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7. Advanced OOP Concepts

7.1 Composition vs Inheritance – When to Use Which

Both composition and inheritance let you reuse code, but they model relationships differently.

AspectInheritance ("is-a")Composition ("has-a")Winner (most cases)RelationshipChild is a kind of ParentClass has a part/componentCompositionCode ReuseInherits all public/protected membersExplicitly includes other classes as attributes—FlexibilityFixed hierarchy (hard to change later)Very flexible (can swap components)CompositionTight CouplingHigh (child depends heavily on parent)Low (loose coupling)CompositionWhen to preferClear "is-a" hierarchy (Dog is an Animal)"has-a" or "uses-a" (Car has an Engine)—Fragile base classCommon problem (changing parent breaks children)RareCompositionMultiple parentsSupported (but complex MRO)Multiple components easilyComposition

Inheritance Example

Python

class Engine: def start(self): return "Engine started" class Car(Engine): # Car is an Engine? (not really good modeling) def drive(self): return self.start() + " → Car moving"

Composition Example (preferred)

Python

class Engine: def start(self): return "Engine started" class Car: def init(self): self.engine = Engine() # Car has an Engine def drive(self): return self.engine.start() + " → Car moving" car = Car() print(car.drive()) # Engine started → Car moving

Golden Rule (2026 best practice): "Favor composition over inheritance" Use inheritance only when there is a clear, stable "is-a" relationship and you truly want to inherit behavior. Use composition for most other cases — it's more flexible, testable, and maintainable.

7.2 Data Classes (@dataclass) – Reduce Boilerplate Code

Introduced in Python 3.7, @dataclass automatically adds init, repr, eq, and more — reducing repetitive code.

Basic usage

Python

from dataclasses import dataclass @dataclass class Person: name: str age: int = 0 city: str = "Unknown" p = Person("Anshuman", 25, "Muzaffarpur") print(p) # Person(name='Anshuman', age=25, city='Muzaffarpur') print(p == Person("Anshuman", 25, "Muzaffarpur")) # True (auto eq)

Advanced options

Python

@dataclass(frozen=True) # immutable (like namedtuple) class Point: x: float y: float @dataclass(order=True) # adds <, >, <=, >= comparison class Student: name: str marks: int s1 = Student("Rahul", 85) s2 = Student("Priya", 92) print(s1 < s2) # True (compares marks)

With default factory (mutable defaults safe)

Python

from dataclasses import dataclass, field @dataclass class Team: name: str players: list[str] = field(default_factory=list) # new list each time t = Team("India") t.players.append("Virat") print(t.players) # ['Virat']

Benefits over normal class:

• No need to write init, repr, eq manually

• Type hints are used for clarity and tools (mypy)

• Safer mutable defaults with field(default_factory=...)

7.3 Magic Methods / Dunder Methods Deep Dive

Dunder methods (double underscore) let you customize object behavior.

Most useful ones (beyond init, str, repr):

MethodPurposeExample Use Case__len__len(obj)len(my_vector)__getitem__obj[index]Custom list/dict-like classes__setitem__obj[index] = valueMutable custom containers__iter__for x in objMake class iterable__next__next(obj)Custom iterator__call__obj()Functor / callable object__enter__ / exitwith obj: ...Context manager__bool__if obj: ...Truth value testing__hash__hash(obj)Use as dict key (must implement with eq)__slots__Memory optimizationReduce memory for many instances

Example – Custom Vector with operators

Python

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 len(self): return 2 def getitem(self, index): return (self.x, self.y)[index] def str(self): return f"Vector({self.x}, {self.y})" v1 = Vector(3, 4) v2 = Vector(1, 2) print(v1 + v2) # Vector(4, 6) print(len(v1)) # 2 print(v1[0]) # 3

7.4 Metaclasses – The Class Behind the Class (type() and Custom Metaclasses)

A metaclass is the class of a class — it controls how classes are created.

Default metaclass = type

Python

class MyClass: pass print(type(MyClass)) # <class 'type'>

Creating class dynamically with type()

Python

def hello(self): return "Hello from dynamic class!" DynamicClass = type( "DynamicClass", (object,), {"hello": hello, "value": 42} ) obj = DynamicClass() print(obj.hello()) # Hello from dynamic class! print(obj.value) # 42

Custom metaclass example – Auto-add docstring check

Python

class RequireDocstringMeta(type): def new(cls, name, bases, attrs): for attr_name, value in attrs.items(): if callable(value) and not attr_name.startswith("_"): if not value.__doc__: raise TypeError(f"Method {name}.{attr_name} needs docstring!") return super().__new__(cls, name, bases, attrs) class MyService(metaclass=RequireDocstringMeta): def process(self): """This has docstring – OK""" pass # def invalid(self): # TypeError if uncommented # pass

Real-world use cases of metaclasses:

• Django models (auto-creates database mapping)

• SQLAlchemy declarative base

• Auto-registration of plugins/commands

• Enforcing class standards (docstrings, naming)

Important advice (2026): Metaclasses are very powerful but make code harder to understand. Use them only when simpler solutions (decorators, __init_subclass__, class decorators) are not enough.

Mini Summary Project – Singleton via Metaclass

Python

class SingletonMeta(type): instances = {} def call_(cls, args, kwargs): if cls not in cls._instances: cls._instances[cls] = super().__call__(args, **kwargs) return cls._instances[cls] class Database(metaclass=SingletonMeta): def connect(self): print("Connected to database") db1 = Database() db2 = Database() print(db1 is db2) # True – same instance

This completes the full Advanced OOP Concepts section — now you understand composition, data classes, dunder methods, and even metaclasses!

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