By running the line to measure a lot of time (see below), timeit give a median value of consumed time. Simply call it this way (in a Jupyter cell):
import timeit %timeit to_measure()
The output looks like this:
1.09 µs ± 32.2 ns per loop (mean ± std. dev. of 7 runs, 1000000 loops each)
Timeit typically run the method for a 1’000’000 times so that the result is a median useful value. If the method takes time, timeit will run it less times (down to 10’000 or lower).
This is a parameter that you can force if you feel the need ;)
There is also the availability to give a defined timer.
It is also possible to call it from command line (both of those examples are from the documentation):
$ python3 -m timeit '"-".join(str(n) for n in range(100))'
Or from Python interpreter:
>>> import timeit >>> timeit.timeit('"-".join(str(n) for n in range(100))', number=10000)