Understanding Python Decorator: Concepts, Functions, & More

Are you looking for an easier way to extend the functionality of your Python code? If so, don’t worry, you’re not alone. 15.7 million developers have adopted the popular and user-friendly programming language Python to create a wide array of projects and applications. With Python decorators, it’s easy for developers to add new features or modify existing behavior without disrupting any original coding structure. 

In this article, we’ll explore what exactly a decorator is– as well as how it works behind the scenes to increase both speediness and readability when writing out code with Python.

What are Decorators in Python?

Python decorator is a function that adds functionality to an existing function. They allow developers to modify the behavior of a function without having to change or rewrite its code. In addition, decorators can be used as wrappers for classes and methods, making it easier for developers to extend their features while keeping the original logic intact.

Decorators in Python are an invaluable tool for developers, as they enable them to easily add extra functionality or alter the behavior of functions, classes, and methods without needing to modify their code. This is done using specific callable objects such as class instances and functions that wrap around existing logic while introducing additional features at the same time. Like logging information about calls made on a decorated object. 

Furthermore, these decorators can be used along with other advanced techniques (such as metaprogramming) so that complex sequences of functionality can be achieved whilst still adhering to good software design principles throughout. To broaden your understanding you can take up an online Python course and gather your expertise in this field. 

Concepts Useful for Understanding Decorators

In order to gain an understanding of decorators, one must have a basic knowledge of Python functions and the concept that everything in Python, including functions is considered an object. In Python, functions are treated as first-class objects. This means they can be assigned to variables, passed into other function(s), and returned from other function(s). Here is the list of functions:

1. Pass Function as Argument

You can pass a function as an argument to another function in Python. An example of this is the following code, which passes one function called ‘adder’ into another function called ‘do_math’. 

def adder(x, y):
    # This is the function that will be passed as an argument.
    return x + y

def do_math(func, num1, num2):
    # The other function takes "adder" as its argument.
    result = func(num1, num2)
    # Here we are using 'func' to call 'adder'.
    print("Result:", result)
    # Prints out the result of adding two numbers together via our 'do_math' function and ‘adder’ being used within it!

do_math(adder, 5, 7)

#Output: Result: 12

2. Nested Function

Nesting functions is a term used to describe the act of including one function inside another. This can help improve code readability, optimize performance, and eliminate duplicate declarations. To create nested functions in Python: 

1) Define an outer function that contains the inner function; 

2) Write the body for both functions; and  

3) Call (execute) each individual or group of commands from within each other as necessary. 

For example,

def boy():             # Outer Function
    def bar():         # Inner Function
        print("Hello World")
        return "Inside Bar"
    print(bar())
    return "Inside boy"

print(boy())

Output:

Hello World

Inside Bar

Inside boy

3. Return a Function as a Value

In Python, you can return a function as a value. This means that when a function is used as the return value of another function, it will be automatically executed whenever the returned value is called. For example, we could create a function to multiply two numbers together and use it in another function which returns this multiplied result:

def multiple_two(x):
    def inner(y):
        return x * y  # Return the product of X*Y when `inner()` gets executed
    return inner  # Return `inner()` from within `multiple_two()`

# Create a new `mult` function by assigning output of `multiple_two` function to it
mult = multiple_two(2)  # Execute 'multiple_two' with parameter 2 passed
result = mult(3)
print(result) 

Output:

6

In this code example, we create a function called “multiple_two()” that takes an input x and returns another inner function. This inner function will take y as its argument and return the product of x*y when it gets executed. Then, we assign our new mult function by calling multiple_two with parameter 2 passed in to execute it. Finally, executing mult(3) prints out 6 which is the result of multiplying two together:2 * 3 =6

Now, having understood these prerequisites for using the decorator function in Python, let’s dive right into it in detail.

What are the Functions of Python Decorators?

Python Decorators are a type of function used to add additional functionality to an existing one. They can be defined as any callable object that accepts and returns another callable, usually by adding extra code or features before it is executed. A simple example would be the following: 

def make_bold(func):
    def inner():
        print("I am bold!")
        func()
    return inner

def plain():
    print("I am plain")

# Output: I am bold!
#         I am plain

“This defines a ‘make_bold’ decorator which takes in any given function (denoted here with ‘func’), then adds two lines within its own newly created ‘inner’ declaration. The added lines print ‘I am bold!’ before calling the original function. Finally, it returns the ‘inner’ function.”

1. The Syntax for Decorator 

Decorators in Python are denoted by the @ symbol followed by the decorator function’s name. The decorator is applied to a callable object (such as a function, method, or class)by placing it on the line directly above that particular callable’s definition like so: 

For example;

@gfg_decorator
def hello_decorator():  # Function to be decorated with decorator 
    print("Gfg"

The above code is equivalent to manually applying the decorator to the function using the decorator function’s syntax:

def hello_decorator():
    print("Gfg")
    
hello_decorator = gfg_decorator(hello_decorator)

The code snippet shows an example of such a decorator, gfg_decorator, which takes in hello_decorator and adds extra functionality before returning it as a wrapper for easy modification. 

The @ symbol in Python provides a simpler and more elegant way to use decorators. With this syntax, you don’t have to manually assign the decorated function call to a variable; instead, just add an ‘@’ followed by the desired decorator before defining your function definition for that area of code. This keeps your code cleaner and easier to read while still having all its required functionality.

For instance:

def make_bold(func):
    def inner():
        print("I am bold")
        func()
    return inner

@make_bold
def ordinary():
    print("I am plain")

ordinary()

Output:

I got decorated

I am ordinary

The @make_bold syntax is used to decorate the plain() function with the make_bold() decorator, which is equivalent to calling plain= make_bold.

2. Calling with the Decorator Function in Python

Now, we will use the decorator function for calling it:

def make_bold(func):
    # define the inner function
    def inner():
        # add some additional behavior to the decorated function
        print("I am bold!")
        
        # call the original function
        func()
    
    # return the inner function as a decorator 
    return inner

# define plain function 
def plain():  
    print("I am plain")

# decorate the plain function with the make_bold() decorator     
decorated_func = make_bold(plain)

# now call our newly created INNER FUNCTION - or "decorated" version of 'plain', which adds additional behavior. In this case, it says "I am bold!" and then calls 'plain' where it prints "I am plain".
decorated_func()

Output:

I am bold!

I am plain

This is an example of the Decorator Pattern, which allows you to add additional functionality to existing functions without having to modify them. The make_bold() function is a decorator that wraps around and alters the behavior of the plain() function. When decorated_func() is called, it causes the print(“I am bold!”) statement within the inner() function of make_bold(), thus resulting in “I am bold!” is printed on the screen.

Conclusion

This guide provides a comprehensive overview of Python decorators, which enable developers to modify the behavior of functions, methods, and classes without changing their code. It demonstrates how to use concepts such as passing functions as arguments, nesting functions, and returning them as values for added flexibility in programming. Decorators can be used to add functionality while keeping code clean and readable – making it an incredibly useful tool when writing codes with Python language.