As discussed previously, profiling means measuring the execution time of a program. We are going to use the cProfile Python module for profiling a program.

Now, we will write a cprof_example.py script and write the following code in it:

mul_value = 0
def mul_numbers( num1, num2 ):
            mul_value = num1 * num2;
            print ("Local Value: ", mul_value)
            return mul_value
mul_numbers( 58, 77 )
print ("Global Value: ", mul_value)

Run the program and you will see the output as follows:

student@ubuntu:~$ python3 -m cProfile cprof_example.py
Local Value:  4466
Global Value:  0
         6 function calls in 0.000 seconds
   Ordered by: standard name
 
   ncalls  tottime  percall  cumtime  percall filename:lineno(function)
        1    0.000    0.000    0.000    0.000 cprof_example.py:1(<module>)
        1    0.000    0.000    0.000    0.000 cprof_example.py:2(mul_numbers)
        1    0.000    0.000    0.000    0.000 {built-in method builtins.exec}
        2    0.000    0.000    0.000    0.000 {built-in method builtins.print}
        1    0.000    0.000    0.000    0.000 {method 'disable' of '_lsprof.Profiler' objects}

So, using cProfile, all functions that are called will get printed with the time spent on each function. Now, we will see what these column headings mean:

• ncalls: Number of calls
• tottime: Total time spent in the given function
• percall: Quotient of tottime divided by ncalls
• cumtime: Cumulative time spent in this and all subfunctions
• percall: Quotient of cumtime divided by primitive calls
• filename:lineno(function): Provides the respective data of each function