Theory is important, but the best way to solidify your understanding of Object-Oriented Programming is to build something. In this article, we will put together all the concepts we've learned in this series—classes, objects, attributes, methods, and encapsulation—to model a real-world entity from scratch.
We have now covered three major pillars of Object-Oriented Programming: Encapsulation, Inheritance, and Polymorphism. The final pillar we will discuss is Abstraction.
In Object-Oriented Programming, a key principle is encapsulation: the idea of bundling data (attributes) and the methods that operate on that data into a single unit (an object). Part of this principle is controlling access to that data. You might want some attributes to be freely accessible, while others should be hidden or only modified through specific methods.
In Object-Oriented Programming, there are two primary ways to build relationships between classes and reuse code: Inheritance and Composition. While we have already covered inheritance, it's crucial to understand its alternative, composition, and to know when to choose one over the other. This choice is one of the most fundamental design decisions you will make when structuring your programs.
Welcome to a new chapter in your Python journey! So far, we've been using a style of programming called procedural programming. We write procedures (functions) that perform operations on data. This is great, but as programs become larger and more complex, it can be hard to manage the relationship between the data and the functions that operate on it.
We've spent a lot of time learning how to write classes, including the init method to store attributes and special methods like repr and eq to make our classes behave well.
In the last article, we learned that a class is a blueprint and an object is the instance we create from it. We used the init() method to set up the initial data, or attributes, of an object.
We have learned about some of the core pillars of Object-Oriented Programming encapsulation.
We have mastered the art of creating self-contained classes that bundle data and behavior. But what if we have multiple classes that share some common logic? For example, a Dog, a Cat, and a Fish are all types of Animal. They might all have a name and an age, but each makes a different sound.
We've learned how to define methods—functions inside a class—that give our objects behavior. So far, all the methods we've created have been instance methods, which operate on a specific instance of a class.
In Object-Oriented Programming, an interface is a description of the actions an object can do. It's a contract or a blueprint that defines a set of methods that a class must implement. For example, any object that acts as a "Data Parser" should have a .parse() method, regardless of whether it's parsing a PDF, a website, or a text file.
We've already encountered some of Python's special "dunder" (double underscore) methods, like init, str, and repr. These methods are special because we don't call them directly (e.g., myobj.str()). Instead, Python calls them for us in response to specific actions, like print(myobj).
Welcome to the final article in our series on Advanced OOP. We are about to explore one of the most profound and mind-bending concepts in Python: metaclasses. This is an advanced topic, but understanding it provides a deep insight into how Python's object model works.
We've learned about inheritance and how a child class can inherit attributes and methods from a parent class. Now we're going to explore polymorphism, a powerful concept that allows us to treat objects of different classes in a similar way.
We've seen how to use private attributes (name) and public methods (getname(), setname()) to enforce encapsulation. This pattern, common in languages like Java or C#, is often called the "getter/setter" pattern.
We've created a Car class that holds data (attributes) and has behaviors (methods). But what happens when we try to print() one of our Car objects directly?