timeit – Time the execution of small bits of Python code.

Purpose:Time the execution of small bits of Python code.
Available In:2.3

The timeit module provides a simple interface for determining the execution time of small bits of Python code. It uses a platform-specific time function to provide the most accurate time calculation possible. It reduces the impact of startup or shutdown costs on the time calculation by executing the code repeatedly.

Module Contents

timeit defines a single public class, Timer. The constructor for Timer takes a statement to be timed, and a setup statement (to initialize variables, for example). The Python statements should be strings and can include embedded newlines.

The timeit() method runs the setup statement one time, then executes the primary statement repeatedly and returns the amount of time which passes. The argument to timeit() controls how many times to run the statement; the default is 1,000,000.

Basic Example

To illustrate how the various arguments to Timer are used, here is a simple example which prints an identifying value when each statement is executed:

import timeit

# using setitem
t = timeit.Timer("print 'main statement'", "print 'setup'")

print 'TIMEIT:'
print t.timeit(2)

print 'REPEAT:'
print t.repeat(3, 2)

When run, the output is:

$ python timeit_example.py

TIMEIT:
setup
main statement
main statement
1.90734863281e-06
REPEAT:
setup
main statement
main statement
setup
main statement
main statement
setup
main statement
main statement
[9.5367431640625e-07, 9.5367431640625e-07, 1.1920928955078125e-06]

When called, timeit() runs the setup statement one time, then calls the main statement count times. It returns a single floating point value representing the amount of time it took to run the main statement count times.

When repeat() is used, it calls timeit() severeal times (3 in this case) and all of the responses are returned in a list.

Storing Values in a Dictionary

For a more complex example, let’s compare the amount of time it takes to populate a dictionary with a large number of values using a variety of methods. First, a few constants are needed to configure the Timer. We’ll be using a list of tuples containing strings and integers. The Timer will be storing the integers in a dictionary using the strings as keys.

# {{{cog include('timeit/timeit_dictionary.py', 'header')}}}
import timeit
import sys

# A few constants
range_size=1000
count=1000
setup_statement="l = [ (str(x), x) for x in range(%d) ]; d = {}" % range_size
# {{{end}}}

Next, we can define a short utility function to print the results in a useful format. The timeit() method returns the amount of time it takes to execute the statement repeatedly. The output of show_results() converts that into the amount of time it takes per iteration, and then further reduces the value to the amount of time it takes to store one item in the dictionary (as averages, of course).

# {{{cog include('timeit/timeit_dictionary.py', 'show_results')}}}
def show_results(result):
    "Print results in terms of microseconds per pass and per item."
    global count, range_size
    per_pass = 1000000 * (result / count)
    print '%.2f usec/pass' % per_pass,
    per_item = per_pass / range_size
    print '%.2f usec/item' % per_item

print "%d items" % range_size
print "%d iterations" % count
print
# {{{end}}}

To establish a baseline, the first configuration tested will use __setitem__(). All of the other variations avoid overwriting values already in the dictionary, so this simple version should be the fastest.

Notice that the first argument to Timer is a multi-line string, with indention preserved to ensure that it parses correctly when run. The second argument is a constant established above to initialize the list of values and the dictionary.

# {{{cog include('timeit/timeit_dictionary.py', 'setitem')}}}
# Using __setitem__ without checking for existing values first
print '__setitem__:\t',
sys.stdout.flush()
# using setitem
t = timeit.Timer("""
for s, i in l:
    d[s] = i
""",
setup_statement)
show_results(t.timeit(number=count))
# {{{end}}}

The next variation uses setdefault() to ensure that values already in the dictionary are not overwritten.

# {{{cog include('timeit/timeit_dictionary.py', 'setdefault')}}}
# Using setdefault
print 'setdefault:\t',
sys.stdout.flush()
t = timeit.Timer("""
for s, i in l:
    d.setdefault(s, i)
""",
setup_statement)
show_results(t.timeit(number=count))
# {{{end}}}

Another way to avoid overwriting existing values is to use has_key() to check the contents of the dictionary explicitly.

# {{{cog include('timeit/timeit_dictionary.py', 'has_key')}}}
# Using has_key
print 'has_key:\t',
sys.stdout.flush()
# using setitem
t = timeit.Timer("""
for s, i in l:
    if not d.has_key(s):
        d[s] = i
""",
setup_statement)
show_results(t.timeit(number=count))
# {{{end}}}

Or by adding the value only if we receive a KeyError exception when looking for the existing value.

# {{{cog include('timeit/timeit_dictionary.py', 'exception')}}}
# Using exceptions
print 'KeyError:\t',
sys.stdout.flush()
# using setitem
t = timeit.Timer("""
for s, i in l:
    try:
        existing = d[s]
    except KeyError:
        d[s] = i
""",
setup_statement)
show_results(t.timeit(number=count))
# {{{end}}}

And the last method we will test is the (relatively) new form using “in” to determine if a dictionary has a particular key.

# {{{cog include('timeit/timeit_dictionary.py', 'in')}}}
# Using "in"
print '"not in":\t',
sys.stdout.flush()
# using setitem
t = timeit.Timer("""
for s, i in l:
    if s not in d:
        d[s] = i
""",
setup_statement)
show_results(t.timeit(number=count))
# {{{end}}}

When run, the script produces output similar to this:

$ python timeit_dictionary.py

1000 items
1000 iterations

__setitem__:    107.40 usec/pass 0.11 usec/item
setdefault:     228.97 usec/pass 0.23 usec/item
has_key:        183.76 usec/pass 0.18 usec/item
KeyError:       120.74 usec/pass 0.12 usec/item
"not in":       92.42 usec/pass 0.09 usec/item

Those times are for a MacBook Pro running Python 2.6. Your times will be different. Experiment with the range_size and count variables, since different combinations will produce different results.

From the Command Line

In addition to the programmatic interface, timeit provides a command line interface for testing modules without instrumentation.

To run the module, use the new -m option to find the module and treat it as the main program:

$ python -m timeit

For example, to get help:

$ python -m timeit -h

Tool for measuring execution time of small code snippets.

This module 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.

Library usage: see the Timer class.

Command line usage:
    python timeit.py [-n N] [-r N] [-s S] [-t] [-c] [-h] [--] [statement]

Options:
  -n/--number N: how many times to execute 'statement' (default: see below)
  -r/--repeat N: how many times to repeat the timer (default 3)
  -s/--setup S: statement to be executed once initially (default 'pass')
  -t/--time: use time.time() (default on Unix)
  -c/--clock: use time.clock() (default on Windows)
  -v/--verbose: print raw timing results; repeat for more digits precision
  -h/--help: print this usage message and exit
  --: separate options from statement, use when statement starts with -
  statement: statement to be timed (default 'pass')

A multi-line statement may be given by specifying each line as a
separate 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.

The difference in default timer function is because on Windows,
clock() has microsecond granularity but time()'s granularity is 1/60th
of a second; on Unix, clock() has 1/100th of a second granularity and
time() is much more precise.  On either platform, the default timer
functions measure wall clock time, not the CPU time.  This means that
other processes running on the same computer may interfere with the
timing.  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 clock() 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.

The baseline overhead differs between Python versions!  Also, to
fairly compare older Python versions to Python 2.3, you may want to
use python -O for the older versions to avoid timing SET_LINENO
instructions.

The statement argument works a little differently than the argument to Timer. Instead of one long string, you pass each line of the instructions as a separate command line argument. To indent lines (such as inside a loop), embed spaces in the string by enclosing the whole thing in quotes. For example:

$ python -m timeit -s "d={}" "for i in range(1000):" "  d[str(i)] = i"

1000 loops, best of 3: 289 usec per loop

It is also possible to define a function with more complex code, then import the module and call the function from the command line:

def test_setitem(range_size=1000):
    l = [ (str(x), x) for x in range(range_size) ]
    d = {}
    for s, i in l:
        d[s] = i

Then to run the test:

$ python -m timeit "import timeit_setitem; timeit_setitem.test_setitem()\
"

1000 loops, best of 3: 417 usec per loop

See also

timeit
Standard library documentation for this module.
profile
The profile module is also useful for performance analysis.
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