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#15979: merge with 3.2.

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Ezio Melotti 2012-10-02 06:01:16 +03:00
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Doc/library/timeit.rst
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@ -14,113 +14,154 @@
--------------
This module provides a simple way to time small bits of Python code. It has both
command line as well as callable interfaces. 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.
The module defines the following public class:
a :ref:`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 *Python
Cookbook*, published by O'Reilly.
.. class:: Timer(stmt='pass', setup='pass', timer=<timer function>)
Basic Examples
--------------
Class for timing execution speed of small code snippets.
The following example shows how the :ref:`command-line-interface`
can be used to compare three different expressions:
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.
.. code-block:: sh
To measure the execution time of the first statement, use the :meth:`Timer.timeit`
method. The :meth:`repeat` method is a convenience to call :meth:`.timeit`
multiple times and return a list of results.
$ python -m timeit '"-".join(str(n) for n in range(100))'
10000 loops, best of 3: 40.3 usec per loop
$ python -m timeit '"-".join([str(n) for n in range(100)])'
10000 loops, best of 3: 33.4 usec per loop
$ python -m timeit '"-".join(map(str, range(100)))'
10000 loops, best of 3: 25.2 usec per loop
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 :meth:`.timeit`. Note that the timing overhead is a
little larger in this case because of the extra function calls.
This can be achieved from the :ref:`python-interface` with::
>>> import timeit
>>> timeit.timeit('"-".join(str(n) for n in range(100))', number=10000)
0.8187260627746582
>>> timeit.timeit('"-".join([str(n) for n in range(100)])', number=10000)
0.7288308143615723
>>> timeit.timeit('"-".join(map(str, range(100)))', number=10000)
0.5858950614929199
Note however that :mod:`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.
.. method:: Timer.print_exc(file=None)
.. _python-interface:
Helper to print a traceback from the timed code.
Python Interface
----------------
Typical use::
t = Timer(...) # outside the try/except
try:
t.timeit(...) # or t.repeat(...)
except:
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``.
The module defines three convenience functions and a public class:
.. method:: Timer.repeat(repeat=3, number=1000000)
.. function:: timeit(stmt='pass', setup='pass', timer=<default timer>, number=1000000)
Call :meth:`.timeit` a few times.
This is a convenience function that calls the :meth:`.timeit` repeatedly,
returning a list of results. The first argument specifies how many times to
call :meth:`.timeit`. The second argument specifies the *number* argument for
:meth:`.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 :func:`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.
Create a :class:`Timer` instance with the given statement, *setup* code and
*timer* function and run its :meth:`.timeit` method with *number* executions.
.. method:: Timer.timeit(number=1000000)
.. function:: repeat(stmt='pass', setup='pass', timer=<default timer>, repeat=3, 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, :meth:`.timeit` temporarily turns off :term:`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()
The module also defines three convenience functions:
Create a :class:`Timer` instance with the given statement, *setup* code and
*timer* function and run its :meth:`.repeat` method with the given *repeat*
count and *number* executions.
.. function:: default_timer()
The default timer, which is always :func:`time.perf_counter`.
.. function:: repeat(stmt='pass', setup='pass', timer=<default timer>, repeat=3, number=1000000)
Create a :class:`Timer` instance with the given statement, setup code and timer
function and run its :meth:`repeat` method with the given repeat count and
*number* executions.
.. versionchanged:: 3.3
:func:`time.perf_counter` is now the default timer.
.. function:: timeit(stmt='pass', setup='pass', timer=<default timer>, number=1000000)
.. class:: Timer(stmt='pass', setup='pass', timer=<timer function>)
Create a :class:`Timer` instance with the given statement, setup code and timer
function and run its :meth:`.timeit` method with *number* executions.
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.
To measure the execution time of the first statement, use the :meth:`.timeit`
method. The :meth:`.repeat` method is a convenience to call :meth:`.timeit`
multiple times and return a list of results.
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 :meth:`.timeit`. Note that the timing overhead is a
little larger in this case because of the extra function calls.
Command Line Interface
.. method:: Timer.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, :meth:`.timeit` temporarily turns off :term:`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()
.. method:: Timer.repeat(repeat=3, number=1000000)
Call :meth:`.timeit` a few times.
This is a convenience function that calls the :meth:`.timeit` repeatedly,
returning a list of results. The first argument specifies how many times
to call :meth:`.timeit`. The second argument specifies the *number*
argument for :meth:`.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 :func:`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.
.. method:: Timer.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:
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 :data:`sys.stderr`.
.. _command-line-interface:
Command-Line Interface
----------------------
When called as a program from the command line, the following form is used::
@ -184,25 +225,53 @@ most cases. You can use :func:`time.process_time` to measure CPU time.
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.
baseline overhead can be measured by invoking the program without arguments,
and it might differ between Python versions.
The baseline overhead differs between Python versions! Also, to fairly compare
older Python versions to Python 2.3, you may want to use Python's :option:`-O`
option for the older versions to avoid timing ``SET_LINENO`` instructions.
.. _timeit-examples:
Examples
--------
Here are two example sessions (one using the command line, one using the module
interface) that compare the cost of using :func:`hasattr` vs.
:keyword:`try`/:keyword:`except` to test for missing and present object
attributes. ::
It is possible to provide a setup statement that is executed only once at the beginning:
.. code-block:: sh
$ 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 :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()
[0.40193588800002544, 0.3960157959998014, 0.39594301399984033]
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:
.. code-block:: sh
$ 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'
@ -211,36 +280,32 @@ attributes. ::
::
>>> import timeit
>>> # attribute is missing
>>> s = """\
... try:
... str.__bool__
... except AttributeError:
... pass
... """
>>> t = timeit.Timer(stmt=s)
>>> print("%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000))
17.09 usec/pass
>>> s = """\
... if hasattr(str, '__bool__'): pass
... """
>>> t = timeit.Timer(stmt=s)
>>> print("%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000))
4.85 usec/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
... """
>>> t = timeit.Timer(stmt=s)
>>> print("%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000))
1.97 usec/pass
>>> s = """\
... if hasattr(int, '__bool__'): pass
... """
>>> t = timeit.Timer(stmt=s)
>>> print("%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000))
3.15 usec/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::
@ -250,7 +315,5 @@ To give the :mod:`timeit` module access to functions you define, you can pass a
L = [i for i in range(100)]
if __name__ == '__main__':
from timeit import Timer
t = Timer("test()", "from __main__ import test")
print(t.timeit())
import timeit
print(timeit.timeit("test()", setup="from __main__ import test"))

2
Misc/NEWS
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@ -110,6 +110,8 @@ Documentation
- Issue #15533: Clarify docs and add tests for `subprocess.Popen()`'s cwd
argument.
- Issue #15979: Improve timeit documentation.
- Issue #16036: Improve documentation of built-in `int()`'s signature and
arguments.