Python Object-Oriented Programming (OOP) Concept

In the realm of programming languages, Python stands as a versatile giant. Known for its simplicity and readability, Python has gained immense popularity as a robust object-oriented programming (OOP) language. In this article, we embark on a journey through the world of Python OOP concepts, uncovering the profound significance of understanding OOP in the context of Python's programming landscape.

1. Classes and Objects

Unveiling the Essence of Classes and Objects

At the heart of Python's OOP paradigm lies the concept of classes and objects. A class can be perceived as a blueprint or template that encapsulates data and methods, providing structure to our code. On the other hand, an object is an instance of a class, carrying with it the attributes and behaviors defined within the class.

class Dog:
    def __init__(self, name, breed): = name
        self.breed = breed

    def bark(self):
        return f"{} says Woof!"

# Creating objects
dog1 = Dog("Buddy", "Golden Retriever")
dog2 = Dog("Max", "German Shepherd")

# Accessing object attributes and invoking methods
print(  # Output: Buddy
print(dog2.bark())  # Output: Max says Woof!

The Intricate Dance of Class Attributes and Methods

In Python, class attributes are variables that are shared by all instances of a class, while class methods are functions that belong to the class rather than instances. This not only optimizes memory usage but also fosters code reusability.

class Circle:
    pi = 3.14159265  # Class attribute

    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return self.pi * self.radius ** 2

# Accessing class attributes and invoking class methods
circle1 = Circle(5)
print(Circle.pi)  # Output: 3.14159265
print(circle1.area())  # Output: 78.53981625

2. Encapsulation

Unraveling the Concept of Encapsulation

Encapsulation is a fundamental pillar of OOP that promotes data hiding and protection. In Python, encapsulation is achieved through access modifiers: public, private, and protected. These modifiers determine the visibility and accessibility of class members.

class BankAccount:
    def __init__(self, account_number, balance):
        self.__account_number = account_number  # Private attribute
        self._balance = balance  # Protected attribute

    def deposit(self, amount):
        self._balance += amount

    def withdraw(self, amount):
        if amount <= self._balance:
            self._balance -= amount
            return "Insufficient balance"

# Creating a bank account object
account1 = BankAccount("12345", 1000)

# Accessing protected and private attributes
print(account1._balance)  # Output: 1000
print(account1.__account_number)  # Raises AttributeError

Harnessing Getters and Setters

To manipulate private attributes, Python employs getters and setters, allowing controlled access while maintaining data integrity.

class Student:
    def __init__(self, name, age):
        self.__name = name  # Private attribute
        self.__age = age  # Private attribute

    def get_name(self):
        return self.__name

    def set_age(self, age):
        if age > 0:
            self.__age = age

# Accessing private attributes via getters and setters
student1 = Student("Alice", 20)
print(student1.get_name())  # Output: Alice

3. Inheritance

Grasping the Essence of Inheritance

Inheritance embodies the concept of deriving new classes from existing ones, facilitating code reuse and hierarchical organization. Python supports both single inheritance and multiple inheritance, granting developers flexibility in crafting class hierarchies.

class Animal:
    def __init__(self, name): = name

    def speak(self):

class Dog(Animal):
    def speak(self):
        return f"{} barks!"

class Cat(Animal):
    def speak(self):
        return f"{} meows!"

# Creating instances of derived classes
dog = Dog("Buddy")
cat = Cat("Whiskers")

# Invoking overridden methods
print(dog.speak())  # Output: Buddy barks!
print(cat.speak())  # Output: Whiskers meows!

4. Polymorphism

Embracing Polymorphism's Significance

Polymorphism allows objects of different classes to be treated as objects of a common superclass. It encompasses method overriding and method overloading, providing an elegant way to achieve dynamic behavior.

class Shape:
    def area(self):

class Circle(Shape):
    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return 3.14159265 * self.radius ** 2

class Square(Shape):
    def __init__(self, side_length):
        self.side_length = side_length

    def area(self):
        return self.side_length ** 2

# Polymorphic behavior
shapes = [Circle(5), Square(4)]
for shape in shapes:
    print(f"Area: {shape.area()}")

5. Abstraction

Grasping the Notion of Abstraction

Abstraction involves simplifying complex reality by modeling classes based on real-world entities. In Python, this is accomplished through abstract classes and abstract methods.

from abc import ABC, abstractmethod

class Shape(ABC):
    def area(self):

class Circle(Shape):
    def __init__(self, radius):
        self.radius = radius

    def area(self):
        return 3.14159265 * self.radius ** 2

# Creating an instance of a derived class
circle = Circle(7)
print(circle.area())  # Output: 153.93804005

6. Encapsulation and Information Hiding

Safeguarding Data with Encapsulation

Encapsulation plays a pivotal role in data security by limiting access to critical data. Access specifiers such as public, private, and protected control the visibility of attributes.

class Employee:
    def __init__(self, emp_id, emp_name):
        self.emp_id = emp_id  # Public attribute
        self._emp_name = emp_name  # Protected attribute

    def display_details(self):
        return f"ID: {self.emp_id}, Name: {self._emp_name}"

# Accessing attributes with different access specifiers
employee = Employee(101, "Alice")
print(employee.emp_id)  # Output: 101
print(employee._emp_name)  # Output: Alice

7. Method Resolution Order (MRO)

Navigating the Method Resolution Order

In Python, the Method Resolution Order (MRO) determines the sequence in which methods are resolved in the presence of multiple inheritance. Python employs C3 Linearization to ensure method resolution is consistent and predictable.

class A:
    def show(self):
        return "A"

class B(A):
    def show(self):
        return "B"

class C(A):
    def show(self):
        return "C"

class D(B, C):

# Method Resolution Order
obj = D()
print(  # Output: B

8. Composition vs. Inheritance

Weighing Composition Against Inheritance

While both composition and inheritance are mechanisms for code reuse, it's crucial to discern when to employ one over the other. Composition promotes flexibility and modularity by allowing objects to collaborate without inheritance.

class Engine:
    def start(self):
        return "Engine started"

class Car:
    def __init__(self):
        self.engine = Engine()

    def drive(self):
        return self.engine.start() + " and car is moving"

# Utilizing composition
car = Car()
print(  # Output: Engine started and car is moving

9. Magic Methods

Unveiling Python's Magic Methods

Python boasts a repertoire of magic methods (also known as dunder methods) that enhance the functionality and behavior of classes. These methods, denoted by double underscores (e.g., __init__, __str__, __add__), enable customization of class behavior.

class ComplexNumber:
    def __init__(self, real, imag):
        self.real = real
        self.imag = imag

    def __str__(self):
        return f"{self.real} + {self.imag}i"

    def __add__(self, other):
        return ComplexNumber(self.real + other.real, self.imag + other.imag)

# Utilizing magic methods
num1 = ComplexNumber(2, 3)
num2 = ComplexNumber(1, 2)
result = num1 + num2
print(result)  # Output: 3 + 5i

10. Design Principles

Embracing SOLID Principles in Python OOP

To craft robust and maintainable code, Python developers often adhere to the SOLID principles—Single Responsibility Principle (SRP), Open/Closed Principle (OCP), Liskov Substitution Principle (LSP), Interface Segregation Principle (ISP), and Dependency Inversion Principle (DIP).

# Applying Single Responsibility Principle (SRP)
class Employee:
    def __init__(self, emp_id, emp_name):
        self.emp_id = emp_id
        self.emp_name = emp_name

class Payroll:
    def calculate_salary(self, employee):

# Open/Closed Principle (OCP) adhered through abstraction

# Liskov Substitution Principle (LSP) maintained in derived classes

# Interface Segregation Principle (ISP) with smaller, focused interfaces

# Dependency Inversion Principle (DIP) with dependency injection

11. Conclusion

In this comprehensive exploration of Python OOP concepts, we've delved into the core principles that underpin object-oriented programming in Python. Armed with knowledge of classes, encapsulation, inheritance, polymorphism, abstraction, and more, you're well-equipped to harness the power of OOP in your Python projects.

As you embark on your Python programming journey, remember that mastering these OOP concepts is not just a skill but a gateway to building elegant, scalable, and maintainable Python applications. Continue to explore, practice, and refine your understanding to become a proficient Python developer.

12. Let’s Revise

Classes and Objects

  • Classes are blueprints that encapsulate data and methods.
  • Objects are instances of classes, carrying attributes and behaviors.

Class Attributes and Methods

  • Class attributes are shared by all instances of a class.
  • Class methods belong to the class rather than instances, promoting code reusability.


  • Encapsulation promotes data hiding and protection.
  • Access modifiers (public, private, protected) control visibility.
  • Getters and setters manipulate private attributes while maintaining data integrity.


  • Inheritance allows new classes to derive from existing ones.
  • Python supports single and multiple inheritance for code reuse.
  • Hierarchical organization enhances class structures.


  • Polymorphism allows different classes to be treated as a common superclass.
  • It involves method overriding and method overloading.
  • Achieved dynamically for flexible behavior.


  • Abstraction simplifies complex reality by modeling classes.
  • Abstract classes and abstract methods define structure.
  • Provides a high-level view of objects.

Encapsulation and Information Hiding

  • Encapsulation limits access to critical data for data security.
  • Access specifiers (public, private, protected) control visibility.

Method Resolution Order (MRO)

  • MRO defines the sequence for method resolution.
  • Ensures consistency in multiple inheritance.
  • Resolves method conflicts logically.

Composition vs. Inheritance

  • Composition allows objects to collaborate without inheritance.
  • Promotes flexibility and modularity in code design.

Magic Methods

  • Magic methods enhance class functionality.
  • Denoted by double underscores (dunder methods).
  • Customize class behavior for various operations.

Design Principles

  • SOLID principles (SRP, OCP, LSP, ISP, DIP) guide OOP design.
  • Promote maintainable, robust, and scalable code.

13. Test Your Knowledge

1. What is a class in Python?
2. What is an object in Python?
3. What are class attributes in Python?
4. Which access modifiers control the visibility of class members in Python?
5. What is the primary purpose of getters and setters in Python?
6. What does polymorphism in Python allow?
7. How is abstraction achieved in Python?
8. What role does encapsulation play in Python OOP?
9. How does Python handle method conflicts in multiple inheritance?
10. What is the primary advantage of composition over inheritance in Python?
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