Why the Fastest Way to Loop in Python Is Not to Loop at All

It is well known that Python is not a high‑performance language and that loops are time‑consuming; repeating a simple operation millions of times multiplies the execution time.

The two common loop keywords while and for have different runtimes. A benchmark shows a for loop summing numbers from 0 to n is about 1.5 seconds faster than an equivalent while loop.

import timeit

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

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

def main():
    print('while loop', timeit.timeit(while_loop, number=1))
    print('for loop', timeit.timeit(for_loop, number=1))

if __name__ == '__main__':
    main()
# => while loop        4.718853999860585
# => for loop          3.211570399813354

The speed gap comes from the extra boundary check and explicit increment that the while loop performs on each iteration, both written in pure Python, whereas the for loop relies on C‑level iteration.

Adding unnecessary checks or increments to a for loop eliminates its advantage, as shown by two additional functions that deliberately insert those operations.

def for_loop_with_inc(n=100_000_000):
    s = 0
    for i in range(n):
        s += i
        i += 1
    return s

def for_loop_with_test(n=100_000_000):
    s = 0
    for i in range(n):
        if i < n:
            pass
        s += i
    return s

Using Python’s built‑in sum function, which is implemented in C, reduces the runtime dramatically to under one second.

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

Even faster is applying the arithmetic series formula directly, which eliminates the loop entirely and finishes in microseconds.

def math_sum(n=100_000_000):
    return (n * (n - 1)) // 2

Thus, the fastest way to “loop” in Python is to avoid explicit loops and use built‑in functions or mathematical formulas whenever possible.