Object-Oriented Programming in Python: Part – 1

1. Organization of Data:

In OOP, you start by thinking about the main components of your program. For a car, you’d consider attributes like color, make, model, and speed. These attributes define the state of a car.

2. Encapsulation:

Encapsulation means bundling data (attributes) and the methods (actions) that work with that data together into a single unit. In our example, each car would be an object that combines its attributes and methods.

3.Objects:

So, let’s create an object called “Car”. This object has attributes like color (e.g., “red”), make (e.g., “Toyota”), model (e.g., “Camry”), and speed (e.g., 60 mph).

4. Methods:

Now, let’s define methods (actions) that a car can perform. For instance, we might have methods like “accelerate” and “brake”. When you call car.accelerate(), it increases the speed attribute. When you call car.brake(), it decreases the speed attribute.

5. Real-World Comparison:

Think of a car object as a real car you see on the road. A car has its unique attributes (color, make, model) and can perform actions (accelerate, brake). 

This mapping of real-world entities to objects and their actions is the heart of OOP.

Why use OOP in Python?

Using OOP, you can create multiple car objects with their own individual attributes and methods, without worrying about them interfering with each other. You can easily add new car types or modify their behaviours without affecting the entire program. It’s like building with LEGO blocks – each piece (object) has its purpose and fits together seamlessly.