unix – Is there any simple way to benchmark Python script?

Have a look at timeit, the python profiler and pycallgraph. Also make sure to have a look at the comment below by nikicc mentioning SnakeViz. It gives you yet another visualisation of profiling data which can be helpful.

timeit

def test():
    Stupid test function
    lst = []
    for i in range(100):
        lst.append(i)

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

    # For Python>=3.5 one can also write:
    print(timeit.timeit(test(), globals=locals()))

Essentially, you can pass it python code as a string parameter, and it will run in the specified amount of times and prints the execution time. The important bits from the docs:

timeit.timeit(stmt=pass, setup=pass, timer=<default timer>, number=1000000, globals=None)
Create a Timer instance with the given statement, setup
code and timer function and run its timeit method with
number executions. The optional globals argument specifies a namespace in which to execute the code.

… and:

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, 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()

Profiling

Profiling will give you a much more detailed idea about whats going on. Heres the instant example from the official docs:

import cProfile
import re
cProfile.run(re.compile(foo|bar))

Which will give you:

      197 function calls (192 primitive calls) in 0.002 seconds

Ordered by: standard name

ncalls  tottime  percall  cumtime  percall filename:lineno(function)
     1    0.000    0.000    0.001    0.001 <string>:1(<module>)
     1    0.000    0.000    0.001    0.001 re.py:212(compile)
     1    0.000    0.000    0.001    0.001 re.py:268(_compile)
     1    0.000    0.000    0.000    0.000 sre_compile.py:172(_compile_charset)
     1    0.000    0.000    0.000    0.000 sre_compile.py:201(_optimize_charset)
     4    0.000    0.000    0.000    0.000 sre_compile.py:25(_identityfunction)
   3/1    0.000    0.000    0.000    0.000 sre_compile.py:33(_compile)

Both of these modules should give you an idea about where to look for bottlenecks.

Also, to get to grips with the output of profile, have a look at this post

pycallgraph

NOTE pycallgraph has been officially abandoned since Feb. 2018. As of Dec. 2020 it was still working on Python 3.6 though. As long as there are no core changes in how python exposes the profiling API it should remain a helpful tool though.

This module uses graphviz to create callgraphs like the following:

callgraph

You can easily see which paths used up the most time by colour. You can either create them using the pycallgraph API, or using a packaged script:

pycallgraph graphviz -- ./mypythonscript.py

The overhead is quite considerable though. So for already long-running processes, creating the graph can take some time.

I use a simple decorator to time the func

import time

def st_time(func):
    
        st decorator to calculate the total time of a func
    

    def st_func(*args, **keyArgs):
        t1 = time.time()
        r = func(*args, **keyArgs)
        t2 = time.time()
        print(Function=%s, Time=%s % (func.__name__, t2 - t1))
        return r

    return st_func

unix – Is there any simple way to benchmark Python script?

The timeit module was slow and weird, so I wrote this:

def timereps(reps, func):
    from time import time
    start = time()
    for i in range(0, reps):
        func()
    end = time()
    return (end - start) / reps

Example:

import os
listdir_time = timereps(10000, lambda: os.listdir(/))
print python can do %d os.listdir(/) per second % (1 / listdir_time)

For me, it says:

python can do 40925 os.listdir(/) per second

This is a primitive sort of benchmarking, but its good enough.