Python | Timing and Profiling the program

Last Updated : 12 Jun, 2019

Problems – To find where the program spends its time and make timing measurements.

To simply time the whole program, it’s usually easy enough to use something like the Unix time command as shown below.

Code #1 : Command to time the whole program

bash % time python3 someprogram.py 
real 0m13.937s
user 0m12.162s
sys 0m0.098s
bash %

On the other extreme, to have a detailed report showing what the program is doing, cProfile module is used.

bash % python3 -m cProfile someprogram.py

Output :

Ordered by: standard name
    ncalls tottime percall cumtime percall filename:lineno(function)
    263169 0.080 0.000 0.080 0.000 someprogram.py:16(frange)
    513 0.001 0.000 0.002 0.000 someprogram.py:30(generate_mandel)
    262656 0.194 0.000 15.295 0.000 someprogram.py:32()
    1 0.036 0.036 16.077 16.077 someprogram.py:4()
    262144 15.021 0.000 15.021 0.000 someprogram.py:4(in_mandelbrot)
    1 0.000 0.000 0.000 0.000 os.py:746(urandom)
    1 0.000 0.000 0.000 0.000 png.py:1056(_readable)
    1 0.000 0.000 0.000 0.000 png.py:1073(Reader)
    1 0.227 0.227 0.438 0.438 png.py:163()
    512 0.010 0.000 0.010 0.000 png.py:200(group)

More often than not, profiling the code lies somewhere in between these two extremes. For example, if one already knows that the code spends most of its time in a few selected functions. For selected profiling of functions, a short decorator can be useful.

Code #3: Using short decorator for selected profiling of functions

# abc.py 
  
import time 
from functools import wraps 
  
def timethis(func): 
    @wraps(func) 
    def wrapper(*args, **kwargs): 
        start = time.perf_counter() 
        r = func(*args, **kwargs) 
        end = time.perf_counter() 
        print('{}.{} : {}'.format(func.__module__, func.__name__, end - start)) 
        return r 
    return wrapper 

To use the decorator, simply place it in front of a function definition to get timings from it as shown in the code below.
Code #4 :

@abc
def countdown(n): 
    while n > 0: 
    n -= 1
  
countdown(10000000) 

Output :

__main__.countdown : 0.803001880645752

Code #5: Defining a context manager to time a block of statements.

from contextlib import contextmanager 
  
def timeblock(label): 
    start = time.perf_counter() 
    try: 
        yield
    finally: 
        end = time.perf_counter() 
        print('{} : {}'.format(label, end - start)) 

Code #6: How the context manager works

with timeblock('counting'): 
    n = 10000000
    while n > 0: 
        n -= 1

Output :

counting : 1.5551159381866455

Code #7 : Using timeit module to study the performance of small code fragments

from timeit import timeit 
print (timeit('math.sqrt(2)', 'import math'), "\n") 
  
print (timeit('sqrt(2)', 'from math import sqrt')) 

Output :

0.1432319980012835
0.10836604500218527

timeit works by executing the statement specified in the first argument a million times and measuring the time.

Python | Basic Program Crash Debugging

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Python | Timing and Profiling the program

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