Object-Oriented Programming

1. Classes and Objects

Definition

A class is a blueprint (template) that defines the structure and behaviour of objects. An object is an instance of a class — a concrete entity with specific values for the attributes Defined by the class.

Python Implementation

class BankAccount:
    def __init__(self, account_number, owner, balance=0.0):
        self._account_number = account_number
        self._owner = owner
        self._balance = balance

    def deposit(self, amount):
        if amount > 0:
            self._balance += amount

    def withdraw(self, amount):
        if 0 < amount <= self._balance:
            self._balance -= amount
            return True
        return False

    def get_balance(self):
        return self._balance

Class vs Instance

2. Encapsulation

Definition

Encapsulation is the bundling of data (attributes) and methods that operate on that data within A class, and restricting direct access to internal state.

Access Modifiers

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

    def get_name(self):
        return self.__name

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

Benefits of encapsulation:

1. Data hiding: Prevents unauthorised access
2. Validation: Input validation through setters
3. Flexibility: Internal implementation can change without affecting users
4. Maintainability: Reduces coupling between components

:::tip Exam tip In exams, always justify why encapsulation is important. Focus on data integrity (preventing invalid states) and implementation flexibility. :::

3. Inheritance

Definition

Inheritance allows a class (subclass/child) to inherit attributes and methods from another class (superclass/parent), enabling code reuse and establishing an “is-a” relationship.

class Shape:
    def __init__(self, colour):
        self._colour = colour

    def area(self):
        return 0

    def __str__(self):
        return f"{self.__class__.__name__} (colour: {self._colour})"

class Rectangle(Shape):
    def __init__(self, colour, width, height):
        super().__init__(colour)
        self._width = width
        self._height = height

    def area(self):
        return self._width * self._height

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

    def area(self):
        return 3.14159 * self._radius ** 2

Types of Inheritance

Method Overriding

A subclass can override a method inherited from the superclass by defining a method with the Same name.

class Animal:
    def speak(self):
        return "Some sound"

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

class Cat(Animal):
    def speak(self):
        return "Meow"

The super() Function

super() calls the parent class’s method, enabling extension (not replacement) of inherited Behaviour.

4. Polymorphism

Definition

Polymorphism (Greek: “many forms”) allows objects of different classes to be treated through a Common interface, with the specific behaviour determined at runtime.

Types of Polymorphism

Compile-Time (Static) Polymorphism

Achieved through method overloading (multiple methods with the same name but different Parameters). Python does not support method overloading directly, but can simulate it with default Arguments or type checking.

Run-Time (Dynamic) Polymorphism

Achieved through method overriding and duck typing. The actual method called depends on the Object’s type at runtime.

def make_speak(animal):
    print(animal.speak())

animals = [Dog(), Cat(), Animal()]
for animal in animals:
    make_speak(animal)

Output:

Woof
Meow
Some sound

Theorem (Liskov Substitution Principle). If $S$ is a subtype of $T$Then objects of type $T$ May be replaced with objects of type $S$ without altering any of the desirable properties of the Program.

This means: wherever a superclass object is expected, a subclass object should work correctly.

:::info Board-specific AQA requires understanding of classes, objects, inheritance, Polymorphism, encapsulation; uses pseudocode class definitions. CIE (9618) covers OOP Principles; may require implementation in a specific language (Python/Pascal). OCR (A) requires Class definitions with attributes and methods; constructor/destructor understanding. Edexcel Covers OOP with pseudocode; emphasises encapsulation and data hiding. :::

5. Abstract Classes and Interfaces

Abstract Classes

An abstract class cannot be instantiated and may contain abstract methods (methods without Implementation that must be implemented by subclasses).

from abc import ABC, abstractmethod

class Vehicle(ABC):
    @abstractmethod
    def start_engine(self):
        pass

    @abstractmethod
    def stop_engine(self):
        pass

    def drive(self):
        self.start_engine()
        print("Driving...")
        self.stop_engine()

Interfaces

An interface is a contract specifying methods a class must implement, without providing any Implementation. In Python, interfaces are simulated using abstract classes with only Abstract methods.

6. Association, Aggregation, and Composition

Association

A general relationship between two classes. Objects can exist independently.

class Doctor:
    def __init__(self, name):
        self.name = name
        self.patients = []

Aggregation (“has-a”, weak)

A whole-part relationship where parts can exist independently of the whole.

class Department:
    def __init__(self, name):
        self.name = name
        self.teachers = []

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

Composition (“has-a”, strong)

A whole-part relationship where parts cannot exist without the whole.

class House:
    def __init__(self, address):
        self.address = address
        self.rooms = [Room("living"), Room("bedroom")]

class Room:
    def __init__(self, purpose):
        self.purpose = purpose

When a House is destroyed, its Room objects are also destroyed.

7. SOLID Principles

Example: Single Responsibility

class User:
    def __init__(self, name, email):
        self.name = name
        self.email = email

class UserRepository:
    def save(self, user):
        pass

class EmailService:
    def send_welcome(self, user):
        pass

Each class has a single responsibility.

Problem Set

Problem 1. Design a class hierarchy for different types of employees in a company: Manager, Developer, and Intern. Include a common method calculate_salary() with different implementations.

from abc import ABC, abstractmethod

class Employee(ABC):
    def __init__(self, name, base_salary):
        self.name = name
        self.base_salary = base_salary

    @abstractmethod
    def calculate_salary(self):
        pass

class Manager(Employee):
    def __init__(self, name, base_salary, bonus):
        super().__init__(name, base_salary)
        self.bonus = bonus

    def calculate_salary(self):
        return self.base_salary + self.bonus

class Developer(Employee):
    def __init__(self, name, base_salary, overtime_hours):
        super().__init__(name, base_salary)
        self.overtime_hours = overtime_hours

    def calculate_salary(self):
        return self.base_salary + self.overtime_hours * 50

class Intern(Employee):
    def calculate_salary(self):
        return self.base_salary

Problem 2. Explain the difference between a class variable and an instance variable. Give an Example.

class Dog:
    species = "Canis familiaris"  # Class variable (shared)

    def __init__(self, name):
        self.name = name  # Instance variable (unique)

Problem 3. Explain how polymorphism is demonstrated in the following code:

shapes = [Rectangle("red", 3, 4), Circle("blue", 5)]
for shape in shapes:
    print(shape.area())

Problem 4. A student writes a Square class that inherits from Rectangle. The Square Constructor takes only a side parameter. Explain why this might violate the Liskov Substitution Principle.

class Square(Rectangle):
    def __init__(self, colour, side):
        super().__init__(colour, side, side)

r = Square("red", 5)
r.set_width(10)  # If Rectangle has this, width = 10, height = 5
# Now r is no longer a valid square!

Problem 5. Explain the difference between aggregation and composition with examples.

Problem 6. Implement an abstract class DataStructure with abstract methods insert``delete And search. Then implement it as a Stack.

from abc import ABC, abstractmethod

class DataStructure(ABC):
    @abstractmethod
    def insert(self, value):
        pass

    @abstractmethod
    def delete(self):
        pass

    @abstractmethod
    def search(self, value):
        pass

class Stack(DataStructure):
    def __init__(self):
        self._data = []

    def insert(self, value):
        self._data.append(value)

    def delete(self):
        if self._data:
            return self._data.pop()
        raise Exception("Stack underflow")

    def search(self, value):
        for i in range(len(self._data) - 1, -1, -1):
            if self._data[i] == value:
                return i
        return -1

Problem 7. Explain the Open/Closed Principle and give an example of a design that violates it, Then fix it.

class AreaCalculator:
    def area(self, shape):
        if shape.type == "rectangle":
            return shape.width * shape.height
        elif shape.type == "circle":
            return 3.14159 * shape.radius ** 2
        # Adding a new shape requires modifying this class!

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

class Rectangle(Shape):
    def area(self):
        return self.width * self.height

class Circle(Shape):
    def area(self):
        return 3.14159 * self.radius ** 2

class AreaCalculator:
    def total_area(self, shapes):
        return sum(s.area() for s in shapes)

Problem 8. What is the output of the following code? Explain the method resolution order.

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

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

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

class D(B, C):
    pass

print(D().greet())
print(D.__mro__)

B
(<class 'D'>, <class 'B'>, <class 'C'>, <class 'A'>, <class 'object'>)

Problem 9. Explain why multiple inheritance can lead to the “diamond problem” and how Python Resolves it.

    A
   / \
  B   C
   \ /
    D

Problem 10. Design a library system with classes for Book``MemberAnd Library. Use Encapsulation appropriately. Include methods for borrowing and returning books.

class Book:
    def __init__(self, isbn, title, author):
        self.__isbn = isbn
        self.__title = title
        self.__author = author
        self.__available = True

    def is_available(self):
        return self.__available

    def borrow(self):
        if self.__available:
            self.__available = False
            return True
        return False

    def return_book(self):
        self.__available = True

class Member:
    def __init__(self, member_id, name):
        self.__member_id = member_id
        self.__name = name
        self.__borrowed_books = []

    def borrow_book(self, library, isbn):
        book = library.find_book(isbn)
        if book and book.is_available():
            book.borrow()
            self.__borrowed_books.append(book)
            return True
        return False

    def return_book(self, library, isbn):
        for i, book in enumerate(self.__borrowed_books):
            if book._Book__isbn == isbn:
                book.return_book()
                self.__borrowed_books.pop(i)
                return True
        return False

    def get_borrowed_count(self):
        return len(self.__borrowed_books)

class Library:
    def __init__(self):
        self.__books = {}
        self.__members = {}

    def add_book(self, book):
        self.__books[book._Book__isbn] = book

    def register_member(self, member):
        self.__members[member._Member__member_id] = member

    def find_book(self, isbn):
        return self.__books.get(isbn)

For revision on programming fundamentals, see Programming Constructs.

Common Pitfalls

1. Confusing authentication (who you are) with authorisation (what you can do) in security contexts.

2. Forgetting that $O(n \log n)$ average-case for quicksort becomes $O(n^2)$ worst-case on already sorted input.

3. Neglecting to normalise database designs, leading to data redundancy and update anomalies.

4. Mixing up Big O, Big $\Omega$, and Big $\Theta$ notation — Big O is an upper bound, not necessarily tight.

Summary

The key principles covered in this topic are linked in the sub-pages above. Focus on understanding the definitions, applying the formulas or frameworks, and evaluating strengths and limitations of each approach.

Worked Examples

Worked examples demonstrating the application of key concepts are covered in the detailed sub-pages linked above.