| Python 3.6.5 Documentation > "timeit" — Measure execution time of small code snippets
"timeit" — Measure execution time of small code snippets
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**Source code:** Lib/timeit.py
======================================================================
This module provides a simple way to time small bits of Python code.
It has both a Command-Line Interface as well as a callable one. It
avoids a number of common traps for measuring execution times. See
also Tim Peters’ introduction to the “Algorithms” chapter in the
*Python Cookbook*, published by O’Reilly.
Basic Examples
==============
The following example shows how the Command-Line Interface can be used
to compare three different expressions:
$ python3 -m timeit '"-".join(str(n) for n in range(100))'
10000 loops, best of 3: 30.2 usec per loop
$ python3 -m timeit '"-".join([str(n) for n in range(100)])'
10000 loops, best of 3: 27.5 usec per loop
$ python3 -m timeit '"-".join(map(str, range(100)))'
10000 loops, best of 3: 23.2 usec per loop
This can be achieved from the 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
Note however that "timeit" will automatically determine the number of
repetitions only when the command-line interface is used. In the
Examples section you can find more advanced examples.
Python Interface
================
The module defines three convenience functions and a public class:
timeit.timeit(stmt='pass', setup='pass', timer=<default timer>, number=1000000, globals=None)
Create a "Timer" instance with the given statement, *setup* code
and *timer* function and run its "timeit()" method with *number*
executions. The optional *globals* argument specifies a namespace
in which to execute the code.
Changed in version 3.5: The optional *globals* parameter was added.
timeit.repeat(stmt='pass', setup='pass', timer=<default timer>, repeat=3, number=1000000, globals=None)
Create a "Timer" instance with the given statement, *setup* code
and *timer* function and run its "repeat()" method with the given
*repeat* count and *number* executions. The optional *globals*
argument specifies a namespace in which to execute the code.
Changed in version 3.5: The optional *globals* parameter was added.
timeit.default_timer()
The default timer, which is always "time.perf_counter()".
Changed in version 3.3: "time.perf_counter()" is now the default
timer.
class timeit.Timer(stmt='pass', setup='pass', timer=<timer function>, globals=None)
Class for timing execution speed of small code snippets.
The constructor takes a statement to be timed, an additional
statement used for setup, and a timer function. Both statements
default to "'pass'"; the timer function is platform-dependent (see
the module doc string). *stmt* and *setup* may also contain
multiple statements separated by ";" or newlines, as long as they
don’t contain multi-line string literals. The statement will by
default be executed within timeit’s namespace; this behavior can be
controlled by passing a namespace to *globals*.
To measure the execution time of the first statement, use the
"timeit()" method. The "repeat()" and "autorange()" methods are
convenience methods to call "timeit()" multiple times.
The execution time of *setup* is excluded from the overall timed
execution run.
The *stmt* and *setup* parameters can also take objects that are
callable without arguments. This will embed calls to them in a
timer function that will then be executed by "timeit()". Note that
the timing overhead is a little larger in this case because of the
extra function calls.
Changed in version 3.5: The optional *globals* parameter was added.
timeit(number=1000000)
Time *number* executions of the main statement. This executes
the setup statement once, and then returns the time it takes to
execute the main statement a number of times, measured in
seconds as a float. The argument is the number of times through
the loop, defaulting to one million. The main statement, the
setup statement and the timer function to be used are passed to
the constructor.
Note: By default, "timeit()" temporarily turns off *garbage
collection* during the timing. The advantage of this approach
is that it makes independent timings more comparable. This
disadvantage is that GC may be an important component of the
performance of the function being measured. If so, GC can be
re-enabled as the first statement in the *setup* string. For
example:
timeit.Timer('for i in range(10): oct(i)', 'gc.enable()').timeit()
autorange(callback=None)
Automatically determine how many times to call "timeit()".
This is a convenience function that calls "timeit()" repeatedly
so that the total time >= 0.2 second, returning the eventual
(number of loops, time taken for that number of loops). It calls
"timeit()" with *number* set to successive powers of ten (10,
100, 1000, …) up to a maximum of one billion, until the time
taken is at least 0.2 second, or the maximum is reached.
If *callback* is given and is not "None", it will be called
after each trial with two arguments: "callback(number,
time_taken)".
New in version 3.6.
repeat(repeat=3, number=1000000)
Call "timeit()" a few times.
This is a convenience function that calls the "timeit()"
repeatedly, returning a list of results. The first argument
specifies how many times to call "timeit()". The second
argument specifies the *number* argument for "timeit()".
Note: It’s tempting to calculate mean and standard deviation
from the result vector and report these. However, this is not
very useful. In a typical case, the lowest value gives a lower
bound for how fast your machine can run the given code
snippet; higher values in the result vector are typically not
caused by variability in Python’s speed, but by other
processes interfering with your timing accuracy. So the
"min()" of the result is probably the only number you should
be interested in. After that, you should look at the entire
vector and apply common sense rather than statistics.
print_exc(file=None)
Helper to print a traceback from the timed code.
Typical use:
t = Timer(...) # outside the try/except
try:
t.timeit(...) # or t.repeat(...)
except Exception:
t.print_exc()
The advantage over the standard traceback is that source lines
in the compiled template will be displayed. The optional *file*
argument directs where the traceback is sent; it defaults to
"sys.stderr".
Command-Line Interface
======================
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] [-t] [-c] [-h] [statement ...]
Where the following options are understood:
-n N, --number=N
how many times to execute ‘statement’
-r N, --repeat=N
how many times to repeat the timer (default 3)
-s S, --setup=S
statement to be executed once initially (default "pass")
-p, --process
measure process time, not wallclock time, using
"time.process_time()" instead of "time.perf_counter()", which is
the default
New in version 3.3.
-t, --time
use "time.time()" (deprecated)
-u, --unit=U
specify a time unit for timer output; can select usec, msec, or
sec
New in version 3.5.
-c, --clock
use "time.clock()" (deprecated)
-v, --verbose
print raw timing results; repeat for more digits precision
-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 "-s" options
are treated similarly.
If "-n" is not given, a suitable number of loops is calculated by
trying successive powers of 10 until the total time is at least 0.2
seconds.
"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 "-r" option is good for this; the default of 3
repetitions is probably enough in most cases. You can use
"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.
Examples
========
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'
10000000 loops, best of 3: 0.0877 usec per loop
$ python -m timeit -s 'text = "sample string"; char = "g"' 'text.find(char)'
1000000 loops, best of 3: 0.342 usec per loop
>>> 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 "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()
[0.40193588800002544, 0.3960157959998014, 0.39594301399984033]
The following examples show how to time expressions that contain
multiple lines. Here we compare the cost of using "hasattr()" vs.
"try"/"except" to test for missing and present object attributes:
$ python -m timeit 'try:' ' str.__bool__' 'except AttributeError:' ' pass'
100000 loops, best of 3: 15.7 usec per loop
$ python -m timeit 'if hasattr(str, "__bool__"): pass'
100000 loops, best of 3: 4.26 usec per loop
$ python -m timeit 'try:' ' int.__bool__' 'except AttributeError:' ' pass'
1000000 loops, best of 3: 1.43 usec per loop
$ python -m timeit 'if hasattr(int, "__bool__"): pass'
100000 loops, best of 3: 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 "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 "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()))
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