point(0, 0), Point(0, 1), Point(0, 2), Point(0, 3))
n_points = 100
pick_val = lambda : 10 * random.random() - 5
large_set = [Point(pick_val(), pick_val()) for _ in range(n_points)]
    # the distance function
f_dist = lambda x0, x1, y0, y1: math.sqrt((x0 - x1) ** 2 + (y0 - y1) ** 2)
    # go through each point, get its distance from all remaining points 
f_pos = lambda p1, p2: (p1.x, p2.x, p1.y, p2.y)

extract_dists = lambda x: itertools.starmap(f_dist, 
                          itertools.starmap(f_pos, 
                          itertools.combinations(x, 2)))

print('Distances:', list(extract_dists(point_set)))

t0_f = time.time()
list(extract_dists(large_set))
dt_f = time.time() - t0_f

功能版本是否比程序版本更快?

def extract_dists_procedural(pts):
    n_pts = len(pts)
    l = []    
    for k_p1 in range(n_pts - 1):
        for k_p2 in range(k_p1, n_pts):
            l.append((pts[k_p1].x - pts[k_p2].x) ** 2 +
                     (pts[k_p1].y - pts[k_p2].y) ** 2)
    return l

t0_p = time.time()
list(extract_dists_procedural(large_set)) 
    # using list() on the assumption that
    # it eats up as much time as in the functional version

dt_p = time.time() - t0_p

f_vs_p = dt_p / dt_f
if f_vs_p >= 1.0:
    print('Time benefit of functional progamming:', f_vs_p, 
          'times as fast for', n_points, 'points')
else:
    print('Time penalty of functional programming:', 1 / f_vs_p, 
          'times as slow for', n_points, 'points')

You ask specifically about map(), filter() and reduce(), but I assume you want to know about functional programming in general. Having tested this myself on the problem of computing distances between all points within a set of points, functional programming (using the starmap function from the built-in itertools module) turned out to be slightly slower than for-loops (taking 1.25 times as long, in fact). Here is the sample code I used:

import itertools, time, math, random

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

point_set = (Point(0, 0), Point(0, 1), Point(0, 2), Point(0, 3))
n_points = 100
pick_val = lambda : 10 * random.random() - 5
large_set = [Point(pick_val(), pick_val()) for _ in range(n_points)]
    # the distance function
f_dist = lambda x0, x1, y0, y1: math.sqrt((x0 - x1) ** 2 + (y0 - y1) ** 2)
    # go through each point, get its distance from all remaining points 
f_pos = lambda p1, p2: (p1.x, p2.x, p1.y, p2.y)

extract_dists = lambda x: itertools.starmap(f_dist, 
                          itertools.starmap(f_pos, 
                          itertools.combinations(x, 2)))

print('Distances:', list(extract_dists(point_set)))

t0_f = time.time()
list(extract_dists(large_set))
dt_f = time.time() - t0_f

Is the functional version faster than the procedural version?

def extract_dists_procedural(pts):
    n_pts = len(pts)
    l = []    
    for k_p1 in range(n_pts - 1):
        for k_p2 in range(k_p1, n_pts):
            l.append((pts[k_p1].x - pts[k_p2].x) ** 2 +
                     (pts[k_p1].y - pts[k_p2].y) ** 2)
    return l

t0_p = time.time()
list(extract_dists_procedural(large_set)) 
    # using list() on the assumption that
    # it eats up as much time as in the functional version

dt_p = time.time() - t0_p

f_vs_p = dt_p / dt_f
if f_vs_p >= 1.0:
    print('Time benefit of functional progamming:', f_vs_p, 
          'times as fast for', n_points, 'points')
else:
    print('Time penalty of functional programming:', 1 / f_vs_p, 
          'times as slow for', n_points, 'points')

回答 3

我写了一个简单的脚本来测试速度,这就是我发现的结果。实际上对于我来说,for循环最快。真的让我感到惊讶,请检查波纹管(计算平方和)。

from functools import reduce
import datetime


def time_it(func, numbers, *args):
    start_t = datetime.datetime.now()
    for i in range(numbers):
        func(args[0])
    print (datetime.datetime.now()-start_t)

def square_sum1(numbers):
    return reduce(lambda sum, next: sum+next**2, numbers, 0)


def square_sum2(numbers):
    a = 0
    for i in numbers:
        i = i**2
        a += i
    return a

def square_sum3(numbers):
    sqrt = lambda x: x**2
    return sum(map(sqrt, numbers))

def square_sum4(numbers):
    return(sum([int(i)**2 for i in numbers]))


time_it(square_sum1, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum2, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum3, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum4, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
0:00:00.302000 #Reduce
0:00:00.144000 #For loop
0:00:00.318000 #Map
0:00:00.390000 #List comprehension

I wrote a simple script that test the speed and this is what I found out. Actually for loop was fastest in my case. That really suprised me, check out bellow (was calculating sum of squares).

from functools import reduce
import datetime


def time_it(func, numbers, *args):
    start_t = datetime.datetime.now()
    for i in range(numbers):
        func(args[0])
    print (datetime.datetime.now()-start_t)

def square_sum1(numbers):
    return reduce(lambda sum, next: sum+next**2, numbers, 0)


def square_sum2(numbers):
    a = 0
    for i in numbers:
        i = i**2
        a += i
    return a

def square_sum3(numbers):
    sqrt = lambda x: x**2
    return sum(map(sqrt, numbers))

def square_sum4(numbers):
    return(sum([int(i)**2 for i in numbers]))


time_it(square_sum1, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum2, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum3, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum4, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
0:00:00.302000 #Reduce
0:00:00.144000 #For loop
0:00:00.318000 #Map
0:00:00.390000 #List comprehension

回答 4

我修改了@Alisa的代码,并用来cProfile说明为什么列表理解速度更快:

from functools import reduce
import datetime

def reduce_(numbers):
    return reduce(lambda sum, next: sum + next * next, numbers, 0)

def for_loop(numbers):
    a = []
    for i in numbers:
        a.append(i*2)
    a = sum(a)
    return a

def map_(numbers):
    sqrt = lambda x: x*x
    return sum(map(sqrt, numbers))

def list_comp(numbers):
    return(sum([i*i for i in numbers]))

funcs = [
        reduce_,
        for_loop,
        map_,
        list_comp
        ]

if __name__ == "__main__":
    # [1, 2, 5, 3, 1, 2, 5, 3]
    import cProfile
    for f in funcs:
        print('=' * 25)
        print("Profiling:", f.__name__)
        print('=' * 25)
        pr = cProfile.Profile()
        for i in range(10**6):
            pr.runcall(f, [1, 2, 5, 3, 1, 2, 5, 3])
        pr.create_stats()
        pr.print_stats()

结果如下:

=========================
Profiling: reduce_
=========================
         11000000 function calls in 1.501 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  1000000    0.162    0.000    1.473    0.000 profiling.py:4(reduce_)
  8000000    0.461    0.000    0.461    0.000 profiling.py:5(<lambda>)
  1000000    0.850    0.000    1.311    0.000 {built-in method _functools.reduce}
  1000000    0.028    0.000    0.028    0.000 {method 'disable' of '_lsprof.Profiler' objects}


=========================
Profiling: for_loop
=========================
         11000000 function calls in 1.372 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  1000000    0.879    0.000    1.344    0.000 profiling.py:7(for_loop)
  1000000    0.145    0.000    0.145    0.000 {built-in method builtins.sum}
  8000000    0.320    0.000    0.320    0.000 {method 'append' of 'list' objects}
  1000000    0.027    0.000    0.027    0.000 {method 'disable' of '_lsprof.Profiler' objects}


=========================
Profiling: map_
=========================
         11000000 function calls in 1.470 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  1000000    0.264    0.000    1.442    0.000 profiling.py:14(map_)
  8000000    0.387    0.000    0.387    0.000 profiling.py:15(<lambda>)
  1000000    0.791    0.000    1.178    0.000 {built-in method builtins.sum}
  1000000    0.028    0.000    0.028    0.000 {method 'disable' of '_lsprof.Profiler' objects}


=========================
Profiling: list_comp
=========================
         4000000 function calls in 0.737 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  1000000    0.318    0.000    0.709    0.000 profiling.py:18(list_comp)
  1000000    0.261    0.000    0.261    0.000 profiling.py:19(<listcomp>)
  1000000    0.131    0.000    0.131    0.000 {built-in method builtins.sum}
  1000000    0.027    0.000    0.027    0.000 {method 'disable' of '_lsprof.Profiler' objects}

恕我直言:

  • reduce并且map总体上来说很慢。不仅如此,summap返回sum列表相比,在返回的迭代器上使用慢
  • for_loop 使用append,这在某种程度上当然很慢
  • 列表理解不仅花费了最少的时间来构建列表,而且与之sum相比,它也变得更快map

I modified @Alisa’s code and used cProfile to show why list comprehension is faster:

from functools import reduce
import datetime

def reduce_(numbers):
    return reduce(lambda sum, next: sum + next * next, numbers, 0)

def for_loop(numbers):
    a = []
    for i in numbers:
        a.append(i*2)
    a = sum(a)
    return a

def map_(numbers):
    sqrt = lambda x: x*x
    return sum(map(sqrt, numbers))

def list_comp(numbers):
    return(sum([i*i for i in numbers]))

funcs = [
        reduce_,
        for_loop,
        map_,
        list_comp
        ]

if __name__ == "__main__":
    # [1, 2, 5, 3, 1, 2, 5, 3]
    import cProfile
    for f in funcs:
        print('=' * 25)
        print("Profiling:", f.__name__)
        print('=' * 25)
        pr = cProfile.Profile()
        for i in range(10**6):
            pr.runcall(f, [1, 2, 5, 3, 1, 2, 5, 3])
        pr.create_stats()
        pr.print_stats()

Here’s the results:

=========================
Profiling: reduce_
=========================
         11000000 function calls in 1.501 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  1000000    0.162    0.000    1.473    0.000 profiling.py:4(reduce_)
  8000000    0.461    0.000    0.461    0.000 profiling.py:5(<lambda>)
  1000000    0.850    0.000    1.311    0.000 {built-in method _functools.reduce}
  1000000    0.028    0.000    0.028    0.000 {method 'disable' of '_lsprof.Profiler' objects}


=========================
Profiling: for_loop
=========================
         11000000 function calls in 1.372 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  1000000    0.879    0.000    1.344    0.000 profiling.py:7(for_loop)
  1000000    0.145    0.000    0.145    0.000 {built-in method builtins.sum}
  8000000    0.320    0.000    0.320    0.000 {method 'append' of 'list' objects}
  1000000    0.027    0.000    0.027    0.000 {method 'disable' of '_lsprof.Profiler' objects}


=========================
Profiling: map_
=========================
         11000000 function calls in 1.470 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  1000000    0.264    0.000    1.442    0.000 profiling.py:14(map_)
  8000000    0.387    0.000    0.387    0.000 profiling.py:15(<lambda>)
  1000000    0.791    0.000    1.178    0.000 {built-in method builtins.sum}
  1000000    0.028    0.000    0.028    0.000 {method 'disable' of '_lsprof.Profiler' objects}


=========================
Profiling: list_comp
=========================
         4000000 function calls in 0.737 seconds

   Ordered by: standard name

   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
  1000000    0.318    0.000    0.709    0.000 profiling.py:18(list_comp)
  1000000    0.261    0.000    0.261    0.000 profiling.py:19(<listcomp>)
  1000000    0.131    0.000    0.131    0.000 {built-in method builtins.sum}
  1000000    0.027    0.000    0.027    0.000 {method 'disable' of '_lsprof.Profiler' objects}

IMHO:

  • reduce and map are in general pretty slow. Not only that, using sum on the iterators that map returned is slow, compared to suming a list
  • for_loop uses append, which is of course slow to some extent
  • list-comprehension not only spent the least time building the list, it also makes sum much quicker, in contrast to map

回答 5

Alphii答案增加一点扭曲,实际上for循环将是第二好,并且比循环慢6倍map

from functools import reduce
import datetime


def time_it(func, numbers, *args):
    start_t = datetime.datetime.now()
    for i in range(numbers):
        func(args[0])
    print (datetime.datetime.now()-start_t)

def square_sum1(numbers):
    return reduce(lambda sum, next: sum+next**2, numbers, 0)


def square_sum2(numbers):
    a = 0
    for i in numbers:
        a += i**2
    return a

def square_sum3(numbers):
    a = 0
    map(lambda x: a+x**2, numbers)
    return a

def square_sum4(numbers):
    a = 0
    return [a+i**2 for i in numbers]

time_it(square_sum1, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum2, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum3, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum4, 100000, [1, 2, 5, 3, 1, 2, 5, 3])

主要的更改是消除了缓慢的sum通话,以及int()在最后一种情况下可能不必要的通话。实际上,将for循环和map放在相同的术语中就可以说是事实。请记住,lambda是功能性概念,从理论上讲不应该具有副作用,但是,它们可以具有诸如添加到的副作用a。在这种情况下使用Python 3.6.1,Ubuntu 14.04,Intel(R)Core(TM)i7-4770 CPU @ 3.40GHz时的结果

0:00:00.257703 #Reduce
0:00:00.184898 #For loop
0:00:00.031718 #Map
0:00:00.212699 #List comprehension

Adding a twist to Alphii answer, actually the for loop would be second best and about 6 times slower than map

from functools import reduce
import datetime


def time_it(func, numbers, *args):
    start_t = datetime.datetime.now()
    for i in range(numbers):
        func(args[0])
    print (datetime.datetime.now()-start_t)

def square_sum1(numbers):
    return reduce(lambda sum, next: sum+next**2, numbers, 0)


def square_sum2(numbers):
    a = 0
    for i in numbers:
        a += i**2
    return a

def square_sum3(numbers):
    a = 0
    map(lambda x: a+x**2, numbers)
    return a

def square_sum4(numbers):
    a = 0
    return [a+i**2 for i in numbers]

time_it(square_sum1, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum2, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum3, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum4, 100000, [1, 2, 5, 3, 1, 2, 5, 3])

Main changes have been to eliminate the slow sum calls, as well as the probably unnecessary int() in the last case. Putting the for loop and map in the same terms makes it quite fact, actually. Remember that lambdas are functional concepts and theoretically shouldn’t have side effects, but, well, they can have side effects like adding to a. Results in this case with Python 3.6.1, Ubuntu 14.04, Intel(R) Core(TM) i7-4770 CPU @ 3.40GHz

0:00:00.257703 #Reduce
0:00:00.184898 #For loop
0:00:00.031718 #Map
0:00:00.212699 #List comprehension

回答 6

我设法修改了@alpiii的一些代码,并发现List理解比for循环快一点。它可能是由引起的int(),在列表理解和for循环之间是不公平的。

from functools import reduce
import datetime

def time_it(func, numbers, *args):
    start_t = datetime.datetime.now()
    for i in range(numbers):
        func(args[0])
    print (datetime.datetime.now()-start_t)

def square_sum1(numbers):
    return reduce(lambda sum, next: sum+next*next, numbers, 0)

def square_sum2(numbers):
    a = []
    for i in numbers:
        a.append(i*2)
    a = sum(a)
    return a

def square_sum3(numbers):
    sqrt = lambda x: x*x
    return sum(map(sqrt, numbers))

def square_sum4(numbers):
    return(sum([i*i for i in numbers]))

time_it(square_sum1, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum2, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum3, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum4, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
0:00:00.101122 #Reduce

0:00:00.089216 #For loop

0:00:00.101532 #Map

0:00:00.068916 #List comprehension

I have managed to modify some of @alpiii’s code and discovered that List comprehension is a little faster than for loop. It might be caused by int(), it is not fair between list comprehension and for loop.

from functools import reduce
import datetime

def time_it(func, numbers, *args):
    start_t = datetime.datetime.now()
    for i in range(numbers):
        func(args[0])
    print (datetime.datetime.now()-start_t)

def square_sum1(numbers):
    return reduce(lambda sum, next: sum+next*next, numbers, 0)

def square_sum2(numbers):
    a = []
    for i in numbers:
        a.append(i*2)
    a = sum(a)
    return a

def square_sum3(numbers):
    sqrt = lambda x: x*x
    return sum(map(sqrt, numbers))

def square_sum4(numbers):
    return(sum([i*i for i in numbers]))

time_it(square_sum1, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum2, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum3, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
time_it(square_sum4, 100000, [1, 2, 5, 3, 1, 2, 5, 3])
0:00:00.101122 #Reduce

0:00:00.089216 #For loop

0:00:00.101532 #Map

0:00:00.068916 #List comprehension