Summary

The provided web content offers an introduction to Python decorators, explaining their role in metaprogramming and their ability to modify functions while maintaining code readability.

Abstract

The article serves as a beginner's guide to understanding decorators in Python, emphasizing that Python treats everything as an object, including integers, which have attributes and associated functions. It introduces the concept of metaprogramming, a powerful but complex feature simplified in Python through the use of decorators. Decorators are presented as a means to modify functions without altering their code, enhancing abstraction and code readability. The article illustrates practical examples of decorators, including their use with the @ keyword for syntactic sugar, passing arguments to decorators, and a generic template for creating decorators that can handle any arguments. It concludes by acknowledging the complexity of decorators but encourages learning them as part of mastering Python's core concepts and best practices.

Opinions

The Python community is described as particularly fond of decorators, valuing their ability to abstract functionality while preserving code clarity.
Metaprogramming is characterized as a complex and potentially intimidating aspect of programming, but the article suggests that Python decorators offer a more accessible entry point to this concept.
The author implies that while decorators may seem daunting to beginners, understanding them is crucial for developing a deeper comprehension of Python.
The article implies that the versatility of decorators, as demonstrated by their use in popular modules and libraries like timeit, Thread, Processes, lru_cache, flask, and fast api, makes them an essential tool for Python programmers.

An Introduction to Decorators in Python

A beginner’s guide to understanding decorators in Python.

World of Objects

used: https://imgflip.com/memegenerator/80964944/Woody-and-Buzz-Lightyear-Everywhere-Widescreen

Have you ever wondered — Why a simple data type as an integer which is a byte in other programming languages takes up more memory in Python?

methods associated with an integer in Python

In Python, integers are objects. That means it has an attribute and a function associated with it. If you stay in Python long enough, you are bound to hear- everything in Python is an object. Everything you can think of like data types, classes function, exceptions, are first-class citizens and behave like any other object. It means they can be passed as an argument to another function, be returned as the output of the function, and stored as attributes for any other objects. If you don’t believe me try experimenting in Python.

This simple design allows us to do metaprogramming- it is a powerful feature that can be used to modify the program. Metaprogramming sounds big and scary cause it is. They are hard to understand, complex to use, and easy to mess up. Luckily, most of us will never need to use it in our normal programming journey. , However, in Python, we have a simple feature called a decorator to carry out our metaprogramming.

Decorators

Python community loves decorators. Rightfully so, it takes abstraction to a new level while maintaining the readability of code. Decorators in layman’s terms allow us to modify the functions without having to touch the code written inside them.

In the above example, we have a function fibonacci which takes a number returns an output. We have a decorator which takes a function as an argument and variance arguments. It prints function details and calculates the time taken to execute the function. To get the decorator we can use decorator(fibonacci, 10).

Once we run the above code we get:

fibonacci(10,)->3628800 total_time: 3.337860107421875e-06

The above way is how we use modules like timeit, Thread and Processs.

Syntactic sugar

Now, we will look at the more popular way of creating a decorator using the @ keyword.

In the above snippet, we have created a wrapper that will enclose our function and add extra print to the functions. The decorator will return the wrapper. To add the wrapper for fibonacci() function we simply put @decorator. Now we can use the fibonacci() function as a normal method and it will run the wrapper. This is the way we use popular tools like lru_cache.

Passing Arguments to Decorator

Passing an argument to the decorator requires one more wrapper.

Here we created the top module with default_arg. Once the decorator is called it will create another wrapper that will wrap the function. This is what we use in a library like flask and fast api.

Generic Template

This is the generic syntax for decorators. The *args we have seen in the above examples are a list of arguments. While **kwargs are a list of keyword arguments. Together they can handle any argument given to use:

The output of the above function is:

args: (1, 2, 3) <class ‘tuple’> kwargs: {‘a’: ‘a’, ‘z’: ‘b’} <class ‘dict’>

Conclusion

Decorators are a deadly arsenal to your coding repertoire. Decorators are one of the most complex core concepts in Python. They may be a little intimidating to approach as beginners. But in learning, we understand more about Python and better practices.

And there you have it. Thank you for reading.

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