Are list-comprehensions and functional functions faster than “for loops”?

Question

In terms of performance in Python, is a list-comprehension, or functions like map(), filter() and reduce() faster than a for loop? Why

Answer 1

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