:mod:`!timeit` --- Measure execution time of small code snippets
.. module:: timeit :synopsis: Measure the execution time of small code snippets.
Source code: :source:`Lib/timeit.py`
.. index:: single: Benchmarking single: Performance
This module provides a simple way to time small bits of Python code. It has both a :ref:`timeit-command-line-interface` as well as a :ref:`callable <python-interface>` one. It avoids a number of common traps for measuring execution times. See also Tim Peters' introduction to the "Algorithms" chapter in the second edition of Python Cookbook, published by O'Reilly.
The following example shows how the :ref:`timeit-command-line-interface` can be used to compare three different expressions:
$ python -m timeit "'-'.join(str(n) for n in range(100))"
10000 loops, best of 5: 30.2 usec per loop
$ python -m timeit "'-'.join([str(n) for n in range(100)])"
10000 loops, best of 5: 27.5 usec per loop
$ python -m timeit "'-'.join(map(str, range(100)))"
10000 loops, best of 5: 23.2 usec per loop
This can be achieved from the :ref:`python-interface` with:
>>> import timeit
>>> timeit.timeit('"-".join(str(n) for n in range(100))', number=10000)
0.3018611848820001
>>> timeit.timeit('"-".join([str(n) for n in range(100)])', number=10000)
0.2727368790656328
>>> timeit.timeit('"-".join(map(str, range(100)))', number=10000)
0.23702679807320237
A callable can also be passed from the :ref:`python-interface`:
>>> timeit.timeit(lambda: "-".join(map(str, range(100))), number=10000) 0.19665591977536678
Note however that :func:`.timeit` will automatically determine the number of repetitions only when the command-line interface is used. In the :ref:`timeit-examples` section you can find more advanced examples.
The module defines three convenience functions and a public class:
.. function:: timeit(stmt='pass', setup='pass', timer=<default timer>,\
number=1000000, globals=None,\
*,\
global_setup='pass')
Create a :class:`Timer` instance with the given statement, *setup* code,
*global_setup* global code and *timer* function and run its :meth:`.timeit`
method with the given *number* executions.
The optional *globals* argument specifies a namespace in which to execute the
code. Names created in the global setup are seen as globals in the setup and
testing code.
.. versionchanged:: 3.5
The optional *globals* parameter was added.
.. versionchanged:: next
The optional *global_setup* parameter was added.
.. function:: repeat(stmt='pass', setup='pass', timer=<default timer>,\
repeat=5, number=1000000, globals=None,\
*,\
global_setup='pass')
Create a :class:`Timer` instance with the given statement, *setup* code,
*global_setup* global code and *timer* function and run its :meth:`.repeat`
method with the given *repeat* count and *number* executions.
The optional *globals* argument specifies a namespace in which to execute the
code. Names created in the global setup are seen as globals in the setup and
testing code.
.. versionchanged:: 3.5
The optional *globals* parameter was added.
.. versionchanged:: 3.7
Default value of *repeat* changed from 3 to 5.
.. versionchanged:: next
The optional *global_setup* parameter was added.
.. function:: default_timer()
The default timer, which is always time.perf_counter(), returns float seconds.
An alternative, time.perf_counter_ns, returns integer nanoseconds.
.. versionchanged:: 3.3
:func:`time.perf_counter` is now the default timer.
When called as a program from the command line, the following form is used:
python -m timeit [-n N] [-r N] [-u U] [-s S] [-g S] [-p] [-v] [-h] [statement ...]
Where the following options are understood:
.. program:: timeit
.. option:: -n N, --number=N how many times to execute 'statement'
.. option:: -r N, --repeat=N how many times to repeat the timer (default 5)
.. option:: -s S, --setup=S statement to be executed once per timer repetition (default ``pass``)
.. option:: -g S, --global-setup=S statement to be executed once initially (default ``pass``) .. versionadded:: next
.. option:: -p, --process measure process time, not wallclock time, using :func:`time.process_time` instead of :func:`time.perf_counter`, which is the default .. versionadded:: 3.3
.. option:: -u, --unit=U specify a time unit for timer output; can select ``nsec``, ``usec``, ``msec``, or ``sec`` .. versionadded:: 3.5
.. option:: -v, --verbose print raw timing results; repeat for more digits precision
.. option:: -h, --help print a short usage message and exit
A multi-line statement may be given by specifying each line as a separate statement argument; indented lines are possible by enclosing an argument in quotes and using leading spaces. Multiple :option:`-s` options are treated similarly.
If :option:`-n` is not given, a suitable number of loops is calculated by trying increasing numbers from the sequence 1, 2, 5, 10, 20, 50, ... until the total time is at least 0.2 seconds.
:func:`default_timer` measurements can be affected by other programs running on the same machine, so the best thing to do when accurate timing is necessary is to repeat the timing a few times and use the best time. The :option:`-r` option is good for this; the default of 5 repetitions is probably enough in most cases. You can use :func:`time.process_time` to measure CPU time.
Note
There is a certain baseline overhead associated with executing a pass statement. The code here doesn't try to hide it, but you should be aware of it. The baseline overhead can be measured by invoking the program without arguments, and it might differ between Python versions.
It is possible to provide a setup statement that is executed only once at the beginning:
$ python -m timeit -s "text = 'sample string'; char = 'g'" "char in text"
5000000 loops, best of 5: 0.0877 usec per loop
$ python -m timeit -s "text = 'sample string'; char = 'g'" "text.find(char)"
1000000 loops, best of 5: 0.342 usec per loop
In the output, there are three fields. The loop count, which tells you how many times the statement body was run per timing loop repetition. The repetition count ('best of 5') which tells you how many times the timing loop was repeated, and finally the time the statement body took on average within the best repetition of the timing loop. That is, the time the fastest repetition took divided by the loop count.
>>> import timeit
>>> timeit.timeit('char in text', setup='text = "sample string"; char = "g"')
0.41440500499993504
>>> timeit.timeit('text.find(char)', setup='text = "sample string"; char = "g"')
1.7246671520006203
The same can be done using the :class:`Timer` class and its methods:
>>> import timeit
>>> t = timeit.Timer('char in text', setup='text = "sample string"; char = "g"')
>>> t.timeit()
0.3955516149999312
>>> t.repeat()
[0.40183617287970225, 0.37027556854118704, 0.38344867356679524, 0.3712595970846668, 0.37866875250654886]
The following examples show how to time expressions that contain multiple lines. Here we compare the cost of using :func:`hasattr` vs. :keyword:`try`/:keyword:`except` to test for missing and present object attributes:
$ python -m timeit "try:" " str.__bool__" "except AttributeError:" " pass"
20000 loops, best of 5: 15.7 usec per loop
$ python -m timeit "if hasattr(str, '__bool__'): pass"
50000 loops, best of 5: 4.26 usec per loop
$ python -m timeit "try:" " int.__bool__" "except AttributeError:" " pass"
200000 loops, best of 5: 1.43 usec per loop
$ python -m timeit "if hasattr(int, '__bool__'): pass"
100000 loops, best of 5: 2.23 usec per loop
>>> import timeit >>> # attribute is missing >>> s = """\ ... try: ... str.__bool__ ... except AttributeError: ... pass ... """ >>> timeit.timeit(stmt=s, number=100000) 0.9138244460009446 >>> s = "if hasattr(str, '__bool__'): pass" >>> timeit.timeit(stmt=s, number=100000) 0.5829014980008651 >>> >>> # attribute is present >>> s = """\ ... try: ... int.__bool__ ... except AttributeError: ... pass ... """ >>> timeit.timeit(stmt=s, number=100000) 0.04215312199994514 >>> s = "if hasattr(int, '__bool__'): pass" >>> timeit.timeit(stmt=s, number=100000) 0.08588060699912603
To give the :mod:`timeit` module access to functions you define, you can pass a setup parameter which contains an import statement:
def test():
"""Stupid test function"""
L = [i for i in range(100)]
if __name__ == '__main__':
import timeit
print(timeit.timeit("test()", setup="from __main__ import test"))
Another option is to pass :func:`globals` to the globals parameter, which will cause the code to be executed within your current global namespace. This can be more convenient than individually specifying imports:
def f(x):
return x**2
def g(x):
return x**4
def h(x):
return x**8
import timeit
print(timeit.timeit('[func(42) for func in (f,g,h)]', globals=globals()))