Source code:Lib/timeit.py
This module provides a simple way to time small bits of Python code. It has bothaCommand-Line Interface as well as acallableone. It avoids a number of common traps for measuring execution times.See also Tim Peters’ introduction to the “Algorithms” chapter in thePythonCookbook, published by O’Reilly.
The following example shows how theCommand-Line Interfacecan 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 thePython Interface with:
>>>importtimeit>>>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 thattimeit will automatically determine the number ofrepetitions only when the command-line interface is used. In theExamples section you can find more advanced examples.
The module defines three convenience functions and a public class:
Create aTimer instance with the given statement,setup code andtimer function and run itstimeit() method withnumber executions.
Create aTimer instance with the given statement,setup code andtimer function and run itsrepeat() method with the givenrepeatcount andnumber executions.
The default timer, which is alwaystime.perf_counter().
Changed in version 3.3:time.perf_counter() is now the default timer.
Class for timing execution speed of small code snippets.
The constructor takes a statement to be timed, an additional statement usedfor setup, and a timer function. Both statements default to'pass';the timer function is platform-dependent (see the module doc string).stmt andsetup 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 thetimeit()method. Therepeat() method is a convenience to calltimeit()multiple times and return a list of results.
Thestmt andsetup parameters can also take objects that are callablewithout arguments. This will embed calls to them in a timer function thatwill then be executed bytimeit(). Note that the timing overhead is alittle larger in this case because of the extra function calls.
Timenumber executions of the main statement. This executes the setupstatement once, and then returns the time it takes to execute the mainstatement a number of times, measured in seconds as a float.The argument is the number of times through the loop, defaulting to onemillion. The main statement, the setup statement and the timer functionto be used are passed to the constructor.
Note
By default,timeit() temporarily turns offgarbagecollection during the timing. The advantage of this approach is thatit makes independent timings more comparable. This disadvantage isthat GC may be an important component of the performance of thefunction being measured. If so, GC can be re-enabled as the firststatement in thesetup string. For example:
timeit.Timer('for i in range(10): oct(i)','gc.enable()').timeit()
Calltimeit() a few times.
This is a convenience function that calls thetimeit() repeatedly,returning a list of results. The first argument specifies how many timesto calltimeit(). The second argument specifies thenumberargument fortimeit().
Note
It’s tempting to calculate mean and standard deviation from the resultvector and report these. However, this is not very useful.In a typical case, the lowest value gives a lower bound for how fastyour machine can run the given code snippet; higher values in theresult vector are typically not caused by variability in Python’sspeed, but by other processes interfering with your timing accuracy.So themin() of the result is probably the only number youshould be interested in. After that, you should look at the entirevector and apply common sense rather than statistics.
Helper to print a traceback from the timed code.
Typical use:
t=Timer(...)# outside the try/excepttry:t.timeit(...)# or t.repeat(...)except:t.print_exc()
The advantage over the standard traceback is that source lines in thecompiled template will be displayed. The optionalfile argument directswhere the traceback is sent; it defaults tosys.stderr.
When called as a program from the command line, the following form is used:
python-mtimeit[-nN][-rN][-sS][-t][-c][-h][statement...]
Where the following options are understood:
how many times to execute ‘statement’
how many times to repeat the timer (default 3)
statement to be executed once initially (defaultpass)
measure process time, not wallclock time, usingtime.process_time()instead oftime.perf_counter(), which is the default
New in version 3.3.
usetime.time() (deprecated)
usetime.clock() (deprecated)
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 separatestatement argument; indented lines are possible by enclosing an argument inquotes and using leading spaces. Multiple-s options are treatedsimilarly.
If-n is not given, a suitable number of loops is calculated by tryingsuccessive powers of 10 until the total time is at least 0.2 seconds.
default_timer() measurements can be affected by other programs running onthe same machine, so the best thing to do when accurate timing is necessary isto repeat the timing a few times and use the best time. The-roption is good for this; the default of 3 repetitions is probably enough inmost cases. You can usetime.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. Thebaseline overhead can be measured by invoking the program without arguments,and it might differ between Python versions.
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
>>>importtimeit>>>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 theTimer class and its methods:
>>>importtimeit>>>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 usinghasattr() vs.try/exceptto 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
>>>importtimeit>>># 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 thetimeit module access to functions you define, you can pass asetup parameter which contains an import statement:
deftest():"""Stupid test function"""L=[iforiinrange(100)]if__name__=='__main__':importtimeitprint(timeit.timeit("test()",setup="from __main__ import test"))
27.6.trace — Trace or track Python statement execution
Enter search terms or a module, class or function name.
