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Timeit in Python with Examples?
Python provides many ways to measure the time of execution for a piece of python code. One way is to use the python inbuilt time module and save the time before and after the execution of the program?
Python timeit
When some program is running, many processes also run in the background to make that code executable. The time module doesn’t count background processes execution time, however if you need precise time performance measurements timeit is the module to go for it.
The timeit module runs the code approximately 1 million times (default value) and take into account the minimum amount of time it took to run that piece of code.
Getting Python Execution time using timeit
We can use timeit module in many ways. One of the simplest ways is to use directly on the python CLI.
Example
We will start using python CLI with timeit module first. When CLI is used, we’ll notice that the module itself decides the number of repetitions performed for the same piece of code.
Example 1
C:\Users\rajesh>python -m timeit "'-'.join(str(n) for n in range(200))" 1000 loops, best of 3: 290 usec per loop C:\Users\rajesh>python -m timeit "'-'.join(str(n) for n in range(200))" 1000 loops, best of 3: 292 usec per loop C:\Users\rajesh>python -m timeit "'-'.join(str(n) for n in range(200))" 1000 loops, best of 3: 294 usec per loop
Example 2
Next we introduce timeit with another simple example but first we must import timeit module with “import timeit” statement. This is required incase we are not using command-line syntax like above.
#Import timeit module
import timeit
# The instructions being timed.
print('x' * 5)
print('x' + 'x' + 'x' + 'x' + 'x')
# Call timeit on the statements and print the time returned.
# ... Specify optional number of iterations.
print(timeit.timeit("y = 'x' * 3", number=10000000))
print(timeit.timeit("xy = 'x' + 'x' + 'x' + 'x' + 'x'", number = 10000000))Above we pass the statements in quoted strings to the timeit.timeit method and then we increase the iterations by specifying a number argument.
Output
First time on running above program, output generated:
xxxxx xxxxx 0.9041136896626635 0.7712796073957123
Second time on running above program, output generated:
xxxxx xxxxx 0.7317015874427751 0.7312688195585995
Third time on running above program, output generated:
xxxxx xxxxx 0.7240862411172824 0.7255863890794246
We performed our above program multiple times(3 times) and see there is reduction is execution time. One instead of doing it manually, let’s do the repetition through program:
#Import timeit module
import timeit
# Call timeit on the statements and print the time returned.
# ... Specify optional number of iterations.
print(timeit.repeat("y = 'x' * 3", number=10000000, repeat = 5))
print()
print(timeit.repeat("xy= 'x' + 'x' + 'x' + 'x' + 'x'", number = 10000000, repeat = 5))Output
[0.7303736343436382, 0.7213687552991258, 0.7362311105941466, 0.7293136666273243, 0.7278277732068212] [0.7388334197158559, 0.7378481457977326, 0.9486733868277772, 0.735295442480929, 0.7398226849056382]
Running Multiple statements using timeit module:
We can use multiple statements with timeit module. We separate each statements using a semicolon. Though it’s not the best way to write code but helps in specifying longer code fragments.
#Import timeit module
import timeit
# Use semicolon for multiple statements.
print(timeit.repeat("x = 2; x *= 2", number=100000000))
print(timeit.repeat("x = 1; x *= 4", number=100000000))Output
[24.859605879029118, 23.58795536845994, 23.95826726353284] [22.70639977603264, 21.380195994245724, 20.71523588130414]
Using Methods, setup in Timeit module:
We can use the custom methods in timeit by specifying a setup argument. In this argument, we specify an import statement that indicates the methods we invoke.
#Import timeit module
import timeit
def func1():
return 1
def func2():
return sum([-1, 0, 1, 1])
# Test methods.
print(func1())
print(func2())
# Pass setup argument to call methods.
print(timeit.repeat("func1()", setup="from __main__ import func1"))
print(timeit.repeat("func2()", setup="from __main__ import func2"))In above program we benchmark the func1() method against the func2() method.
Output
1 1 [0.44798489246658874, 0.4411512652046069, 0.44570416580426686] [1.583622557983199, 1.5712399227517881, 1.5469479030713984]
As func1() is doing less work, executed much faster.
Summary
Above we saw how we can measure the performance of small piece of python code using the timeit module using CLI and scripts as well.
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