就Python中的性能而言，列表理解或map()， filter()和reduce()等函数是否比for循环更快?为什么，从技术上讲，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循环绘制复杂而巨大的地图。这个问题肯定是相关的，例如，如果一个列表理解确实更快，为了避免延迟，它将是一个更好的选择(尽管代码的视觉复杂性)。
当前回答
如果你检查python.org上的信息，你可以看到这个摘要:
Version Time (seconds)
Basic loop 3.47
Eliminate dots 2.45
Local variable & no dots 1.79
Using map function 0.54
但您确实应该详细阅读上面的文章，以了解性能差异的原因。
我还强烈建议您使用timeit来计时。最后，可能会出现这样的情况，例如，当满足条件时，您可能需要跳出for循环。这可能比调用map得到结果更快。
其他回答
我修改了@Alisa的代码，并使用cProfile来展示为什么列表理解更快:
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和map通常都很慢。不仅如此，在map返回的迭代器上使用sum函数比在列表中使用sum函数要慢 For_loop使用append，这在某种程度上当然比较慢 列表理解不仅花费最少的时间构建列表，而且与map相比，它还使sum更快
我写了一个简单的脚本来测试速度，这是我发现的。实际上在我的例子中，for循环是最快的。这真的让我很惊讶，看看下面(正在计算平方和)。
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
您特别询问了map()、filter()和reduce()，但我假设您想了解一般的函数式编程。我亲自测试了计算一组点中所有点之间距离的问题，函数式编程(使用内置itertools模块中的starmap函数)比for-loops稍微慢一点(实际上需要1.25倍的时间)。下面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
函数版本是否比过程版本更快?
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')
在Alphii的答案中添加一个扭曲，实际上for循环是第二好的，大约比map慢6倍
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])
主要的变化是消除了缓慢的sum调用，以及在最后一种情况下可能不必要的int()。将for循环和map放在同一个术语中，实际上使它变得非常真实。记住lambda是函数概念，理论上不应该有副作用，但是，它们可能有副作用，比如添加到。 在这种情况下，使用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
我已经设法修改了一些@alpiii的代码，并发现List理解比for循环快一点。它可能是由int()引起的，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
