All you need to know about OOP in Python

OOPin Python

In object-oriented programming (OOP), we often discuss the four pillars of its foundation. They’re like the building blocks that help us structure our code in a more organized and efficient way. 


Think of encapsulation as wrapping up data (variables) and methods (functions) into a single unit, known as a class. It’s like putting your code into a neat little package, where the inner workings are hidden from the outside world, and only the necessary interfaces are exposed. This helps better manage complexity and prevent unauthorized access or modification to data.

Let’s see an example to make it clearer. Suppose we’re building a `Car` class. Now, in the world of OOP, we want to encapsulate the data related to the car, like its make, model, and speed, along with methods to interact with this data.

class Car:

    def __init__(self, make, model):

        self.make = make

        self.model = model

        self.speed = 0

    def accelerate(self, amount):

        self.speed += amount

    def brake(self, amount):

        self.speed -= amount

    def get_speed(self):

        return self.speed

In this `Car` class:

– We have variables like `make`, `model`, and `speed`. These are encapsulated within the class, meaning they’re not directly accessible from outside the class.

– We also have methods like `accelerate`, `brake`, and `get_speed`. These methods provide controlled access to the internal state (the speed variable) of the `Car` object. They allow us to interact with the car’s speed in a controlled way, ensuring that we can’t accidentally set the speed to an invalid value or directly modify it without proper validation.

Now, let’s see how we can use this `Car` class:

# Create a new Car object

my_car = Car("Toyota", "Civic")

# Accelerate the car


# Check the speed

print("Current speed:", my_car.get_speed())

# Brake


# Check the speed again

print("Current speed:", my_car.get_speed())

Here, we’re interacting with the `Car` object through its methods (`accelerate`, `brake`, and `get_speed`). We don’t directly touch the internal variables like `speed`, thanks to encapsulation. This helps keep our code organized, preventing accidental modifications, and ensuring that the object’s state remains consistent.

Related: SOLID Principles in Python


Abstraction is about focusing on the essential qualities of an object while ignoring the irrelevant details. It’s like driving a car without needing to understand the intricate workings of the engine. In OOP, we create classes that represent real-world objects or concepts, abstracting away the complexities to make them easier to work with and understand.

Let’s dive into the concept of abstraction in object-oriented programming. Picture abstraction as wearing a pair of noise-canceling headphones while working in a busy coffee shop. You’re blocking out all the distracting background noise and focusing solely on your work. Similarly, in OOP, abstraction allows us to focus on the essential aspects of an object while hiding the complex details.

Let’s illustrate this with an example. Suppose we’re building a `Shape` class hierarchy to represent various geometric shapes. Each shape will have common properties like area and perimeter, but the specific calculations will vary depending on the shape.

from abc import ABC, abstractmethod

import math

class Shape(ABC):

    def area(self):

    def perimeter(self):

class Circle(Shape):

    def __init__(self, radius):

        self.radius = radius

    def area(self):

        return math.pi * self.radius**2

    def perimeter(self):

        return 2 * math.pi * self.radius

class Rectangle(Shape):

    def __init__(self, length, width):

        self.length = length

        self.width = width

    def area(self):

        return self.length * self.width

    def perimeter(self):

        return 2 * (self.length + self.width)

In this code:

– We have an abstract `Shape` class. It defines two abstract methods: `area()` and `perimeter()`. Abstract methods have no implementation details but provide a blueprint for subclasses to follow.

– We then have concrete subclasses `Circle` and `Rectangle` that inherit from the `Shape` class.

– Each subclass provides its implementation of the `area()` and `perimeter()` methods based on the specific characteristics of that shape.

Now, let’s see how we can use this abstraction:

circle = Circle(5)

print("Circle Area:", circle.area())

print("Circle Perimeter:", circle.perimeter())

rectangle = Rectangle(4, 6)

print("Rectangle Area:", rectangle.area())

print("Rectangle Perimeter:", rectangle.perimeter())

Here, we’re interacting with shapes without concerning ourselves with the intricate details of their calculations. We treat each shape as a black box that provides us with the area and perimeter when requested. This abstraction allows us to work with shapes at a higher level of understanding, promoting code clarity, and maintainability. We’re focusing on what the objects can do (their interfaces) rather than how they do it (their implementations).


Inheritance is a powerful concept where a new class can inherit properties and behavior (methods) from an existing class. It’s like passing down traits from parents to children. This helps promote code reusability and establish hierarchical relationships between classes, where more specialized classes can extend or modify the behavior of more generalized ones.

Inheritance in object-oriented programming is like passing down traits from parents to children. Imagine you have a family tree where characteristics like eye color or height are inherited from one generation to the next. Similarly, in OOP, inheritance allows a new class (child class) to inherit properties and behavior (methods) from an existing class (parent class).

Let’s demonstrate this with a simple example using animals:

class Animal:

    def __init__(self, name):

        self.name = name

    def speak(self):

        raise NotImplementedError("Subclass must implement abstract method")

class Dog(Animal):

    def speak(self):

        return f"{self.name} says Woof!"

class Cat(Animal):

    def speak(self):

        return f"{self.name} says Meow!"

class Cow(Animal):

    def speak(self):

        return f"{self.name} says Moo!"

class Sheep(Animal):

    def speak(self):

        return f"{self.name} says Baa!"

Here’s what’s happening in this code:

– We have a parent class `Animal`. It has an `__init__` method to initialize the animal’s name and a method `speak`, which is marked as abstract using `raise NotImplementedError`. This means that any subclass must provide its implementation of the `speak` method.

– We then have four subclasses `Dog`, `Cat`, `Cow`, and `Sheep`, each representing a specific type of animal. These subclasses are inherited from the `Animal` class.

– Each subclass overrides the `speak` method with its implementation, reflecting the sound that a particular animal makes.

Now, let’s see how we can use this inheritance:

dog = Dog("Buddy")

print(dog.speak())  # Output: Buddy says Woof!

cat = Cat("Whiskers")

print(cat.speak())  # Output: Whiskers says Meow!

cow = Cow("Bessie")

print(cow.speak())  # Output: Bessie says Moo!

sheep = Sheep("Dolly")

print(sheep.speak())  # Output: Dolly says Baa!

In this example, each animal type inherits the common behavior from the `Animal` class (the `speak` method) but provides its unique implementation. This promotes code reusability and helps in hierarchically organizing classes, making the code more manageable and scalable.


Polymorphism allows objects of different classes to be treated as objects of a common superclass. It’s like the ability of a function to work with different types of data. This enables flexibility in designing and interacting with objects, as methods can behave differently based on the specific implementation in each subclass. Polymorphism is often achieved through method overriding (having a subclass provide a specific implementation of a method) and method overloading (providing multiple methods with the same name but different parameters).

Polymorphism is like having a single remote control that can operate multiple devices. You press the same button, but depending on which device you’re using it with, it performs a different action. Similarly, in object-oriented programming, polymorphism allows objects of different classes to be treated as objects of a common superclass.

Let’s illustrate this concept with a practical example using shapes:

class Shape:

    def area(self):

        raise NotImplementedError("Subclass must implement abstract method")

class Rectangle(Shape):

    def __init__(self, width, height):

        self.width = width

        self.height = height

    def area(self):

        return self.width * self.height

class Circle(Shape):

    def __init__(self, radius):

        self.radius = radius

    def area(self):

        import math

        return math.pi * self.radius**2

Here’s what’s happening in this code:

– We have a superclass `Shape` with an abstract method `area`. This method must be implemented by subclasses.

– We then have two subclasses: `Rectangle` and `Circle`. Both subclasses inherit from the `Shape` class and provide their implementations of the `area` method.

Now, let’s see polymorphism in action:

shapes = [Rectangle(5, 4), Circle(3)]

for shape in shapes:

    print("Area:", shape.area())

In this example:

– We create a list called `shapes` containing instances of both `Rectangle` and `Circle` objects.

– We iterate over each shape in the list.

– We call the `area` method on each shape. Despite calling the same method (`area`), Python knows to execute the appropriate implementation based on the type of shape (whether it’s a rectangle or a circle).

The magic of polymorphism lies in Python’s ability to dynamically determine which method to call based on the object’s actual type at runtime. This flexibility allows us to write code that can work with objects of different classes without needing to know their specific types in advance, promoting code reuse and making our programs more versatile and adaptable.

You might like: Lets learn about Python Dataframes


Object-oriented programming (OOP) is a powerful paradigm that provides a structured approach to software development, enabling programmers to build complex systems in a more organized, scalable, and maintainable manner. At the heart of OOP lie the four pillars: encapsulation, abstraction, inheritance, and polymorphism. Together, these pillars form the foundation upon which robust and efficient object-oriented systems are built.

Encapsulation serves as the first pillar, emphasizing the bundling of data and methods into a single unit known as a class. By encapsulating data, OOP enables better control over access to and manipulation of data, promoting data integrity and security. Encapsulation fosters modular design, allowing developers to hide implementation details and expose only relevant interfaces to the outside world. This enhances code readability, reduces complexity, and facilitates code maintenance and debugging.

Abstraction, the second pillar, allows developers to focus on essential features of objects while hiding unnecessary details. Through abstraction, OOP provides a simplified and generalized view of complex systems, making them easier to understand and manage. Abstract classes and interfaces define a blueprint for objects, specifying common behaviors without providing concrete implementations. This promotes code reuse and facilitates adaptability, as subclasses can override abstract methods to tailor behavior to specific requirements.

Inheritance, the third pillar, facilitates code reuse and establishes hierarchical relationships between classes. Inheritance enables a subclass to inherit properties and behaviors from a superclass, allowing developers to extend and modify existing functionality without duplicating code. By organizing classes into hierarchies, OOP promotes code organization, enhances code readability, and simplifies maintenance. Inheritance encourages the creation of generic, reusable components, fostering a modular and scalable design approach.

Polymorphism, the fourth pillar, enables objects of different classes to be treated interchangeably based on their common interface. Polymorphism allows for flexibility and extensibility in object-oriented systems, as methods can operate on objects of various types without the need for explicit type checking. Through method overriding and method overloading, OOP supports dynamic binding, allowing the appropriate method implementation to be invoked at runtime based on the object’s actual type. Polymorphism promotes code flexibility, scalability, and adaptability, facilitating the development of robust and versatile software systems.

In conclusion, the four pillars of OOP encapsulation, abstraction, inheritance, and polymorphism collectively provide a solid foundation for building modular, reusable, and maintainable software systems. By adhering to these principles, developers can create code that is more robust, flexible, and adaptable to changing requirements. OOP encourages a disciplined approach to software design, promoting code organization, readability, and reusability. As a result, OOP has become a widely adopted paradigm in software development, empowering developers to create efficient, scalable, and maintainable solutions to complex problems.

Software Engineer | Website

Talha is a seasoned Software Engineer with a passion for exploring the ever-evolving world of technology. With a strong foundation in Python and expertise in web development, web scraping, and machine learning, he loves to unravel the intricacies of the digital landscape. Talha loves to write content on this platform for sharing insights, tutorials, and updates on coding, development, and the latest tech trends

Similar Posts

Leave a Reply

Your email address will not be published. Required fields are marked *