Published on

Using the timeit Module to Identify Bottlenecks and Improve Performance

Authors

Introduction

The timeit module in Python is a built-in module that allows to measure time of code snnippets. It is very useful tool for comparing the performance of different approaches. It can be used to identify bottlenecks in the code and improve the performance of the code.

How to use it?

Below is a program for sorting a list of 1000 random numbers using the sort() method of the list class.

import random

def sort_list():
    random_list = [random.randint(0, 1000) for i in range(1000)]
    random_list.sort()

if __name__ == "__main__":
    sort_list()

Now, let's use the timeit module to measure the time taken by the sort_list() function to sort the list.

import timeit

setup = """
import random

def sort_list():
    random_list = [random.randint(0, 1000) for i in range(1000)]
    random_list.sort()
"""

print(timeit.timeit("sort_list()", setup=setup, number=1000))

The timeit.timeit() function takes three arguments:

  • stmt: The statement to be measured

  • setup: The setup statement to be executed before the stmt statement

  • number: The number of times the stmt statement is to be executed

The output of the above program is:

0.016000000000000014

The output is the time taken by the sort_list() function to sort the list 1000 times. The time is in seconds. The time taken by the sort_list() function to sort the list once is 0.016/1000 = 0.000016 seconds.

Conclusion

The timeit module is a very useful tool for measuring the time taken by a code snippet to execute. It can be used to compare the performance of different approaches to solve a problem. It can also be used to identify bottlenecks in the code and improve the performance of the code.