Is Python Object-Oriented? Exploring Object-Oriented Programming in Python

Did you know? Python started as a hobby project by Guido van Rossum in 1989! 
By 1991, its first release already had powerful features like classes, inheritance, and exception handling, setting the stage for Python’s strong object-oriented programming capabilities.

Latest Update:  Python is the world’s most popular programming language in 2025, according to a report by TIOBE.

Object-oriented programming languages group data and functions into objects, making code easier to organize and reuse. Understanding is Python object oriented helps you see how it brings these benefits to your projects. But if you’re new to object-oriented programming in python, it can be confusing to know where to start or how these concepts actually work in real code. 

This article explains Python’s OOP features clearly, so you can write cleaner, more modular, and reusable code that fits your needs.

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Object-Oriented Programming in Python: Key Concepts & Examples

Key OOP concepts in Python exist to help you organize complex programs by breaking them into manageable pieces. Imagine building a game: you’d need different characters, each with unique behaviors but some shared traits. Concepts like inheritance and polymorphism let you create a general character blueprint, then build specific characters without rewriting code. 

This keeps your program organized, easier to update, and reduces errors. 

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Key Features of Python's Object-Oriented Nature:

• Multi-Paradigm Support: Python blends object-oriented, procedural, and functional programming, allowing developers to use the best approach for their needs.
• Core OOP Principles: Python adheres to principles like encapsulation, inheritance, and polymorphism, which form the backbone of object-oriented design.
• Dynamic and Flexible: Python's dynamic nature enables developers to define and manipulate objects at runtime.

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Object-oriented programming languages organize code by grouping data and the functions that work on that data into reusable objects. Knowing what is object oriented language helps you understand why this approach makes complex programs easier to build and maintain. 

So, is Python object oriented language? Yes, it uses simple yet powerful tools to bring OOP to life:

Classes

These are blueprints that define the structure and behavior of objects. A class is like a recipe that tells Python what attributes and methods an object should have.

Let’s break down the structure of a Python class to see how it works in action:

• Attributes: These are variables inside the class that store data unique to each object. Think of them as characteristics or properties.
• Methods: These are functions defined inside the class that describe what the objects can do.

Here’s a simple example:

class Animal:
    def __init__(self, name, species):
        self.name = name
        self.species = species
    def info(self):
        return f"{self.name} is a {self.species}."

In this example, Animal is a class with two attributes—name and species. The __init__ method is a special constructor that sets these attributes when you create a new Animal object.

Next, we create an object:

dog = Animal("Buddy", "Dog")
print(dog.info())  # Output: Buddy is a Dog.

Here, dog is an instance of the Animal class, with its own name ("Buddy") and species ("Dog"). When you call dog.info(), it accesses those attributes and returns a clear, formatted string describing the animal.

Objects

These are instances created from classes. Each object holds its own data and can perform actions defined by its class.

Let’s dive into what makes objects so powerful in Python:

• Encapsulation of Data and Methods: Each object holds its own unique attributes and lets you use the class’s methods to interact with that data.
• Instance-Specific Behavior: Every object keeps track of its own state independently from others, even if they come from the same class.

Here’s a simple example to show this in action:

class Car:
    def __init__(self, make, model):
        self.make = make
        self.model = model

    def start(self):
        return f"{self.make} {self.model} is starting."

This Car class has two attributes—make and model—which get set when you create a new car object. The start method describes what happens when the car starts.

Now, let’s create two different car objects:

car1 = Car("Toyota", "Corolla")
car2 = Car("Tesla", "Model 3")
print(car1.start())  # Output: Toyota Corolla is starting.
print(car2.start())  # Output: Tesla Model 3 is starting.

Each car has its own details, and calling start() on each object returns a message specific to that car.

These building blocks, Python classes and objects, are essential to understanding is Python object oriented and how object-oriented programming in Python works in practice.

As your programs grow, managing lots of code can become overwhelming and error-prone. That’s where core OOP concepts like encapsulation, inheritance, and polymorphism come in, they exist to keep your code organized, reusable, and easier to maintain.

Let’s see what encapsulation is in OOP in Python.

Encapsulation

Encapsulation is a core idea in object oriented programming languages that helps keep your data safe and your code organized. In object-oriented programming in Python, encapsulation means restricting direct access to an object’s data so sensitive information stays protected.

Python classes and objects use a simple way to do this, by marking attributes and methods as private with a single or double underscore. 

If you’ve wondered is Python object oriented and how it handles data security, understanding encapsulation is key to writing clean, reliable code.

Let’s start with getter and setter methods, a powerful way to implement encapsulation in Python. These methods allow you to control how the data in your objects is accessed or modified, giving you more control over your program. 

Instead of directly changing an object’s private attributes, you use getter methods to fetch the data and setter methods to update it. This ensures your object’s data remains secure and its integrity is maintained. 

Here’s an example to show you how it works in practice:

class BankAccount:
    def __init__(self, account_holder, balance):
        self.__account_holder = account_holder  # Private attribute
        self.__balance = balance

    def get_balance(self):
        return self.__balance

    def deposit(self, amount):
        if amount > 0:
            self.__balance += amount
            return f"Deposited ${amount}. New balance: ${self.__balance}"
        return "Invalid deposit amount."

# Creating an object
account = BankAccount("Aliah", 1000)
print(account.get_balance())  # Output: 1000
print(account.deposit(500))   # Output: Deposited $500. New balance: $1500

Output: 

1000
Deposited $500. New balance: $1500

Explanation:

• Class Definition:
  • The BankAccount class has private attributes __account_holder and __balance, which cannot be accessed directly outside the class.
  • Getter method get_balance is used to retrieve the balance.
  • Setter method deposit allows adding money to the account, ensuring the deposit amount is valid.
• Object Creation:
  • An object account is created with "Aliah" as the account holder and 1000 as the initial balance.
• Method Call:
  • account.get_balance() returns the current balance of 1000.
  • account.deposit(500) adds 500 to the balance, updating it to 1500.
• Output: The printed output shows the initial balance and the updated balance after the deposit.

Also Read: What are the Advantages of Object-Oriented Programming?

You’ll now find out about inheritance in object oriented programming languages.

Inheritance

Inheritance is one of the most powerful features in object oriented programming languages. It allows you to create new classes that acquire the properties and methods of existing classes, making your code more modular and reusable. 

If you’ve ever asked is Python object oriented, the answer is yes, inheritance is one of the key concepts that make Python’s OOP system both flexible and efficient.

In object-oriented programming in Python, inheritance helps you avoid rewriting code by enabling new classes (child classes) to inherit the behavior and attributes of existing classes (parent classes). This keeps your code cleaner, more organized, and easier to maintain. 

Let’s break down the different types of inheritance in Python:

• Single Inheritance: In this simplest form of inheritance, a child class inherits from just one parent class. This is the most straightforward way to reuse code.
• Multilevel Inheritance: Here, a class inherits from a parent class, which is itself a derived class. This creates a chain of inheritance, allowing you to build upon existing functionality in multiple stages.

Multiple Inheritance: A class can inherit from more than one parent class, combining features from multiple classes into a single child class. This is powerful but should be used with care, as it can lead to complexity.

Example of Single Inheritance:

class Parent:
    def greet(self):
        return "Hello from the Parent!"

class Child(Parent):
    def greet(self):
        return "Hello from the Child!"

child = Child()
print(child.greet())  # Output: Hello from the Child!

Output: 

Hello from the Child!

Explanation:

• Class Definition:
  • The Parent class has a method greet that returns a greeting message "Hello from the Parent!".
  • The Child class inherits from Parent and overrides the greet method to return a different message "Hello from the Child!".
• Object Creation:
  • An object child is created from the Child class.
• Method Call:
  • When child.greet() is called, it invokes the greet method from the Child class, not the Parent class, due to method overriding.
• Output: The printed output shows the greeting from the Child class, demonstrating how method overriding works in inheritance.

Example of Multiple Inheritance:

class Mother:
    def skill(self):
        return "Cooking"

class Father:
    def skill(self):
        return "Driving"

class Child(Mother, Father):
    pass

child = Child()
print(child.skill())  # Output: Cooking (resolves to the first parent class)

Output: 

Cooking

Explanation:

• Class Definition:
  • The Mother class has a method skill that returns "Cooking".
  • The Father class has a method skill that returns "Driving".
  • The Child class inherits from both Mother and Father, making it an example of multiple inheritance.
• Object Creation:
  • An object child is created from the Child class, which inherits methods from both parent classes.
• Method Call:
  • When child.skill() is called, Python resolves the method call by following the method resolution order (MRO). Since Mother is listed first in the inheritance order, it calls the skill method from the Mother class, returning "Cooking".
• Output: The printed output shows "Cooking", as the method from the first parent class (Mother) is called.

Also Read: Polymorphism vs Inheritance in Java: Key Differences [2025]

Now, you’ll dig deeper into the essential concept of polymorphism in Python OOP.

Polymorphism

In simple terms, polymorphism means "many forms." It allows objects of different classes to use the same method, but each object can respond to that method in its own unique way. 

There are two main ways Python achieves polymorphism: method overriding and operator overloading.

• Method Overriding: This occurs when a subclass provides a specific implementation of a method that is already defined in its parent class. The subclass method overrides the parent method, allowing the same method to behave differently for different objects.

Operator Overloading: Python also allows you to redefine how operators (like +, -, *) behave for objects of your class. This enables objects of your class to interact with standard operators in a customized way.

Example of Method Overriding:

class Animal:
    def speak(self):
        return "Animal speaks."

class Dog(Animal):
    def speak(self):
        return "Woof!"

animal = Animal()
dog = Dog()

print(animal.speak())  # Output: Animal speaks.
print(dog.speak())     # Output: Woof!

Output: 

Animal speaks.
Woof!

Explanation:

• Class Definition:
  • The Animal class has a method speak that returns "Animal speaks.".
  • The Dog class inherits from Animal and overrides the speak method to return "Woof!".
• Object Creation:
  • An object animal is created from the Animal class.
  • An object dog is created from the Dog class.
• Method Call:
  • When animal.speak() is called, it uses the speak method from the Animal class, returning "Animal speaks.".
  • When dog.speak() is called, it uses the overridden speak method from the Dog class, returning "Woof!".
• Output: The printed output shows different messages based on the object type.

Abstraction

Abstraction helps manage complexity by showing only the essential features of an object. It hides the internal details that don’t need to be exposed to the user, making your code more readable and easier to maintain. 

For example, when you drive a car, you don’t need to know how the engine works, just how to use the steering wheel, pedals, and gears. In object oriented programming languages, abstraction lets you design objects in the same way.

Example of Abstraction:

from abc import ABC, abstractmethod

class Shape(ABC):
    @abstractmethod
    def area(self):
        pass

class Rectangle(Shape):
    def __init__(self, width, height):
        self.width = width
        self.height = height

    def area(self):
        return self.width * self.height

# Using the abstract class
rect = Rectangle(10, 5)
print(rect.area())  # Output: 50

Output: 

50

Explanation:

• Class Definition:
  • The Shape class is an abstract class that contains an abstract method area, which any subclass must implement.
  • The Rectangle class inherits from Shape and provides a concrete implementation of the area method, calculating the area as width * height.
• Object Creation:
  • An object rect is created from the Rectangle class with width = 10 and height = 5.
• Method Call:
  • When rect.area() is called, it calculates and returns the area of the rectangle, which is 50.
• Output: The printed output shows the calculated area.

Python’s OOP capabilities empower developers to create well-structured, reusable, and scalable code. By understanding and applying these principles, you can harness the full potential of Python in developing complex applications.

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Also Read: Abstraction vs Encapsulation: Difference Between Abstraction and Encapsulation

Now, let’s find out how to implement OOP principles in Python the proper way. 

Working with Methods and Attributes

Python classes can have different types of methods:

• Instance Methods: Operate on instance-level data and require the self parameter.
• Class Methods: Operate on class-level data and use the @classmethod decorator.
• Static Methods: Do not operate on class or instance data and use the @staticmethod decorator.

Example:

class Example:
    class_var = "Class Level Attribute"

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

    def instance_method(self):
        return f"Instance Method: {self.instance_var}"

    @classmethod
    def class_method(cls):
        return f"Class Method: {cls.class_var}"

    @staticmethod
    def static_method():
        return "Static Method: Independent of class or instance."

obj = Example("Instance Level Attribute")
print(obj.instance_method())  # Output: Instance Method: Instance Level Attribute
print(Example.class_method())  # Output: Class Method: Class Level Attribute
print(Example.static_method())  # Output: Static Method: Independent of class or instance.

Output:

Instance Method: Instance Level Attribute
Class Method: Class Level Attribute
Static Method: Independent of class or instance.

Explanation:

• Class Definition:
  • The Example class has a class-level attribute class_var and an instance-level attribute instance_var.
  • It contains three types of methods:
    • instance_method: Accesses the instance attribute instance_var.
    • class_method: A class method that accesses the class-level attribute class_var.
    • static_method: A static method that does not depend on either instance or class.
• Object Creation:
  • An object obj is created from the Example class with the instance attribute "Instance Level Attribute".
• Method Call:
  • obj.instance_method() accesses the instance attribute, returning "Instance Method: Instance Level Attribute".
  • Example.class_method() accesses the class attribute, returning "Class Method: Class Level Attribute".
  • Example.static_method() returns the static method's string, "Static Method: Independent of class or instance."
• Output: The printed output shows the results of calling each method.

Next, you’ll develop an understanding of the self parameter. 

Understanding the self Parameter

The self parameter refers to the instance of the class and is used to access its attributes and methods. It must be explicitly included in all instance method definitions.

Example:

class Car:
    def __init__(self, make, model):
        self.make = make
        self.model = model

    def details(self):
        return f"Car: {self.make} {self.model}"

car = Car("Tesla", "Model S")
print(car.details())  # Output: Car: Tesla Model S

Output: 

Car: Tesla Model S

Explanation:

• Class Definition:
  • The Car class has two attributes: make and model, initialized through the __init__ method.
  • The details method returns a string combining the make and model attributes to describe the car.
• Object Creation:
  • An object car is created from the Car class with "Tesla" as the make and "Model S" as the model.
• Method Call:
  • car.details() accesses the instance attributes using self and returns the string "Car: Tesla Model S".
• Output: The printed output shows the car's make and model details.

Also Read: Python Tutorial: Learn Python from Scratch

Let’s now see how you can use constructors and destructors for implementing OOP in Python.

Utilizing Constructors and Destructors

The __init__ method is a constructor that initializes an object’s attributes when it is created. The __del__ method is a destructor that cleans up resources when an object is deleted or goes out of scope.

Example:

class Resource:
    def __init__(self, name):
        self.name = name
        print(f"Resource {self.name} created.")

    def __del__(self):
        print(f"Resource {self.name} destroyed.")

# Creating and deleting an object
resource = Resource("File")
del resource  # Output: Resource File destroyed.

Output: 

Resource File created.
Resource File destroyed.

Explanation:

• Class Definition:
  • The Resource class defines the __init__ method to initialize the name attribute and prints a message when the resource is created.
  • The __del__ method is the destructor, which prints a message when the resource is destroyed (i.e., when the object is deleted or goes out of scope).
• Object Creation:
  • An object resource is created from the Resource class with "File" as the name. The constructor prints the creation message.
• Object Deletion:
  • del resource deletes the resource object, triggering the __del__ method, which prints the destruction message.
• Output: The printed output shows the creation and destruction messages for the resource object.

After covering the foundational concepts of object oriented programming languages in Python, apply these skills in your projects and continue refining your understanding.  

By learning how Python's OOP approach differs from or aligns with languages like Java or C++, you'll deepen your understanding of what is object oriented language and is python object oriented language strengthen your coding expertise. 

Next, let’s look into a detailed comparison to see how Python’s OOP stands out and where it fits among other popular languages.

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Python OOP vs Other Languages

OOP is used in different languages to simplify managing complex code through reusable structures. Understanding how object-oriented programming in Python compares to languages like Java or C++ helps you avoid confusion when switching between them. 

The table highlights the key differences in Python classes and objects, clarifying what is object oriented language and is Python object oriented language.

After reviewing the differences, focus on how Python’s simplicity and flexibility in OOP can help speed up development, especially for projects that require quick iteration and ease of use. If you’re transitioning from Java or C++, leverage Python’s dynamic typing and multiple inheritance capabilities to write more concise, readable code. 

For more control over memory or stricter typing, consider using Java or C++ for large-scale systems or performance-critical applications.  

If you're still building your Python skills, now is the perfect time to strengthen that foundation. Check out the Programming with Python: Introduction for Beginners free course by upGrad to build the foundation you need before getting into programming.

Advantages and Limitations of Using OOP in Python

Object-oriented programming (OOP) in Python offers numerous benefits, making it a preferred choice for building scalable and maintainable software. However, like any programming paradigm, it also comes with limitations that developers must consider based on project requirements.

First, you’ll learn about the main advantages of OOP in Python. 

While object-oriented programming in Python offers immense benefits like modularity, code reuse, and scalability, it’s important to keep potential limitations in mind. To avoid complexity in large-scale applications, be mindful of multiple inheritance and focus on method resolution order (MRO) or use composition where appropriate. 

For smaller projects, consider simplifying your design and using procedural programming or more advanced techniques to avoid unnecessary overhead.

Advanced OOP Techniques in Python: Exploring Beyond the Basics
 

Advanced object-oriented programming (OOP) techniques in Python enable developers to tackle complex programming challenges. These concepts extend the flexibility and power of Python's OOP, making it ideal for real-world applications.

Let's first see how to use magic methods in Python OOP. 

Magic Methods and Operator Overloading

Magic methods, also known as dunder methods (double underscore), allow customization of object behavior. They enable operator overloading, where operators perform specific tasks based on the object type.

Common Magic Methods:

• __add__: Customizes the addition operator (+).
• __str__: Defines the string representation of an object.
• __eq__: Customizes equality checks.

Example of Operator Overloading:

class Vector:
    def __init__(self, x, y):
        self.x = x
        self.y = y

    def __add__(self, other):
        return Vector(self.x + other.x, self.y + other.y)

    def __str__(self):
        return f"Vector({self.x}, {self.y})"

v1 = Vector(2, 3)
v2 = Vector(4, 5)
v3 = v1 + v2  # Uses __add__
print(v3)     # Output: Vector(6, 8)

Output: 

Vector(6, 8)

Explanation:

• Class Definition:
  • The Vector class defines the __init__ method to initialize the x and y attributes.
  • The __add__ method overloads the + operator, allowing the addition of two Vector objects by adding their respective x and y components.
  • The __str__ method defines how the Vector object is represented as a string, returning a formatted string with the x and y values.
• Object Creation:
  • Two Vector objects, v1 and v2, are created with the coordinates (2, 3) and (4, 5), respectively.
• Operator Overloading:
  • When v1 + v2 is executed, it calls the __add__ method, creating a new Vector object with the coordinates (6, 8).
• Method Call:
  • The print(v3) statement calls the __str__ method to return the string representation "Vector(6, 8)".
• Output: The printed output shows the result of adding the two vectors. 

Next, you’ll see how to work with abstract classes and interfaces for Python OOP. 

Abstract Classes and Interfaces

Abstract classes and interfaces provide templates for designing reusable and consistent APIs. They are implemented using the abc module in Python.

Features:

• Abstract Methods: Defined but not implemented in the base class, forcing derived classes to implement them.
• Consistency: Ensures all subclasses follow a specific structure.

Example using the abc Module:

from abc import ABC, abstractmethod

class Shape(ABC):
    @abstractmethod
    def area(self):
        pass

    @abstractmethod
    def perimeter(self):
        pass

class Rectangle(Shape):
    def __init__(self, width, height):
        self.width = width
        self.height = height

    def area(self):
        return self.width * self.height

    def perimeter(self):
        return 2 * (self.width + self.height)

rect = Rectangle(4, 5)
print(rect.area())        # Output: 20
print(rect.perimeter())   # Output: 18

Output:

20
18

Explanation:

• Abstract Classes and Methods:
  • The Shape class is an abstract class that uses the abc module. It defines two abstract methods, area and perimeter, which are declared but not implemented.
  • Any class inheriting from Shape must implement these abstract methods to avoid a TypeError.
• Class Definition:
  • The Rectangle class inherits from Shape and implements the area and perimeter methods, calculating the area and perimeter of the rectangle based on its width and height.
• Object Creation:
  • A Rectangle object rect is created with width = 4 and height = 5.
• Method Call:
  • rect.area() calculates the area as 4 * 5 = 20.
  • rect.perimeter() calculates the perimeter as 2 * (4 + 5) = 18.
• Output: The printed output shows the area and perimeter of the rectangle.

Finally, let’s explore what metaclasses are and how they work.

Metaclasses

Metaclasses are advanced constructs in Python that control how classes are created. They allow customization of class definitions and behaviors, enabling dynamic modification of classes at runtime.

Key Uses:

• Enforcing design patterns.
• Automatically registering classes.
• Adding custom attributes or methods during class creation.

Example of Metaclasses:

class Meta(type):
    def __new__(cls, name, bases, dct):
        dct['greet'] = lambda self: f"Hello from {self.__class__.__name__}!"
        return super().__new__(cls, name, bases, dct)

class MyClass(metaclass=Meta):
    pass

obj = MyClass()
print(obj.greet())  # Output: Hello from MyClass!

Output: 

Hello from MyClass!

Explanation:

• Metaclass Definition:
  • The Meta class is a metaclass that inherits from type. It overrides the __new__ method, which is called when a class is being created.
  • Inside __new__, a new method greet is added to the class dictionary (dct), which returns a string with the class name.
• Class Definition:
  • The MyClass class uses Meta as its metaclass. This means that the greet method is dynamically added to MyClass during its creation.
• Object Creation:
  • An object obj is created from MyClass.
• Method Call:
  • obj.greet() calls the dynamically added greet method, which returns "Hello from MyClass!".
• Output: The printed output shows the greeting message. 

By mastering these advanced techniques, developers can unlock the full potential of Python's OOP, creating efficient, scalable, and maintainable software solutions for complex use cases.

Now, you'll learn about some common misconceptions about Python and OOP.

Common Misconceptions About Python and OOP

Python's object-oriented programming (OOP) features are often misunderstood, leading to misconceptions about its capabilities and flexibility. This section clarifies popular myths and demonstrates Python's robustness in handling OOP and other paradigms.

Myth 1: "Python is Not Fully Object-Oriented"

Truth: While Python is not a "pure" object-oriented language like Smalltalk, it is fully capable of OOP. Everything in Python, including functions, modules, and numbers, is treated as an object.

Example:

# Even numbers are objects in Python
num = 42
print(type(num))  # Output: <class 'int'>

# Functions as objects
def greet():
    return "Hello, World!"

print(type(greet))  # Output: <class 'function'>

Output: 

<class 'int'>
<class 'function'>

Explanation:

• Objects in Python:
  • In Python, everything is an object, including numbers and functions. This means that even basic types, like integers, are instances of specific classes.
• Example 1 (Numbers as Objects):
  • The variable num is assigned the value 42, which is an integer. Calling type(num) returns <class 'int'>, showing that numbers are treated as objects of the int class.
• Example 2 (Functions as Objects):
  • The function greet is defined. Calling type(greet) returns <class 'function'>, indicating that functions in Python are treated as objects of the function class.

Python blends object-oriented, procedural, and functional paradigms, providing versatility without compromising its OOP features.

Myth 2: "You Cannot Mix Paradigms Effectively in Python"

Truth: Python is inherently multi-paradigm, supporting procedural, functional, and object-oriented styles seamlessly. Developers can mix paradigms to suit their project requirements, enabling flexibility and efficiency.

Example:

# Procedural style
def add(x, y):
    return x + y

# Object-oriented style
class Calculator:
    def add(self, x, y):
        return x + y

# Functional style
add_lambda = lambda x, y: x + y

# Using all paradigms together
print(add(2, 3))  # Procedural: Output 5
calc = Calculator()
print(calc.add(2, 3))  # OOP: Output 5
print(add_lambda(2, 3))  # Functional: Output 5

Output: 

5
5
5

Explanation:

• Procedural Style:
  • The add function is defined in the procedural style, where it takes two arguments x and y and returns their sum. The output is 5 when called with (2, 3).
• Object-Oriented Style:
  • The Calculator class is defined with an add method. An object calc is created from the Calculator class, and the add method is called on it, returning 5.
• Functional Style:
  • A lambda function add_lambda is defined for adding two numbers. It behaves in the functional programming style and returns 5 when called with (2, 3). 

Python's ability to integrate paradigms allows developers to leverage the strengths of each approach.

Comparison Table: Myths vs. Reality

To continue advancing your skills, consider exploring topics like design patterns in Python, functional programming techniques, and testing OOP code. Additionally, working on projects such as building a Python-based API, developing a task manager with OOP, or creating a game engine can help reinforce your understanding and provide hands-on experience. 

Accelerate Your Python Skills with upGrad

This blog provides a comprehensive overview of object-oriented programming in Python, covering core concepts like encapsulation, inheritance, polymorphism, and abstraction. It also explores more advanced techniques such as method overriding and metaclasses, giving you a deeper understanding of Python OOP.
While these concepts are powerful for building scalable applications, many developers struggle to apply them effectively, especially when transitioning from procedural programming or handling complex projects.

upGrad’s courses offer hands-on experience with OOP, guiding you through practical examples and advanced techniques so you can apply these concepts confidently and efficiently in your real-life projects.

In addition to the courses mentioned, here are some more resources to help you further elevate your skills: 

• Learn Python Libraries: NumPy, Matplotlib & Pandas
• Case Study using Tableau, Python and SQL
• Post Graduate Certificate in Data Science & AI (Executive)

Not sure where to go next in your Python journey? upGrad’s personalized career guidance can help you explore the right learning path based on your goals. You can also visit your nearest upGrad center and start hands-on training today! 

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Reference:
https://www.statista.com/statistics/793628/worldwide-developer-survey-most-used-languages/ 
https://www.tiobe.com/tiobe-index/
https://chem.libretexts.org/Courses/University_of_Arkansas_Little_Rock/IOST_Library/05:_Python_Book/01:_Introduction/01:_History_of_Python