timeit.repeat much faster than tic-toc even for one operation?

Question

I have a simple function fun and I'd like to measure how long it takes and also get the value back. I thought Python's "tic-toc" is the way to go, but I noticed that it's significanly slower than timeit (which doesn't return the value):

import numpy as np
import time
import timeit

np.random.seed(0)
x = np.random.rand(10000)

def fun():
    return np.sqrt(x)


t0_ns = time.time_ns()
fun()
t1_ns = time.time_ns()
print(t1_ns - t0_ns)


out = timeit.repeat(
  stmt=fun,
  repeat=1,
  number=1,
  timer=time.perf_counter_ns,
)
print(out[0])

58002
27037

In fact, timeit.repeat is more than twice as fast as the function evaluation itself! What's the reason for this? Is there a way around it?

Answer 1

Promoting Peter Cordes' reply to an answer here.

When reverting the order,

import numpy as np
import time
import timeit

np.random.seed(0)
x = np.random.rand(10000)


def fun():
    return np.sqrt(x)


out = timeit.repeat(
    stmt=fun,
    repeat=1,
    number=1,
    timer=time.perf_counter_ns,
)
print(out[0])

t0_ns = time.time_ns()
fun()
t1_ns = time.time_ns()
print(t1_ns - t0_ns)

36709
12623

the second operation is faster again. This makes clear that the execution speed has nothing to do with the timing methods, but with CPU warm-up effects.