How many fast looping methods do you know in Python?

Hello everyone, I am somenzz, today we will study the fastest loop method in Python.

Various postures

For example, there is a simple task, which is to accumulate from 1 to 100 million. We can achieve it in at least 7 ways, listed as follows:

1. while loop

def while_loop(n=100_000_000):
i = 0
s = 0
while i < n:
s += i
i += 1
return s

2. for loop

def for_loop(n=100_000_000):
s = 0
for i in range(n):
s += i
return s

3. sum range

def sum_range(n=100_000_000):
return sum(range(n))

4. sum generator

def sum_generator(n=100_000_000):
return sum(i for i in range(n))

5. sum list comprehension (list comprehension)

def sum_list_comp(n=100_000_000):
return sum([i for i in range(n)])

6, sum numpy

import numpy
def sum_numpy(n=100_000_000):
return numpy.sum(numpy.arange(n, dtype=numpy.int64))

7, sum numpy python range

import numpy
def sum_numpy_python_range(n=100_000_000):
return numpy.sum(range(n))

The results obtained by the above 7 methods are the same. However, the time consumed varies. You can guess which method is the fastest, and then look at the execution results of the following code:

import timeit

def main():
l_align = 25
print(f'{"1、while 循环":<{l_align}} {timeit.timeit(while_loop, number=1):.6f}')
print(f"{'2、for 循环':<{l_align}}{timeit.timeit(for_loop, number=1):.6f}")
print(f'{"3、sum range":<{l_align}} {timeit.timeit(sum_range, number=1):.6f}')
print(f'{"4、sum generator":<{l_align}} {timeit.timeit(sum_generator, number=1):.6f}')
print(f'{"5、sum list comprehension":<{l_align}} {timeit.timeit(sum_list_comp, number=1):.6f}')
print(f'{"6、sum numpy":<{l_align}} {timeit.timeit(sum_numpy, number=1):.6f}')
print(f'{"7、sum numpy python range":<{l_align}} {timeit.timeit(sum_numpy_python_range, number=1):.6f}')

if __name__ == '__main__':
main()

The execution results are as follows:

A faster way

for than while block

for and while are essentially doing the same thing, but while is pure Python code, while for calls a C extension To increment and bound check variables, we know that the CPython interpreter is written in C language, Python code is slower than C code, and the for loop represents C, and the while loop represents Python, so for is faster than while.

numpy's built-in sum is faster than Python's sum

numpy is mainly written in C. For the same function, numpy is definitely faster. Similarly, numpy's arange is definitely faster than Python's. range fast.

Crossed use will be slower

Numpy's sum is used in combination with Python's range, and the result is the longest, see method 7. It is best to use the numpy package to complete the task, like method 6.

Generators are faster than list comprehensions

Generators are lazy and will not generate 100 million numbers at once, while list comprehensions will allocate all the numbers at once, occupying less memory. Not to mention being higher, the cache cannot be used effectively, so the performance is slightly worse.

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