27.6. timeit — Measure execution time of small code snippets

New in version 2.3.

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:

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

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.


Helper to print a traceback from the timed code.

Typical use:

t = Timer(...)       # outside the try/except
    t.timeit(...)    # or t.repeat(...)

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.

Timer.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().


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.


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.


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()

Starting with version 2.6, the module also defines two convenience functions:

timeit.repeat(stmt[, setup[, timer[, repeat=3[, number=1000000]]]])

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.timeit(stmt[, setup[, timer[, number=1000000]]])

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.

27.6.1. 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')
use time.time() (default on all platforms but Windows)
use time.clock() (default on Windows)
print raw timing results; repeat for more digits precision
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.

The default timer function is platform dependent. 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, 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 time.clock() 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.

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 -O option for the older versions to avoid timing SET_LINENO instructions.

27.6.2. Examples

Here are two example sessions (one using the command line, one using the module interface) that compare the cost of using hasattr() vs. try/except to test for missing and present object attributes.

% timeit.py 'try:' '  str.__nonzero__' 'except AttributeError:' '  pass'
100000 loops, best of 3: 15.7 usec per loop
% timeit.py 'if hasattr(str, "__nonzero__"): pass'
100000 loops, best of 3: 4.26 usec per loop
% timeit.py 'try:' '  int.__nonzero__' 'except AttributeError:' '  pass'
1000000 loops, best of 3: 1.43 usec per loop
% timeit.py 'if hasattr(int, "__nonzero__"): pass'
100000 loops, best of 3: 2.23 usec per loop
>>> import timeit
>>> s = """\
... try:
...     str.__nonzero__
... 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, '__nonzero__'): pass
... """
>>> t = timeit.Timer(stmt=s)
>>> print "%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000)
4.85 usec/pass
>>> s = """\
... try:
...     int.__nonzero__
... 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, '__nonzero__'): pass
... """
>>> t = timeit.Timer(stmt=s)
>>> print "%.2f usec/pass" % (1000000 * t.timeit(number=100000)/100000)
3.15 usec/pass

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):

if __name__=='__main__':
    from timeit import Timer
    t = Timer("test()", "from __main__ import test")
    print t.timeit()