Every business relies on the best programmers producing the best code for their website. The languages they use depend on the work, as for some tasks, developers may choose to work with server-side scripting languages such as PHP, .NET, Node.js, Java, Ruby, and Python. For other tasks, developers may decide to take a more traditional approach of using an object-oriented language for the projects. Usually, the nature of the work will determine which language to go with.

But what does the work demand of an “object-oriented language” in software development? Which one should we choose? This decision can make or break the work. Any wrong language choice means you will get stuck and result in lots of wasted effort, while choosing the right one will make everything flow seamlessly. In this article, we will examine what OOP languages are and the top programming languages that contribute to it significantly.

What Is Object-Oriented Language?

An Object-Oriented Language is a programming language specifically designed (or evolved) to fully support Object-Oriented Programming (OOP) principles, making it natural to structure code around classes, objects, inheritance, and the four pillars. These languages provide built-in syntax for encapsulation (private/public access), polymorphism (method overriding), and abstraction (interfaces/abstract classes), so developers don’t fight the language to use OOP effectively.

Unlike procedural languages (C) or functional ones (Haskell), OOP languages treat everything as objects or provide seamless object support. Java enforces “pure OOP” (no functions outside classes), Python offers flexible dynamic OOP, and C++ blends OOP with low-level control. You can model real-world hierarchies—like Dog extends Animal—without awkward workarounds.

Read More: What are The Most Popular Programming Languages ?

What is Object-Oriented Programming?

“Object-Oriented Programming (OOP)” is the paradigm—the mindset and methodology—not the language itself. It’s organizing code into self-contained objects that bundle data (attributes like bankAccount.balance) and behaviors (methods like withdraw()) together, interacting via well-defined messages rather than global functions manipulating shared data.

Think procedural programming as “data + functions scattered everywhere” (chaos at scale) versus OOP’s “objects own their data and expose clean APIs” (scalable teamwork). Pioneered by Simula (1967) and Smalltalk, OOP powers 80% of enterprise software because it mirrors reality: cars have color/speed and drive() independently.

Key Building Blocks of OOP

OOP rests on just a few elegant concepts that unlock modularity and reusability. Master these, and you’ll think in objects naturally.

1. Classes: The Blueprints

A class is a template or blueprint defining the structure for objects. It declares:

• Attributes (data/properties): Like color, speed, or accountBalance.
• Methods (behaviors/functions): Like accelerate(), getBalance(), or transferFunds().

Real-world example: A class is like a car manufacturing blueprint. It specifies wheels, engine, and doors—but no actual car exists until you build one.

class Car:  # Class definition

    def __init__(self, color, model):  # Constructor

        self.color = color  # Attribute

        self.model = model

        self.speed = 0

    def accelerate(self, increment):  # Method

        self.speed += increment

2. Objects: The Living Instances

An object is a concrete instance of a class—memory gets allocated, data gets filled. Multiple objects can come from one class, each with a unique state.

Real-world example: Objects are actual cars rolled off the assembly line. A red Tesla and a blue Ford share the same “Car” blueprint but have different colors and mileage.

my_tesla = Car("red", "Model S")    # Object 1

my_ford = Car("blue", "F-150")      # Object 2

my_tesla.accelerate(50)  # my_tesla.speed = 50

print(my_ford.speed)     # 0 (independent state)

3. Attributes: The State

Attributes hold an object’s data—what makes it unique at any moment. They can be:

• Instance attributes: Unique per object (e.g., my_tesla.color).
• Class attributes: Shared across all objects (e.g., Car.wheel_count = 4).

class Car:

    wheel_count = 4                    # Class attribute (shared)

    def __init__(self, color, model):

        self.color = color             # Instance attribute (unique)

        self.model = model

        self.speed = 0

4. Methods: The Behaviors

Methods are functions bound to a class/object. They operate on the object’s own data via self (Python) or this (Java/C++). Instance methods act on one object; class/static methods apply broadly.

Key insight: Methods encapsulate logic—withdraw() checks the balance internally before changing it, preventing invalid states.

class Car:

    wheel_count = 4

    def __init__(self, color, model):

        self.color = color

        self.model = model

        self.speed = 0

        self.is_running = False

    # Instance Method 1: Start the engine

def start_engine(self):

        if not self.is_running:

            self.is_running = True

            print(f"{self.color} {self.model} engine started!")

        else:

            print(f"{self.model} already running")

    # Instance Method 2: Accelerate

def accelerate(self, increment):

        if self.is_running:

            self.speed += increment

            print(f"⚡ Speed increased to {self.speed} km/h")

        else:

            print("❌ Start engine first!") 

    # Instance Method 3: Brake

def get_status(self):

        status = f"{self.color} {self.model}"

        status += f" | Speed: {self.speed} km/h"

        status += f" | Running: {'Yes' if self.is_running else 'No'}"

        status += f" | Wheels: {self.wheel_count}"

        return status

    # Class Method (works on class, not instance)

    @classmethod

    def get_wheel_info(cls):

        return f"All cars have {cls.wheel_count} wheels"

    # Static Method (utility, doesn't need self or cls)

    @staticmethod

    def is_valid_speed(speed):

        return 0 <= speed <= 300    

    # Instance Method 4: Get status

# Demo: Methods in Action!

print("🏎️  OOP Methods Demo\n")

# Create objects

tesla = Car("red", "Model S")

ford = Car("blue", "F-150")

# Use methods

tesla.start_engine()      # red Model S engine started!

tesla.accelerate(60)      # Speed increased to 60 km/h

print(tesla.get_status()) # red Model S | Speed: 60 km/h | Running: Yes | Wheels: 4

ford.start_engine()       # blue F-150 engine started!

ford.accelerate(80)       # Speed increased to 80 km/h

ford.brake()              # Speed reduced to 50 km/h

print("\nClass method:", Car.get_wheel_info())

print("Static method:", Car.is_valid_speed(250))  # True

4 Principles of OOP

Almost everything in object-oriented programming (OOP) is analyzed as an object. But what does OOP refer to as an object? Objects store information known as attributes or properties. There are four guiding principles in OOP that dictate how objects relate to each other: encapsulation, inheritance, polymorphism, and abstraction. These principles make it possible for objects to work together to solve a problem and build complex software:

1. Encapsulation

Encapsulation bundles data (attributes) and behaviors (methods) into a single unit, then locks down access to sensitive internals. External code interacts only through controlled “public doors” (methods), keeping the messy wiring hidden.

class VendingMachine:

    def __init__(self):

        self.__inventory = {"chips": 10, "soda": 8}  # Private: double underscore

        self.__money = 0                             # Hidden balance

    def add_money(self, amount):                    # Public method

        if amount > 0:

            self.__money += amount

            print(f"Added ${amount}. Total: ${self.__money}")

    def dispense_item(self, item):                  # Controlled access

        if item in self.__inventory and self.__inventory[item] > 0:

            if self.__money >= 1.50:

                self.__inventory[item] -= 1

                self.__money -= 1.50

                print(f" Dispensed {item}!")

                return True

            else:

                print(" Need $1.50")

        else:

            print("❌ Out of stock")

        return False

    def get_balance(self):                          # Read-only peek

        return self.__money

# Clean external usage

vm = VendingMachine()

vm.add_money(2.00)

vm.dispense_item("chips")

# print(vm.__money)  # ERROR! Encapsulation prevents direct access

print(f"Balance: ${vm.get_balance()}")  # Balance: $0.5

2. Inheritance

Inheritance creates a parent-child hierarchy where child classes inherit (and optionally override) everything from parents. It’s “is-a” relationships: SportsCar is-a Car.

class Vehicle:  # Parent (base class)

    def __init__(self, brand, wheels=4):

        self.brand = brand

        self.wheels = wheels

        self.is_moving = False

    def start(self):

        self.is_moving = True

        return f"{self.brand} started moving!"

    def stop(self):

        self.is_moving = False

        return f"{self.brand} stopped."

class Car(Vehicle):                             # Child inherits Vehicle

    def __init__(self, brand, doors=4):

        super().__init__(brand)                 # Call parent constructor

        self.doors = doors

    def honk(self):                             # New child method

        return "BEEP BEEP!"

class Motorcycle(Vehicle):

    def __init__(self, brand):

        super().__init__(brand, wheels=2)

    def wheelie(self):                          # Unique behavior

        return "Motorcycle doing wheelie! "

# Inheritance in action

tesla = Car("Tesla", doors=2)

honda = Motorcycle("Honda")

print(tesla.start())    # Tesla started moving! (inherited)

print(honda.stop())     # Honda stopped. (inherited)

print(tesla.honk())     # BEEP BEEP! (new)

3. Polymorphism

Polymorphism lets objects of different classes respond to the same method call differently. Call speak() on any Animal—dogs bark, cats meow, birds chirp. The correct behavior resolves at runtime.

class Animal:

    def speak(self):

        return "Some generic animal sound"

class Dog(Animal):

    def speak(self):                        # Override

        return "Woof! "

class Cat(Animal):

    def speak(self):

        return "Meow! "

class Bird(Animal):

    def speak(self):

        return "Tweet! "

# Polymorphism magic—no type checking needed!

def animal_concert(animals):

    for animal in animals:

        print(animal.speak())               # Same call, different results!

pets = [Dog(), Cat(), Bird()]

animal_concert(pets)

# Woof! 

# Meow! 

# Tweet! 

4. Abstraction: The Art of Hiding Complexity

Abstraction hides implementation details, exposing only essential features. Use abstract classes/interfaces to define contracts: “You must implement these methods.”

from abc import ABC, abstractmethod

class PaymentProcessor(ABC):                    # Abstract base class

    @abstractmethod

    def process_payment(self, amount):       # Contract: must implement

        pass

    @abstractmethod

    def get_payment_method(self):

        pass

class CreditCardProcessor(PaymentProcessor):

    def process_payment(self, amount):

        return f"Charged ${amount} to credit card 💳"

    def get_payment_method(self):

        return "Credit Card"

class PayPalProcessor(PaymentProcessor):

    def process_payment(self, amount):

        return f"Transferred ${amount} via PayPal 💰"

    def get_payment_method(self):

        return "PayPal"

# Generic checkout works with ANY processor

def checkout(processor: PaymentProcessor, amount):

    print(processor.process_payment(amount))

    print(f"Method: {processor.get_payment_method()}")

checkout(CreditCardProcessor(), 99.99)

checkout(PayPalProcessor(), 49.99)

Most Popular OOP Languages You Must Know

Though Simula was the very first programming language that was object-oriented, several other programming languages implement OOP today. Moreover, some programming languages are more optimally compatible with OOP than others. For instance, programming languages that are regarded as pure object-oriented programming languages conceptualize everything as an object. Other programming languages are tailored for OOP, with some procedural elements integrated. Most common programming languages are designed or implemented for OOP.

Language	Applications	OOP Strength	Learning Curve	Performance	Best known for
Python	AI/ML, Web (Django), Data Science, Automation	Dynamic classes + mixins, duck typing polymorphism	Easiest (1-2 weeks)	Concise & null safe Android ecosystem	Readable syntax + massive ecosystem. OOP feels natural, not forced.
Java	Enterprise backends, Android apps, Banking systems	Strict interfaces, pure encapsulation, JVM ecosystem ​	Medium (2-4 weeks)	High (JIT optimized)	Write once, run anywhere” + strict OOP enforcement
JS/TS	Frontend (React), Full-stack (Next.js), Browser games	ES6 classes + prototypes, TypeScript contracts ​	Easy (1-3 weeks)	Good (V8 engine)	Powers 98% of websites + Node.js backends
C#	Unity games, .NET services, Windows/Azure apps	LINQ objects, async inheritance, rich hierarchies ​	Medium (2-4 weeks)	High (JIT + GC)	Unity games + .NET ecosystem
C++	AAA Games (Unreal), Systems, Finance HFT, Embedded	Templates, RAII, zero-cost virtual polymorphism	Hard (2-6 months)	Fastest (native)	Fastest performance in this list
Swift	iOS/macOS apps, Apple Watch, Server (Vapor)	Protocol-oriented, optionals prevent nil crashes ​	Easy (2-3 weeks)	High (ARC + LLVM)	Fastest & safest Apple ecosystem
Kotlin	Android apps (Google preferred), Multiplatform	Data classes, sealed inheritance, extension methods ​	Easy (1-2 weeks for Java devs)	High (JVM optimized)	Concise & null safe Andoid ecosystem
Ruby	Web apps (Rails), Scripting, Shopify backend	Everything-is-object, metaprogramming mixins ​	Very Easy (1 week)	Medium (GIL-like)	Conventional & elegant
PHP	WordPress (43% web), Laravel APIs, CMS plugins	Traits (horizontal inheritance), modern PHP 8+	Easy (1-2 weeks)	Good (JIT in 8+)	Powers 77% of websites (WordPress!)
Go (Golang)	Cloud (Kubernetes), Microservices, DevOps tools	Structs + implicit interfaces, embedding composition ​	Simple (1 week)	High (compiled + goroutines)	Google’s efficient systems language

Conclusion

Classes in OOP have private properties by default, meaning that functions defined outside the class cannot access its member variables. Only the class's own member functions can access them. To allow member functions of other classes to access these, we use access specifiers: public, private, and protected. Using objects and classes is a way to bundle functions and data together. OOP also enables the use of inheritance and polymorphism, which improve the maintenance and reusability of the code. 

This is why, among other reasons, OOP features detailed design patterns and protocols to prevent external functions from accessing internal code. Ultimately, OOP has proved its worth and remains the ideal solution for many modern software development challenges, as it allows the creation of data structures with numerous features, along with functions that emphasise security, integrity, and maintainability.