Python have timeit module to test how long a piece of code takes to execute. On the other ipython %timeit magic gives much more useful information.
$ python3 -m timeit -s "5 == 55"
100000000 loops, best of 3: 0.00543 usec per loop
In [23]: %timeit 5 == 55
18.3 ns ± 0.393 ns per loop (mean ± std. dev. of 7 runs, 10000000 loops each)
Luckily @Numerlor was kind enough to extract the functionality from ipython for us
In [26]: nice_timeit('5 == 55')
18.4 ns +- 0.328 ns per loop (mean +- std. dev. of 7 runs, 10000000 loops each)
import math
import timeit
def _format_time(timespan, precision=3):
"""Formats the timespan in a human readable form"""
units = ["s", "ms", "\xb5s", "ns"]
scaling = [1, 1e3, 1e6, 1e9]
if timespan > 0.0:
order = min(-int(math.floor(math.log10(timespan)) // 3), 3)
else:
order = 3
scaled_time = timespan * scaling[order]
unit = units[order]
return f"{scaled_time:.{precision}g} {unit}"
class TimeitResult(object):
"""
Object returned by the timeit magic with info about the run.
Contains the following attributes :
loops: (int) number of loops done per measurement
repeat: (int) number of times the measurement has been repeated
best: (float) best execution time / number
all_runs: (list of float) execution time of each run (in s)
compile_time: (float) time of statement compilation (s)
"""
def __init__(self, loops, repeat, best, worst, all_runs, compile_time, precision):
self.loops = loops
self.repeat = repeat
self.best = best
self.worst = worst
self.all_runs = all_runs
self.compile_time = compile_time
self._precision = precision
self.timings = [dt / self.loops for dt in all_runs]
@property
def average(self):
return math.fsum(self.timings) / len(self.timings)
@property
def stdev(self):
mean = self.average
return (
math.fsum([(x - mean) ** 2 for x in self.timings]) / len(self.timings)
) ** 0.5
def __str__(self):
return "{mean} {pm} {std} per loop (mean {pm} std. dev. of {runs} run{run_plural}, {loops} loop{loop_plural} each)".format(
pm="+-",
runs=self.repeat,
loops=self.loops,
loop_plural="" if self.loops == 1 else "s",
run_plural="" if self.repeat == 1 else "s",
mean=_format_time(self.average, self._precision),
std=_format_time(self.stdev, self._precision),
)
def nice_timeit(
stmt="pass",
setup="pass",
number=0,
repeat=None,
precision=3,
timer_func=timeit.default_timer,
globals=None,
):
"""Time execution of a Python statement or expression."""
if repeat is None:
repeat = 7 if timeit.default_repeat < 7 else timeit.default_repeat
timer = timeit.Timer(stmt, setup, timer=timer_func, globals=globals)
# Get compile time
compile_time_start = timer_func()
compile(timer.src, "<timeit>", "exec")
total_compile_time = timer_func() - compile_time_start
# This is used to check if there is a huge difference between the
# best and worst timings.
# Issue: https://github.com/ipython/ipython/issues/6471
if number == 0:
# determine number so that 0.2 <= total time < 2.0
for index in range(0, 10):
number = 10 ** index
time_number = timer.timeit(number)
if time_number >= 0.2:
break
all_runs = timer.repeat(repeat, number)
best = min(all_runs) / number
worst = max(all_runs) / number
timeit_result = TimeitResult(
number, repeat, best, worst, all_runs, total_compile_time, precision
)
# Check best timing is greater than zero to avoid a
# ZeroDivisionError.
# In cases where the slowest timing is lesser than a microsecond
# we assume that it does not really matter if the fastest
# timing is 4 times faster than the slowest timing or not.
if worst > 4 * best and best > 0 and worst > 1e-6:
print(
f"The slowest run took {worst / best:.2f} times longer than the "
f"fastest. This could mean that an intermediate result "
f"is being cached."
)
print(timeit_result)
if total_compile_time > 0.1:
print(f"Compiler time: {total_compile_time:.2f} s")
return timeit_result
# nice_timeit("time.sleep(0.3)", "import time")
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