Python Decorators: A Powerful Tool for Code Reusability and Modularity

If you’ve ever seen the @ character in Python code, you’ve probably seen it used as such:

@add_logging
@auth_check
def user_login(*args, **kwargs):
   ...

These are what is known as decorators. Python packages such as click leverage decorators to extend functionality into your program.

Let’s take a dive in this tutorial to show you how these work and how you can use them with an example using Flask.

What are Decorators?

Think of a Python function as a bare Christmas tree. It's there, it serves its purpose, but it's a bit plain and ordinary. What if you want to add some extra flair and personality to your tree? That's where decorators come in!

Decorators are like the ornaments, lights, and other decorations you add to a Christmas tree. They allow you to enhance the functionality of a function or a method without modifying its original code.

How Do Decorators Work?

Decorators allow you to run code both before and/or after your function. Here’s a simple example:

When we call test_function(), it will first run the sample_wrap() function. In the sample_wrap() function, we have 2 print statements that will print out the code that runs before calling your function and code that runs after your function is ran.

So, if we run this, we’ll see the following output:

>>> test_function()
Code before running function
Code inside of the function
Code after running function

This is the reason why decorators are so powerful - you can change the functionality and behavior of the function without ever touching it.

Example: User Permissions with Flask

Let’s say that you want to set up an authentication system that checks to make sure that the user has the proper roles to perform certain actions in Flask.

We can write a decorator to check these permissions:

Then, in our code, we can apply this decorator without adding duplicate code to each of our functions: get_users(), update_user(), and delete_user():