5. timeit — Measure execution time of small code snippets¶

5.1. Basic Examples¶

The following example shows how the 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 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

This can be achieved from the 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 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.

5.2. Python Interface¶

The module defines three convenience functions and a public class:

timeit.timeit(stmt='pass', setup='pass', timer=<default timer>, number=1000000)[source]¶

Create a Timer instance with the given statement, setup code and timer function and run its timeit() method with number executions.

New in version 2.6.

timeit.repeat(stmt='pass', setup='pass', timer=<default timer>, repeat=3, number=1000000)[source]¶

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.

New in version 2.6.

timeit.default_timer()¶

Define a default timer, in a platform-specific manner. On Windows, time.clock() has microsecond granularity, but time.time()‘s granularity is 1/60th of a second. On Unix, time.clock() has 1/100th of a second granularity, and time.time() is much more precise. On either platform, default_timer() measures wall clock time, not the CPU time. This means that other processes running on the same computer may interfere with the timing.

class timeit.Timer(stmt='pass', setup='pass', timer=<timer function>)[source]¶

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 timeit() method. The repeat() method is a convenience to call timeit() multiple times and return a list of results.

Changed in version 2.6: The stmt and setup parameters can now 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.

timeit(number=1000000)[source]¶

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 xrange(10): oct(i)', 'gc.enable()').timeit()

repeat(repeat=3, number=1000000)[source]¶

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)[source]¶

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 sys.stderr.

5.3. Command-Line Interface¶

When called as a program from the command line, the following form is used:

python -m timeit [-n N] [-r N] [-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)

-t, --time¶

use time.time() (default on all platforms but Windows)

-c, --clock¶

use time.clock() (default on Windows)

-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() measurations 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. On Unix, you can use time.clock() to measure CPU time.

5.4. 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.__nonzero__' 'except AttributeError:' '  pass'
100000 loops, best of 3: 15.7 usec per loop
$ python -m timeit 'if hasattr(str, "__nonzero__"): pass'
100000 loops, best of 3: 4.26 usec per loop

$ python -m timeit 'try:' '  int.__nonzero__' 'except AttributeError:' '  pass'
1000000 loops, best of 3: 1.43 usec per loop
$ python -m timeit 'if hasattr(int, "__nonzero__"): pass'
100000 loops, best of 3: 2.23 usec per loop

>>> import timeit
>>> # attribute is missing
>>> s = """\
... try:
...     str.__nonzero__
... 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.__nonzero__
... 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 = []
    for i in range(100):
        L.append(i)

if __name__ == '__main__':
    import timeit
    print(timeit.timeit("test()", setup="from __main__ import test"))