Introduction to Object Orientation Encapsulation Inheritance Polymorphism Abstraction Classes & Objects Python

📘 Part 1: Introduction to Object Orientation

• Object Orientation = A way of designing programs around objects (real-world things).

• Each object has:

  • Properties (attributes/data) → e.g., a Car has color, model

  • Methods (functions/behavior) → e.g., a Car can start(), stop()

✅ Why use Object Orientation?

• Models the real world better

• Easy to reuse and extend code

• Easier maintenance

• Saves development time

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📘 Part 2: Main Principles of OOP

1. Encapsulation → hiding details

   • Example: You just call tv.turnOn(), you don’t care how it works inside.

2. Inheritance → child class gets features of parent class

   • Example: Dog and Cat inherit from Animal.

3. Polymorphism → same function, different behavior

   • Example: draw() works differently for Circle and Square.

4. Abstraction → show only important things, hide details

   • Example: A car’s steering wheel hides the complex engine mechanism.

5. Classes & Objects

   • Class = blueprint (like a recipe)

   • Object = actual thing created from blueprint (like a cake baked from recipe)

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📘 Part 3: Python Basics Refresher

✅ Data Structures

Structure	Written With	Ordered?	Changeable?	Allows Duplicates?	Example
Tuple	()	Yes	❌ No	✅ Yes	(1, 2, "Hi")
List	[]	Yes	✅ Yes	✅ Yes	[1,2,2,3]
Set	{}	No	✅ Yes	❌ No	{1,2,3}
Dictionary	{key:val}	Yes	✅ Yes	Keys ❌ dupes, Values ✅	{"A":10, "B":20}

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✅ Variables

• Must start with a letter or _

• Cannot start with number

• Case-sensitive (Age ≠ age)

• Use meaningful names (student_age ✔ vs x ✘)

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✅ Operators

• Arithmetic: + - * / % ** //

• Comparison: == != > < >= <=

• Logical: and, or, not

• Assignment: =, +=, -=

• Membership: in, not in

• Identity: is, is not

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✅ Indentation in Python

• Python uses spaces instead of { }.

• Example:

if age >= 18:
    print("Adult")
else:
    print("Child")

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📘 Part 4: Control Flow

1. Sequence → run line by line

2. Selection (if/else) → decision-making

3. Repetition (loops) → repeat tasks

   • for loop → repeat fixed times

   • while loop → repeat until condition false

   • break → exit loop early

   • continue → skip one loop step

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📘 Part 5: Functions

• A function = block of code that does something specific.

def greet(name):
    return "Hello " + name

print(greet("Faaz"))

• Functions improve reusability and organization.

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📘 Part 6: Sorting & Searching

• Bubble Sort → repeatedly swap until sorted (slow for big data).

• Selection Sort → pick smallest, put in front.

• Insertion Sort → place each new item in correct place.

• Merge & Quick Sort → faster, used in real life.

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📘 Part 7: Advanced Math

• Factorial → 5! = 5×4×3×2×1 = 120

• Permutations (order matters) → number of ways to arrange items

• Combinations (order doesn’t matter) → number of ways to choose items

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📘 Part 8: OOP in Python

class Dog:
    def __init__(self, name):  # constructor
        self.name = name       # attribute

    def bark(self):            # method
        print(self.name + " says Woof!")

dog1 = Dog("Buddy")  # create object
dog1.bark()          # Output: Buddy says Woof!

• self → refers to the object itself (must always be used inside class).

• Method vs Function

  • Function → independent

  • Method → belongs to a class/object

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📘 Part 9: Flowcharts & Problem Solving

• Flowcharts help visualize logic.

• Example: A game where user inputs a number, computer generates random number → compare → check win/loss.

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🔹 Cohesion (togetherness inside a class/module)

• Meaning: How strongly related the functions inside one class are.

• High Cohesion = everything in the class is focused on one clear job.

• Low Cohesion = class does many unrelated jobs → confusing & hard to maintain.

✅ Example:

# High Cohesion (Payment related only)
class PaymentProcessor:
    def validate_payment(self): pass
    def process_payment(self): pass
    def send_receipt(self): pass

This class only handles payment-related work → high cohesion.

# Low Cohesion (Mixing many jobs)
class Utility:
    def send_email(self): pass
    def calculate_salary(self): pass
    def draw_graph(self): pass

Here, one class is trying to do too many different things → low cohesion.

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🔹 Coupling (connections between classes/modules)

• Meaning: How much one class depends on another class.

• Tightly Coupled = classes know too much about each other → hard to change one without breaking the other.

• Loosely Coupled = classes depend only on essential information → flexible and easy to maintain.

✅ Example:

# Loose Coupling (better)
class PaymentProcessor:
    def process(self, payment_method):
        payment_method.pay()

class CreditCard:
    def pay(self): print("Paying with Credit Card")

class PayPal:
    def pay(self): print("Paying with PayPal")

processor = PaymentProcessor()
processor.process(PayPal())  # Can easily swap methods

Here, PaymentProcessor only needs to know that payment_method has a pay() function.
It doesn’t care how PayPal or CreditCard works inside → loosely coupled.

# Tight Coupling (bad)
class PaymentProcessor:
    def pay_with_creditcard(self): pass
    def pay_with_paypal(self): pass

If you add a new method (e.g., Bitcoin), you must modify the class.
This makes the system hard to extend → tightly coupled.

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🔹 Rule of Thumb

👉 Good software should be Highly Cohesive + Loosely Coupled:

• Each class does one job well (high cohesion).

• Classes know only what they must about each other (loose coupling).

That’s how professional software is easier to read, test, reuse, and maintain.

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🔹 What is Abstraction?

Abstraction = show only important details and hide the unnecessary ones.
👉 Example: When you drive a car, you just use the steering wheel & pedals.
You don’t see the engine’s wires or fuel injection system — that’s hidden.

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🔹 Types of Abstraction in OOP

1. Classification Abstraction

• Meaning: Grouping similar objects into classes.

• Analogy: Labrador, Poodle, Bulldog → all grouped as Dog.

• Focus: Looks at similarities.

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

dog1 = Dog("Labrador")
dog2 = Dog("Poodle")

Here, Dog is a general class for all breeds.

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2. Generalization Abstraction

• Meaning: Creating parent-child relationships (inheritance).

• Analogy: "Mammal" is a general class → Dog, Cat, Horse are children.

• Focus: Top-down → from general → specific.

class Animal:   # Parent
    def breathe(self): print("Breathing")

class Dog(Animal):   # Child
    def bark(self): print("Woof!")

d = Dog()
d.breathe()  # Inherited
d.bark()

Here, Dog inherits general features of Animal.

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3. Aggregation Abstraction (weak “has-a” relation)

• Meaning: One object contains another, but parts can live independently.

• Analogy: A Car has a Radio. The Radio can exist without the Car.

class Radio:
    def play(self): print("Playing music")

class Car:
    def __init__(self, radio):
        self.radio = radio   # "has-a" relation

my_radio = Radio()
my_car = Car(my_radio)
my_car.radio.play()

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4. Composition Abstraction (strong “part-of” relation)

• Meaning: Stronger form of aggregation → parts cannot exist independently.

• Analogy: A House has Rooms. Rooms do not exist outside the house.

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

class House:
    def __init__(self):
        self.rooms = [Room("Living"), Room("Bedroom")]

h = House()
for r in h.rooms:
    print(r.name)

Here, Room objects exist only because the House exists.

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5. Behavioral Abstraction

• Meaning: Hiding implementation details of methods, only showing what they do.

• Analogy: You click “Print” → you don’t know if it’s laser/inkjet, but you get output.

• Focus: Method signatures, not how they work inside.

class Printer:
    def print_document(self):   # behavior is abstract
        raise NotImplementedError("Subclasses must implement")

class LaserPrinter(Printer):
    def print_document(self):
        print("Printing with laser...")

class InkjetPrinter(Printer):
    def print_document(self):
        print("Printing with inkjet...")

p = InkjetPrinter()
p.print_document()

Here, print_document() hides details, but each printer implements it differently.

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🔹 Summary

Type	Focus	Example
Classification	Grouping similar objects	Dogs → Dog class
Generalization	Parent-child hierarchy	Animal → Dog, Cat
Aggregation	Weak "has-a"	Car has a Radio
Composition	Strong "part-of"	House has Rooms
Behavioral	Hide method details	Printer with different implementations

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.

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🔹 Classification

• Meaning: Grouping similar objects into classes based on common properties.

• Approach: Bottom-Up → start from specific objects → group them.

• Question it answers: “What category does this object belong to?”

✅ Example:

• Labrador, Poodle, Bulldog → all grouped as Dog.

• Cat, Tiger, Lion → all grouped as Cat family.

# Classification Example
class Dog:
    def __init__(self, breed):
        self.breed = breed

dog1 = Dog("Labrador")
dog2 = Dog("Poodle")
print(dog1.breed)   # Labrador

Here, classification means: we group similar objects → all are Dogs.

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🔹 Generalization

• Meaning: Create hierarchies where specific classes inherit from general ones.

• Approach: Top-Down → start from general concept → create specialized ones.

• Question it answers: “What is the more general concept that covers this?”

✅ Example:

• “Dog”, “Cat”, “Horse” → all are generalized as Animal.

# Generalization Example
class Animal:  # General (parent)
    def breathe(self): print("Breathing")

class Dog(Animal):  # Specific (child)
    def bark(self): print("Woof!")

class Cat(Animal):  # Specific (child)
    def meow(self): print("Meow!")

d = Dog()
d.breathe()   # Inherited from Animal
d.bark()

Here, generalization means: we create a general Animal class → and let Dog & Cat inherit from it.

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🔹 Key Differences (Side-by-Side)

Feature	Classification	Generalization
Definition	Grouping similar objects into categories	Building parent-child relationships
Direction	Bottom-Up (from specific → category)	Top-Down (from general → specialized)
Focus	Similarities among objects	Hierarchies & inheritance
Example	Labrador + Poodle + Bulldog = Dog class	Dog, Cat, Horse → inherit from Animal class
Question	“What category is this object in?”	“What general concept does this belong to?”

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🔹 Real-Life Analogy

• Classification:

  • You look at fruits → Apple, Banana, Orange → group them as Fruits.

• Generalization:

  • You start with “Plant” → then generalize into “Fruit” and “Vegetable”.

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✅ In short:

• Classification organizes objects into groups.

• Generalization creates parent-child hierarchies among those groups.

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🃏 Flashcards for OOP with Python 

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🔹 Beginner (Basics)

Q1: What is Object Orientation?
A1: A way of designing programs around objects (real-world entities) that have attributes (data) and methods (behavior).

Q2: What is a Class?
A2: A blueprint/template for creating objects.

Q3: What is an Object?
A3: An instance of a class (real thing created from the blueprint).

Q4: What are the 4 main principles of OOP?
A4: Encapsulation, Inheritance, Polymorphism, Abstraction.

Q5: What is the difference between Attributes and Methods?
A5: Attributes = variables (data of object). Methods = functions (behavior of object).

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🔹 Encapsulation & Abstraction

Q6: What is Encapsulation?
A6: Hiding internal details of a class and controlling access using getters & setters.

Q7: Example of Encapsulation in Python?
A7:

class Bank:
    def __init__(self, balance):
        self.__balance = balance
    def get_balance(self):
        return self.__balance

Q8: What is Abstraction?
A8: Showing only necessary features, hiding the implementation details.

Q9: Types of Abstraction?
A9: Classification, Generalization, Aggregation, Composition, Behavioral.

Q10: Example of Abstraction in real life?
A10: Driving a car → you use the steering wheel (interface), but don’t see engine details.

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🔹 Inheritance & Polymorphism

Q11: What is Inheritance?
A11: When a class (child) inherits attributes & methods from another class (parent).

Q12: What is Polymorphism?
A12: Same method name, different behavior depending on object.

Q13: Python Example of Polymorphism?
A13:

class Dog:
    def speak(self): print("Woof!")
class Cat:
    def speak(self): print("Meow!")
for animal in [Dog(), Cat()]:
    animal.speak()

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🔹 Cohesion & Coupling

Q14: What is High Cohesion?
A14: A class does only one job well (focused).

Q15: What is Low Coupling?
A15: Classes are independent and interact only through simple interfaces.

Q16: Ideal design principle?
A16: High Cohesion + Low Coupling → modular, reusable code.

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🔹 Python Basics from PDF

Q17: List vs Tuple vs Set vs Dictionary?
A17:

• List → ordered, mutable, allows duplicates

• Tuple → ordered, immutable

• Set → unordered, unique values only

• Dictionary → key-value pairs

Q18: What is Indentation in Python?
A18: Spaces at the start of a line that define blocks of code (instead of {}).

Q19: What does break and continue do in loops?
A19: break exits loop, continue skips current iteration.

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🔹 Control Flow

Q20: Difference between Sequence, Selection, and Repetition?
A20:

• Sequence → step-by-step execution

• Selection → decision-making (if/else)

• Repetition → looping (for, while)

Q21: Deterministic vs Non-Deterministic Loops?
A21:

• Deterministic → fixed iterations (e.g., for i in range(5))

• Non-Deterministic → unknown iterations (e.g., while user_input != "stop")

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🔹 Functions & Recursion

Q22: What is a Function?
A22: A reusable block of code with name, parameters, and optional return value.

Q23: Difference between Function and Method?
A23:

• Function = standalone block

• Method = belongs to a class/object

Q24: What is Recursion?
A24: A function calling itself until a base case is reached.

Q25: Recursion vs Iteration?
A25:

• Recursion → uses call stack, may risk stack overflow

• Iteration → uses loops, more memory efficient

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🔹 Algorithms

Q26: What is Bubble Sort?
A26: Repeatedly swap adjacent elements if out of order.

Q27: What is Selection Sort?
A27: Repeatedly find the minimum element and place it in order.

Q28: What is Insertion Sort?
A28: Insert each element into the correct position in sorted part.

Q29: What is Merge Sort?
A29: Divide & conquer → split list, sort halves, merge results.

Q30: What is Quick Sort?
A30: Choose a pivot, partition list, sort sublists recursively.

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🔹 Math & Probability

Q31: What is Factorial?
A31: n! = n × (n-1) × ... × 1

Q32: What is a Permutation?
A32: Selection where order matters → nPk = n! / (n-k)!

Q33: What is a Combination?
A33: Selection where order does NOT matter → nCk = n! / (k!(n-k)!)

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🔹 Advanced OOP in Python

Q34: What is self in Python classes?
A34: Refers to the current object instance (needed for attributes & methods).

Q35: Why use __ (double underscore) before variable names?
A35: To make them private → accessed only through getters/setters.

Q36: What is the difference between Public, Protected, and Private attributes in Python?
A36:

• Public → normal (name) → accessible anywhere

• Protected → _name → “internal use only” (by convention)

• Private → __name → hidden, only accessible inside class

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🔹 Flowcharts & Problem Solving

Q37: Why use Flowcharts for algorithms?
A37: Visual clarity, easier debugging, error detection, and communication.

Q38: Example: Game logic flashcard
Q38A: User inputs number → computer generates random → compare → if 2+ matches in 3 tries → Win.

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