10 Applications of Object Oriented Programming


10 Applications of Object Oriented Programming

The modern software engineering landscape is changing at a blistering pace. AI integration, low-code applications, and new engineering efficiency tools have set the stage for a busy decade to come (DevPro Journal). These changes make object-oriented programming (OOP) even more important as a cornerstone strategy for building versatile, scalable, efficient applications.

Object-Oriented Programming, or OOP, is a way of writing computer programs using "objects" instead of just actions. Before, programming was mainly about doing the right things in the right order: acquire data, analyze it, implement it, and provide demonstrated results. OOP changes that process through its focus on objects, essentially pieces of information, and their capabilities. Many web developers around the world learn how to use OOP with Python because it helps them create modular software solutions in dramatically less time.

If you are interested in learning more in-depth about OOP and Python, check out our Software Engineering Bootcamp. In this immersive program, you will learn how to create and manipulate objects, utilize inheritance and encapsulation, and apply OOP principles to build robust and modular JavaScript applications.

Here are the 10 basic concepts of OOP that you should know when programming in Python:

1. Client-Server Systems

Object-oriented client-server systems represent a powerful approach to designing and implementing robust IT infrastructures. In these systems, Object-Oriented Programming (OOP) principles are applied to create a structured and modular architecture for building client and server components to provide the IT infrastructure and create Object-Oriented Client-Server Internet (OCSI) applications. 

For example, consider a simple client-server application using Python's ‘socket’ module, promoting code modularity and reusability:

import socket

class Server:
    def __init__(self, host, port):
        self.host = host
        self.port = port
        self.server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        self.server_socket.bind((host, port))

    def accept_connection(self):
        client_socket, addr = self.server_socket.accept()
        print(f"Connection from {addr}")

class Client:
    def __init__(self, host, port):
        self.host = host
        self.port = port
        self.client_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)
        self.client_socket.connect((host, port))

# Usage
server = Server("", 8080)
client = Client("", 8080)
  • Server Class: Initializes a server socket, binds it to a host and port, and listens for incoming connections. It provides a method (accept_connection) to accept client connections.
  • Client Class: Initializes a client socket and connects it to the server's host and port.
  • Usage: An instance of the Client class is created to establish a connection to the server. Once a connection is accepted by the server, communication can occur between the client and server.

This example demonstrates how OOP principles can be applied to implement a basic client-server system in Python, showcasing encapsulation of socket functionality within classes, promoting code modularity and reusability.

2. Object-Oriented Databases

Object-Oriented Databases, often referred to as Object Database Management Systems (ODBMS), represent a specialized category of database systems that store and manage objects directly instead of traditional relational data. These databases are designed to align seamlessly with Object-Oriented Programming (OOP) principles and offer unique advantages in certain application domains.

In the provided example, the shelve module, a simple object-based persistence module, is used to simulate an OODBMS in Python. These databases store objects instead of data, such as real numbers and integers:

import shelve

class Person:
    def __init__(self, name, age):
        self.name = name
        self.age = age

# Storing objects in the database
with shelve.open("mydatabase") as db:
    person1 = Person("Alice", 30)
    person2 = Person("Bob", 25)
    db["alice"] = person1
    db["bob"] = person2

# Retrieving objects from the database
with shelve.open("mydatabase") as db:
    retrieved_person = db["alice"]
    print(f"Name: {retrieved_person.name}, Age: {retrieved_person.age}")
  • Person Class: Represents individuals with attributes like name and age.
  • Storing Objects in the Database: Instances of the Person class are created and stored in the database using the shelve module.
  • Retrieving Objects from the Database: Objects are retrieved from the database and their attributes are accessed and manipulated as needed.

This example illustrates how OOP concepts are applied in interacting with object-oriented databases in Python, showcasing how objects are stored, retrieved, and manipulated, promoting data encapsulation and abstraction.

3. Real-Time System Design

Real-Time System Design is a critical discipline in computer science and engineering that focuses on creating systems capable of responding to external events or inputs within strict timing constraints. Object-Oriented Programming (OOP) techniques can play a significant role in simplifying the complexities associated with designing and implementing real-time systems.

Consider a scenario where real-time data from sensors is processed using OOP:

class Sensor:
    def __init__(self, name):
        self.name = name

    def read_data(self):
        # Simulate reading data from a sensor
        return 42

class RealTimeSystem:
    def __init__(self):
        self.sensors = [Sensor("Temperature"), Sensor("Pressure")]

    def process_data(self):
        for sensor in self.sensors:
            data = sensor.read_data()
            print(f"{sensor.name}: {data}")

# Usage
real_time_system = RealTimeSystem()
  • Sensor Class: Represents a sensor device with attributes such as name and methods like read_data() to simulate data reading.
  • RealTimeSystem Class: Aggregates sensor instances and provides methods to process data from multiple sensors in real-time.
  • Processing Data: The process_data() method of the RealTimeSystem class iterates through the sensors, reads data from each sensor using their read_data() method, and processes it as per system requirements.

This example demonstrates how OOP can be leveraged to model real-time systems in Python, showcasing encapsulation of sensor functionality, abstraction of data processing logic, and modular design for scalability and maintainability.

4. Simulation and Modeling Systems

Simulation and Modeling Systems involve the creation of models that mimic real-world processes or systems for various purposes, such as scientific research, engineering, decision-making, and training. Object-Oriented Programming (OOP) offers an alternative and highly effective approach for simplifying the development and management of complex modeling systems.

Here is an example of how an ecological simulation is implemented using Python classes:

class Organism:
    def __init__(self, species, age):
        self.species = species
        self.age = age

    def simulate_interaction(self):
        print(f"{self.species} interacts with the environment")

class EcologicalSimulation:
    def __init__(self):
        self.organisms = [Organism("Plant", 2), Organism("Animal", 5)]

    def run_simulation(self):
        for organism in self.organisms:

# Usage
simulation = EcologicalSimulation()
  • Organism Class: Represents individual organisms in the ecosystem with attributes such as species and age, along with methods to simulate interactions with the environment.
  • EcologicalSimulation Class: Aggregates instances of Organism and provides methods to simulate interactions among them within the ecosystem.
  • Simulating Interaction: The simulate_interaction() method of the Organism class simulates an organism's interaction with the environment, while the run_simulation() method of the EcologicalSimulation class orchestrates the simulation of interactions among organisms.

This example illustrates how OOP can be utilized to model simulation and modeling systems in Python, showcasing encapsulation of organism behavior, abstraction of ecosystem dynamics, and modular design for flexibility and extendability.

5. Hypertext and Hypermedia

Hypertext and Hypermedia systems are interactive information systems that enable users to navigate through interconnected documents or multimedia content. Object-Oriented Programming (OOP) provides a robust foundation for creating frameworks and applications for hypertext and hypermedia.

Here, Python classes are used to model a basic text processing system extended to handle hypertext processing:

class TextProcessor:
    def __init__(self, content):
        self.content = content

    def process_text(self):
        return f"Processed Text: {self.content}"

class HypertextProcessor(TextProcessor):
    def add_links(self, links):
        self.links = links

    def process_text(self):
        processed_text = super().process_text()
        return f"{processed_text}\nLinks: {self.links}"

# Usage
hypertext_processor = HypertextProcessor("Hello, World!")
hypertext_processor.add_links(["Link 1", "Link 2"])
result = hypertext_processor.process_text()
  • TextProcessor Class: Represents a basic text processing system with methods to process text content.
  • HypertextProcessor Class: Extends the TextProcessor class to include methods for handling hypertext-specific functionality, such as adding and managing links.
  • Processing Text: Both classes provide methods to process text content, with the HypertextProcessor class additionally handling the management of hyperlinks within the text.

This example demonstrates how OOP can be applied to model hypertext and hypermedia systems in Python, showcasing inheritance to extend functionality, encapsulation of text processing logic, and abstraction for managing hyperlinks.

6. Neural Networking and Parallel Programming

Neural Networking and Parallel Programming are two powerful fields of computer science with diverse applications, and Object-Oriented Programming (OOP) can significantly simplify the development and implementation of neural networks, especially in parallel computing environments.

In this example, an OOP approach is taken to model a simple neural network:

class Neuron:
    def __init__(self, weights):
        self.weights = weights

    def activate(self, inputs):
        # Simplified activation function for demonstration purposes
        return sum(w * x for w, x in zip(self.weights, inputs))

class NeuralNetwork:
    def __init__(self, layers):
        self.layers = [Neuron(weights) for weights in layers]

    def predict(self, inputs):
        layer_outputs = [neuron.activate(inputs) for neuron in self.layers]
        return layer_outputs

# Usage
network = NeuralNetwork([[0.5, 0.2], [0.3, 0.8]])
result = network.predict([0.6, 0.4])
  • Neuron Class: Represents individual neurons in the neural network with attributes such as weights and methods to perform activation.
  • NeuralNetwork Class: Aggregates instances of Neuron to form a network and provides methods to predict outputs based on inputs.
  • Predicting Outputs: Takes input data, passes it through the network of neurons, and predicts the outputs based on the network's activation function.

This example illustrates how OOP can be utilized to model neural networks and parallel algorithms in Python, showcasing encapsulation of neuron behavior, abstraction of network dynamics, and modular design for scalability and flexibility.

7. Office Automation Systems

Office Automation Systems (OAS) are software applications that streamline and automate various administrative and communication tasks within organizations. These systems facilitate information sharing, improve efficiency, and enhance communication among employees. Object-Oriented Programming (OOP) is instrumental in the development of OAS, as it offers several advantages for creating both formal and informal electronic systems within organizations.

In the provided example, Python classes are used to model basic functionalities of an office automation system:

class Email:
    def __init__(self, sender, recipient, subject, content):
        self.sender = sender
        self.recipient = recipient
        self.subject = subject
        self.content = content

    def send_email(self):
        print(f"Email sent from {self.sender} to {self.recipient}")

class CalendarEvent:
    def __init__(self, title, date, location):
        self.title = title
        self.date = date
        self.location = location

    def schedule_event(self):
        print(f"Event '{self.title}' scheduled on {self.date} at {self.location}")

# Usage
email = Email("user@example.com", "recipient@example.com", "Meeting", "Discuss project updates")

event = CalendarEvent("Team Meeting", "2024-01-15 10:00 AM", "Conference Room")
  • Email Class: Represents an email message with attributes such as sender, recipient, subject, and content, along with methods to send emails.
  • CalendarEvent Class: Represents a scheduled event with attributes such as title, date, and location, along with methods to schedule events.
  • Interacting with Office Tools: Instances of the Email and CalendarEvent classes are created to interact with email messaging and calendar scheduling functionalities, respectively.

As you can see, OOP can be applied to model components of office automation systems in Python, showcasing encapsulation of functionalities within classes, abstraction of communication processes, and modular design for interoperability.

8. CIM/CAD/CAM Systems

Computer-Integrated Manufacturing (CIM), Computer-Aided Design (CAD), and Computer-Aided Manufacturing (CAM) systems are essential tools in manufacturing and design industries. Object-Oriented Programming (OOP) plays a vital role in these systems, simplifying complex tasks such as blueprint and flowchart design, reducing development effort, and enhancing overall efficiency.

Consider an example where OOP is used to model a CAD system:

class Shape:
    def draw(self):

class Circle(Shape):
    def draw(self):
        print("Drawing a circle")

class Square(Shape):
    def draw(self):
        print("Drawing a square")

class CADSystem:
    def __init__(self):
        self.shapes = [Circle(), Square()]

    def draw_shapes(self):
        for shape in self.shapes:

# Usage
cad_system = CADSystem()
  • Shape Class: Represents geometric shapes with a method to draw the shape.
  • Circle and Square Classes: These classes inherit from the Shape class and provide methods to draw specific shapes.
  • CADSystem Class: Aggregates instances of shapes and provides methods to draw shapes collectively.

This example illustrates how OOP can be applied to model components of CIM/CAD/CAM systems in Python, showcasing inheritance to extend functionality, encapsulation of shape properties and behaviors, and modular design for efficiency and scalability.

9. AI Expert Systems

AI Expert Systems are computer applications that use specialized knowledge and reasoning capabilities to solve complex problems or provide expertise in specific domains. Object-Oriented Programming (OOP) is a valuable approach in the development of AI Expert Systems, especially when dealing with intricate problems that go beyond human cognitive abilities.

In the provided example, Python classes are used to model basic functionalities of a medical expert system:

class ExpertSystem:
    def __init__(self, domain):
        self.domain = domain

    def analyze_problem(self, problem):
        print(f"Analyzing problem in the domain of {self.domain}: {problem}")

class MedicalExpertSystem(ExpertSystem):
    def diagnose_condition(self, symptoms):
        print(f"Diagnosing medical condition based on symptoms: {symptoms}")

# Usage
medical_expert_system = MedicalExpertSystem("Medicine")
medical_expert_system.analyze_problem("Patient's symptoms")
medical_expert_system.diagnose_condition(["Fever", "Headache"])
  • ExpertSystem Class: Represents a generic expert system with attributes such as domain and methods to analyze problems within that domain.
  • MedicalExpertSystem Class: Inherits from the ExpertSystem class and specializes in diagnosing medical conditions based on symptoms, with additional methods specific to medical domain expertise.
  • Interacting with Expert Systems: Instances of the MedicalExpertSystem class are created to interact with the expert system functionalities, analyzing medical problems and diagnosing conditions based on symptoms.

This example demonstrates how OOP can be applied to model AI expert systems in Python, showcasing inheritance to specialize functionalities, encapsulation of domain-specific knowledge and reasoning processes, and modular design for adaptability and scalability within different problem domains.

10. E-Commerce Systems

E-Commerce Systems are complex platforms that enable online buying and selling of goods and services. Object-Oriented Programming (OOP) plays a pivotal role in transforming the development of E-Commerce systems by enhancing their scalability, modularity, and overall efficiency.

Consider where OOP is applied to model a simplified E-Commerce system: 

class Product:
    def __init__(self, name, price, quantity):
        self.name = name
        self.price = price
        self.quantity = quantity

class ShoppingCart:
    def __init__(self):
        self.items = []

    def add_item(self, product, quantity):
        self.items.append({"product": product, "quantity": quantity})

    def calculate_total(self):
        total = sum(item["product"].price * item["quantity"] for item in self.items)
        return total

# Usage
product1 = Product("Laptop", 1000, 2)
product2 = Product("Headphones", 100, 3)

cart = ShoppingCart()
cart.add_item(product1, 1)
cart.add_item(product2, 2)

total_price = cart.calculate_total()
print(f"Total Price: ${total_price}")
  • Product Class: The Product class represents items available for purchase, with attributes such as name, price, and quantity.
  • ShoppingCart Class: The ShoppingCart class represents a user's shopping cart, with methods to add items, calculate the total price, and manage the cart contents.
  • Interacting with E-Commerce Functionality: Instances of the Product class are created to represent different products available for purchase. The ShoppingCart class is used to manage user interactions, such as adding products to the cart and calculating the total price of items in the cart.

This example illustrates how OOP can be applied to model components of e-commerce systems in Python, showcasing encapsulation of product and shopping cart functionalities, abstraction of user interactions, and modular design for scalability and flexibility in handling e-commerce operations.

Get Started in Object-Oriented Programming Today

Object-oriented programming alone isn’t enough to earn you a career in software engineering or web development. But when you pair OOP with skills in algorithm theory, JavaScript, system design, and React applications, you’ll be well on your way to joining a rapidly growing IT job landscape.

That’s the opportunity that QuickStart offers. Our Software Engineering Bootcamp prepares aspiring information technology professionals for the long-term career you deserve. In 12 or 20 weeks, you can learn the same skills that most college students spend four years studying. 

Without crippling college debt or skills decay, you’ll be able to master front-end and back-end development skills that modern IT employers are actively looking for. Connect with a QuickStart Admissions Advisor today if you’re ready to take important steps toward an entry-level career in software engineering, OOP, and web development.

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