Comprehensive Guide on Design Patterns in Python: Types, Importance and When to Use

As a programmer or a developer, one challenge remains universal: solving complex problems while keeping your code clean, maintainable, and adaptable. This is where design patterns come into play, offering tried-and-tested solutions to common coding challenges.

This blog will uncover their significance, types, and real-world applications. Let’s get started!

What Are Design Patterns in Python? Key Concepts Explained

When you solve a challenging puzzle without any prior strategy, you’ll likely waste time figuring out the best approach. The same applies to coding. When you’re building software, certain problems tend to recur.

Design patterns in Python are pre-established solutions to these recurring problems, offering you a blueprint to write clean, scalable, and efficient code. For someone diving into Python development, understanding these patterns is like learning shortcuts to achieve long-term coding excellence. 

Here’s a breakdown of what makes design patterns in Python indispensable:

• Reusable Solutions: Design patterns provide tested solutions to common coding problems, saving time and effort in development.
• Enhanced Code Readability: They structure your code intuitively and easily to maintain, making collaboration smoother.
• Improved Scalability: Patterns help you create software that can grow as user demands and data increase, avoiding bottlenecks.
• Standardized Practices: They act as a universal language, helping teams align on best practices and methodologies.

Let’s say you’re managing a database connection in your application. You want to ensure that only one instance of the connection exists throughout the app. Instead of manually tracking this, the Singleton pattern can enforce this behavior.

Don't worry. You’ll learn more about singleton and other design patterns ahead!

Now, how exactly are the design patterns in Python configured? Let’s see!

What is The Configuration of Design Patterns in Python? Insights

Think of design patterns as adaptable frameworks that provide flexible guidelines to address specific coding problems rather than being direct plug-and-play solutions. Understanding their configuration is essential because it allows you to break down complex issues into manageable parts.

At its core, the configuration of design patterns in Python revolves around its structure, participants, and collaboration. 

Let’s explore these elements in detail to demystify the concept for you:

1. Problem Context
Every design pattern begins with a clearly defined problem. For instance, do you need to manage object creation dynamically? Or are you looking to control relationships between classes? 

Identifying the problem is the first step in configuring the correct design pattern.

2. Structure
A design pattern’s structure is like a roadmap, defining how classes and objects interact. This involves understanding UML diagrams, class hierarchies, and object flows. 

For example, the factory design pattern in Python uses a creator class to instantiate objects based on specific conditions.

3. Participants
Each design pattern has key participants, such as classes, objects, and methods. These participants have specific roles. For example:

• Singleton Pattern: The participant is the singleton class responsible for controlling instance creation.
• Observer Pattern: Includes subjects and observers that communicate updates.

4. Interaction Rules
Once participants are defined, the next step is establishing how they collaborate. These rules ensure seamless communication between objects or classes, adhering to the pattern’s objective. 

For example, in the factory method pattern Python, the interaction involves calling a factory method to delegate object creation.

5. Customization and Flexibility
No design pattern is rigid. Its configuration allows customization based on your project requirements. This flexibility ensures the pattern remains relevant across varied scenarios.

Understanding these configurations allows you to adapt design patterns to suit any coding challenge. 

Also Read: Software Design Patterns: A Complete Guide for 2025

Types of Design Patterns in Python: A Comprehensive Overview

Design patterns in Python are not a one-size-fits-all solution. They are versatile and categorized into three main groups — Creational, Structural, and Behavioral — each addressing unique aspects of software design. 

Together, they create a cohesive, functional system. These categories offer you a structured way to think about and implement patterns that suit your project’s specific needs.

Let’s explore these types of design patterns in Python in detail.

Creational Design Patterns

Creational design patterns are the architects of software development, focusing on how objects are created. Instead of instantiating objects directly,  you can centralize the creation logic, making your code easier to maintain and extend.

Let’s dive into each creational design pattern and explore its intricacies through analogies, examples, and code.

1. Factory Design Pattern in Python

The factory design pattern in Python defines an interface or method to create objects but lets subclasses decide which class to instantiate. It’s useful when the exact object type isn’t known until runtime.

Analogy: Imagine a car dealership where customers can request different types of cars (SUVs, sedans, or hatchbacks). Instead of manufacturing every car on demand, the dealership uses a factory system

Code Example:

# Factory Pattern Example: Car Factory

class Car:
    def drive(self):
        pass

class SUV(Car):
    def drive(self):
        return "Driving an SUV - Spacious and powerful!"

class Sedan(Car):
    def drive(self):
        return "Driving a Sedan - Sleek and efficient!"

class Hatchback(Car):
    def drive(self):
        return "Driving a Hatchback - Compact and agile!"

class CarFactory:
    @staticmethod
    def create_car(car_type):
        if car_type == "SUV":
            return SUV()
        elif car_type == "Sedan":
            return Sedan()
        elif car_type == "Hatchback":
            return Hatchback()
        else:
            raise ValueError("Unknown car type!")

# Usage
car = CarFactory.create_car("SUV")
print(car.drive())

Output:

Driving an SUV - Spacious and powerful!

Explanation:

• The CarFactory class handles object creation based on input.
• This keeps the main code clean, with creation logic abstracted away.

2. Abstract Factory Method Pattern Python

The abstract factory method pattern Python provides an interface for creating families of related or dependent objects without specifying their concrete classes. It is beneficial when the creation needs to remain flexible.

This factory method pattern Python also integrates seamlessly with dependency injection, allowing developers to decouple object creation from the code that uses these objects.

Analogy: Think of a furniture store where you can order a modern or vintage set. Instead of mixing styles, the store ensures that all items in a set follow the chosen theme. The abstract factory ensures compatibility across related objects.

Code Example:

# Abstract Factory Pattern Example: Furniture Factory

class Chair:
    def describe(self):
        pass

class ModernChair(Chair):
    def describe(self):
        return "A sleek, modern chair."

class VintageChair(Chair):
    def describe(self):
        return "An elegant, vintage chair."

class Table:
    def describe(self):
        pass

class ModernTable(Table):
    def describe(self):
        return "A minimalist, modern table."

class VintageTable(Table):
    def describe(self):
        return "A grand, vintage table."

class FurnitureFactory:
    def create_chair(self):
        pass

    def create_table(self):
        pass

class ModernFurnitureFactory(FurnitureFactory):
    def create_chair(self):
        return ModernChair()

    def create_table(self):
        return ModernTable()

class VintageFurnitureFactory(FurnitureFactory):
    def create_chair(self):
        return VintageChair()

    def create_table(self):
        return VintageTable()

# Usage
factory = ModernFurnitureFactory()
chair = factory.create_chair()
table = factory.create_table()

print(chair.describe())
print(table.describe())

Output:

A sleek, modern chair.
A minimalist, modern table.

Explanation:

• The abstract factory ensures that related objects (chairs and tables) are created as a cohesive set.
• This is useful when your objects must follow a specific theme or category.

3. Builder Method

The builder design pattern focuses on constructing a complex object step by step. It separates the construction logic from the final representation.

Analogy: Think of a restaurant where customers can customize their sandwiches. Instead of making fixed menu items, the builder constructs sandwiches based on each customer’s preferences.

Code Example:

# Builder Pattern Example: Sandwich Builder

class Sandwich:
    def __init__(self):
        self.ingredients = []

    def add_ingredient(self, ingredient):
        self.ingredients.append(ingredient)

    def describe(self):
        return f"Sandwich with {', '.join(self.ingredients)}."

class SandwichBuilder:
    def __init__(self):
        self.sandwich = Sandwich()

    def add_bread(self):
        self.sandwich.add_ingredient("bread")
        return self

    def add_meat(self):
        self.sandwich.add_ingredient("meat")
        return self

    def add_veggies(self):
        self.sandwich.add_ingredient("veggies")
        return self

    def add_sauce(self):
     self.sandwich.add_ingredient("sauce")
        return self

    def build(self):
        return self.sandwich

# Usage
builder = SandwichBuilder()
sandwich = builder.add_bread().add_meat().add_veggies().add_sauce().build()
print(sandwich.describe())

Output:

Sandwich with bread, meat, veggies, sauce.

Explanation:

• The Builder pattern lets you construct objects incrementally.
• It’s ideal for creating objects with many optional components.

4. Prototype Method

The prototype design patterns in Python are used to create object clones, avoiding the overhead of instantiating objects directly.

Analogy: Imagine you’re designing a catalog of products. Instead of creating each item from scratch, you copy a prototype of an existing product and tweak it.

Code Example:

# Prototype Pattern Example: Product Cloning

import copy

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

    def clone(self):
        return copy.deepcopy(self)

# Usage
product1 = Product("Laptop", 1500)
product2 = product1.clone()
product2.name = "Tablet"

print(product1.name)
print(product2.name)

Output:

Laptop
Tablet

Explanation:

• The clone method enables object duplication.
• This is useful when object creation is resource-intensive.

5. Singleton Method

The singleton design pattern ensures that a class has only one instance and provides a global point of access to it. It is commonly used to manage shared resources like database connections, configuration settings, and logging mechanisms.

Analogy: Imagine a control tower at an airport. No matter how many pilots request instructions, there’s always a single, consistent control tower to guide them.

Code Example:

# Singleton Pattern Example: Database Connection

class DatabaseConnection:
    _instance = None

    def __new__(cls, *args, **kwargs):
        if not cls._instance:
            cls._instance = super().__new__(cls)
            cls._instance.connection = "Database Connected"
        return cls._instance

# Usage
db1 = DatabaseConnection()
db2 = DatabaseConnection()

print(db1.connection)
print(db1 is db2)

Output: 

Database Connected
True

Explanation:

• The singleton pattern prevents multiple instances of the same class.
• This is ideal for managing shared resources like databases or loggers.

Also, in Python, threading issues can arise when multiple threads try to create an instance of a singleton class simultaneously, leading to duplicate instances. Let’s implement a thread-safe Singleton to handle this challenge.

Code Example with Thread-Safe Singleton:

# Thread-Safe Singleton Implementation

import threading

class SingletonMeta(type):
    """
    A metaclass for creating Singleton classes. Ensures only one instance exists.
    """
    _instances = {}
    _lock = threading.Lock()  # Ensures thread-safe access to the Singleton

    def __call__(cls, *args, **kwargs):
        # Double-checked locking
        if cls not in cls._instances:
            with cls._lock:  # Ensure only one thread can access this block at a time
                if cls not in cls._instances:
                    instance = super().__call__(*args, **kwargs)
                    cls._instances[cls] = instance
        return cls._instances[cls]

class Singleton(metaclass=SingletonMeta):
    def __init__(self):
        self.value = None

# Usage
def singleton_test(value):
    singleton = Singleton()
    singleton.value = value
    print(f"Singleton Value: {singleton.value}")

# Creating threads
thread1 = threading.Thread(target=singleton_test, args=("Thread-1 Value",))
thread2 = threading.Thread(target=singleton_test, args=("Thread-2 Value",))

thread1.start()
thread2.start()

thread1.join()
thread2.join()

Output:

Singleton Value: Thread-1 Value
Singleton Value: Thread-1 Value

(Note: The value printed is consistent across threads, showing that only one instance exists.)

Also Read: Design Patterns: Singleton in Java

These creational design patterns give you powerful tools to manage object creation, optimize resources, and maintain clean code structures.

Structural Design Patterns

Structural design patterns organize classes and objects to ensure clean, extensible, and maintainable code. They emphasize how components work together to form a cohesive system, optimizing relationships while keeping each part loosely coupled.

Let’s explore the structural design patterns in Python using analogies, examples, and code explanations.

1. Adapter Method

The adapter pattern bridges two incompatible interfaces, allowing classes or systems that couldn’t work together to interact seamlessly. It does this by wrapping an existing class with a new interface in Python that the client expects. 

This pattern is particularly useful when integrating legacy systems with modern applications or reusing existing code without modifying its structure.

Analogy: Suppose you’re modernizing an application that interacts with an old payment gateway. The legacy gateway uses a payment method different from what the modern system expects. You can bridge this gap using the adapter without altering the legacy gateway’s code.

Code Example:

# Adapter Pattern Example: Legacy Payment Gateway Adapter

# Legacy system (incompatible with the modern system)
class LegacyPaymentGateway:
    def make_payment(self, amount):
        return f"Processed payment of ${amount} through the legacy gateway."

# Modern interface expected by the client
class PaymentProcessor:
    def process_payment(self, amount):
        pass

# Adapter to bridge the legacy gateway with the modern interface
class PaymentGatewayAdapter(PaymentProcessor):
    def __init__(self, legacy_gateway):
        self.legacy_gateway = legacy_gateway

    def process_payment(self, amount):
        # Delegates the call to the legacy gateway
        return self.legacy_gateway.make_payment(amount)

# Usage
legacy_gateway = LegacyPaymentGateway()
adapter = PaymentGatewayAdapter(legacy_gateway)

# The client uses the modern interface to interact with the legacy system
print(adapter.process_payment(100))

Output:

Processed payment of $100 through the legacy gateway.

Explanation:

• LegacyPaymentGateway (Legacy System): Represents the old system with its unique interface (make_payment).
• PaymentProcessor (Modern Interface): Defines the interface the client expects (process_payment).
• PaymentGatewayAdapter (Adapter): Bridges the gap by wrapping the legacy system and implementing the modern interface.
• Usage: The client interacts with PaymentGatewayAdapter, translating the modern interface’s request to the legacy system’s method.

Also Read: Adapter Class in Java: Practical Guide with Examples

2. Bridge Method

The bridge pattern decouples abstraction (what the object does) from implementation (how the object does it), enabling both to vary independently. Having multiple abstractions and implementations that can mix and match is beneficial.

Analogy: Think of a TV remote. The remote controls various brands and models of TVs, but its interface remains the same. The Bridge pattern separates the abstraction (remote) from its implementation (TV).

Code Example:

# Bridge Pattern Example: TV Remote

class TV:
    def on(self):
        pass

    def off(self):
        pass

class SonyTV(TV):
    def on(self):
        return "Sony TV turned ON"

    def off(self):
        return "Sony TV turned OFF"

class SamsungTV(TV):
    def on(self):
        return "Samsung TV turned ON"

    def off(self):
        return "Samsung TV turned OFF"

class Remote:
    def __init__(self, tv):
        self.tv = tv

    def turn_on(self):
        return self.tv.on()

    def turn_off(self):
        return self.tv.off()

# Usage
sony_remote = Remote(SonyTV())
print(sony_remote.turn_on())
print(sony_remote.turn_off())

Output:

Sony TV turned ON
Sony TV turned OFF

Explanation:

• TV (Abstraction): The base class that defines everyday operations (on and off) for all TVs.
• SonyTV and SamsungTV (Concrete Implementations): Implement the abstract methods with specific behaviors for each brand.
• Remote (Bridge): Acts as the intermediary that uses a TV instance to perform operations. The abstraction (Remote) and implementation (TV) are loosely coupled.
• Usage: The client uses the Remote without knowing the internal details of the TV.

3. Composite Method

The composite pattern allows you to build tree-like structures where individual objects and groups can be treated uniformly. It’s handy in file systems, where folders can contain both files and other folders.

Analogy: Picture a tree. Each branch can have leaves or other branches. The Composite pattern lets you treat individual objects and compositions uniformly, like branches and leaves.

Code Example:

# Composite Pattern Example: File System

class FileSystemComponent:
    def display(self):
        pass

class File(FileSystemComponent):
    def __init__(self, name):
        self.name = name

    def display(self):
        return self.name

class Folder(FileSystemComponent):
    def __init__(self, name):
        self.name = name
        self.children = []

    def add(self, component):
        self.children.append(component)

    def display(self):
        return f"Folder: {self.name}, Contents: {[child.display() for child in self.children]}"

# Usage
file1 = File("File1.txt")
file2 = File("File2.txt")
folder = Folder("MyFolder")
folder.add(file1)
folder.add(file2)

print(folder.display())

Output:

Folder: MyFolder, Contents: ['File1.txt', 'File2.txt']

Explanation:

• FileSystemComponent (Base Class): Defines a common interface for all components, whether individual files or folders.
• File (Leaf): Represents individual components that cannot contain other objects.
• Folder (Composite): Holds child components, files or other folders. It implements logic to manage and display its contents.
• Usage: The client interacts with a Folder object, which manages its child components internally.

4. Decorator Method

The decorator method allows the dynamic addition of behavior or responsibilities to objects without altering their structure. Unlike inheritance, Python decorators provide a flexible alternative for runtime behavior modification.

Analogy: Imagine ordering coffee. You start with a basic coffee, then add milk, sugar, or flavors. The Decorator pattern dynamically adds responsibilities to an object.

Code Example:

# Decorator Pattern Example: Coffee Shop

class Coffee:
    def cost(self):
        return 5

    def description(self):
        return "Basic Coffee"

class MilkDecorator:
    def __init__(self, coffee):
        self.coffee = coffee

    def cost(self):
        return self.coffee.cost() + 2

    def description(self):
        return f"{self.coffee.description()} with Milk"

# Usage
basic_coffee = Coffee()
milk_coffee = MilkDecorator(basic_coffee)

print(milk_coffee.description())
print(f"Cost: {milk_coffee.cost()}")

Output:

Basic Coffee with Milk
Cost: 7

Explanation:

• Coffee (Base Class): Represents the core component with a base cost and description.
• MilkDecorator (Decorator): Wraps the base component and adds specific functionality (e.g., adding milk). It delegates unmodified methods to the wrapped object while enhancing others.
• Usage: The client can layer multiple decorators to build a custom object (e.g., Coffee + MilkDecorator).

5. Facade Method

The facade pattern simplifies the interaction with complex subsystems by providing a unified interface. This reduces the need for clients to understand the inner workings of multiple components, improving usability and reducing coupling.

Analogy: Think of a restaurant. You don’t interact with the chef, cashier, and waiter separately. Instead, the manager handles everything for you. The Facade pattern simplifies interactions by providing a unified interface.

Code Example:

# Facade Pattern Example: Home Theater System

class DVDPlayer:
    def play(self):
        return "DVD Player: Playing movie."

class SoundSystem:
    def on(self):
        return "Sound System: Surround sound enabled."

class Projector:
    def start(self):
        return "Projector: Projecting video."

class HomeTheaterFacade:
    def __init__(self):
        self.dvd_player = DVDPlayer()
        self.sound_system = SoundSystem()
        self.projector = Projector()

    def watch_movie(self):
        return f"{self.dvd_player.play()} {self.sound_system.on()} {self.projector.start()}"

# Usage
theater = HomeTheaterFacade()
print(theater.watch_movie())

Output:

DVD Player: Playing movie. Sound System: Surround sound enabled. Projector: Projecting video.

Explanation:

• Subsystems (DVDPlayer, SoundSystem, Projector): These are independent components with their functionalities.
• HomeTheaterFacade (Facade): Encapsulates the complexity of interacting with the subsystems by providing a single watch_movie method.
• Usage: The client interacts only with HomeTheaterFacade, which internally coordinates the subsystems.

6. Proxy Method

The proxy method provides a placeholder for another object to control access to it. It is useful for adding functionality such as lazy initialization, security, or logging to an existing class without modifying its structure.

Analogy: Think of a bank ATM. It acts as a proxy for accessing your account. You don’t interact directly with the bank; the ATM provides controlled access.

Code Example:

# Proxy Pattern Example: Internet Access

class InternetAccess:
    def connect(self):
        pass

class RealInternet(InternetAccess):
    def connect(self):
        return "Connecting to the internet."

class ProxyInternet(InternetAccess):
    def __init__(self):
        self.banned_sites = ["banned.com"]

    def connect(self, site):
        if site in self.banned_sites:
            return f"Access denied to {site}"
        return RealInternet().connect()

# Usage
proxy = ProxyInternet()
print(proxy.connect("banned.com"))
print(proxy.connect("example.com"))

Output:

Access denied to banned.com
Connecting to the internet.

Explanation:

• InternetAccess (Base Class): Defines the interface for accessing the internet.
• RealInternet (Real Object): Represents the actual object providing the functionality (connecting to the internet).
• ProxyInternet (Proxy): Controls access to the RealInternet, implementing additional logic (e.g., banning specific websites).
• Usage: The client interacts with the proxy, deciding whether to delegate requests to the object or deny access.

7. Flyweight Method

The flyweight pattern minimizes memory usage by sharing objects that are identical in some ways but differ in others. It separates shared (intrinsic) properties from non-shared (extrinsic) ones to efficiently create many objects.

Analogy: Imagine a chess game where every black pawn shares the same intrinsic properties (size, color) but has unique positions. The Flyweight pattern minimizes memory usage by sharing common parts.

Code Example:

# Flyweight Pattern Example: Chess Pieces

class ChessPiece:
    _instances = {}

    def __new__(cls, piece_type):
        if piece_type not in cls._instances:
            cls._instances[piece_type] = super().__new__(cls)
            cls._instances[piece_type].type = piece_type
        return cls._instances[piece_type]

# Usage
pawn1 = ChessPiece("Pawn")
pawn2 = ChessPiece("Pawn")

print(pawn1 is pawn2)

Output:

True

Explanation:

• ChessPiece (Flyweight): Represents a shared instance. If a piece type already exists, it reuses the existing object; otherwise, it creates a new one.
• Usage: Multiple instances of ChessPiece are created for the same type (Pawn), but they all point to the same object, reducing memory usage.

These types demonstrate how structural solutions can simplify your codebase while enhancing flexibility and performance.

Behavioral Design Patterns

Behavioral design patterns in Python focus on how objects interact and communicate with each other. They help define clear, flexible, and reusable communication protocols between objects without making them tightly coupled.

Let’s delve into these patterns with rich analogies, practical examples, and detailed explanations.

1. Command Method

The command pattern encapsulates a request as an object, allowing you to parameterize objects with different requests, delay execution, or support undoable operations. It decouples the sender of a request from its receiver using a command interface.

Analogy: Imagine a TV remote. Each button (command) sends a specific signal to the TV, but the TV doesn’t know the internal workings of the remote. This decoupling ensures flexibility.

Code Example:

# Command Pattern Example: TV Remote

class Command:
    def execute(self):
        pass

class TurnOnTVCommand(Command):
    def __init__(self, tv):
        self.tv = tv

    def execute(self):
        return self.tv.turn_on()

class TurnOffTVCommand(Command):
    def __init__(self, tv):
        self.tv = tv

    def execute(self):
        return self.tv.turn_off()

class TV:
    def turn_on(self):
        return "TV is ON"

    def turn_off(self):
        return "TV is OFF"

class RemoteControl:
    def __init__(self):
        self.command = None

    def set_command(self, command):
        self.command = command

    def press_button(self):
        return self.command.execute()

# Usage
tv = TV()
remote = RemoteControl()

turn_on_command = TurnOnTVCommand(tv)
turn_off_command = TurnOffTVCommand(tv)

remote.set_command(turn_on_command)
print(remote.press_button())
remote.set_command(turn_off_command)
print(remote.press_button())

Output:

TV is ON
TV is OFF

Code Explanation:

• Command (Interface): Defines the contract for all commands.
• TurnOnTVCommand and TurnOffTVCommand (Concrete Commands): Implement specific behaviors for turning the TV on and off.
• RemoteControl (Invoker): Invokes the command without knowing the receiver's details.
• TV (Receiver): Executes the actual operation when the command calls.

2. Observer Method

The observer pattern establishes a one-to-many dependency between objects. When one object (the Subject) changes state, it automatically notifies all its dependents (the Observers). 

This is particularly useful in event-driven programming, where multiple components must react to state changes without tight coupling.

Analogy: Imagine subscribing to a YouTube channel. When the channel posts a new video, all subscribers are notified without the channel needing to communicate with each subscriber directly. The channel is the subject, and the subscribers are the observers.

Example: React State Management

# Observer Pattern Example: Stock Price Notifications

class Subject:
    def __init__(self):
        self._observers = []

    def subscribe(self, observer):
        self._observers.append(observer)

    def unsubscribe(self, observer):
        self._observers.remove(observer)

    def notify_observers(self, data):
        for observer in self._observers:
            observer.update(data)

class StockMarket(Subject):
    def __init__(self):
        super().__init__()
        self.price = 0

    def update_price(self, new_price):
        self.price = new_price
        self.notify_observers(self.price)

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

    def update(self, price):
        print(f"{self.name} notified of new stock price: {price}")

# Usage
market = StockMarket()

investor1 = StockObserver("Investor 1")
investor2 = StockObserver("Investor 2")

market.subscribe(investor1)
market.subscribe(investor2)

market.update_price(100)  # Notify all observers
market.unsubscribe(investor1)
market.update_price(120)  # Notify remaining observers

Output:

Investor 1 notified of new stock price: 100
Investor 2 notified of new stock price: 100
Investor 2 notified of new stock price: 120

Here, in React, state changes trigger updates in all components relying on that state. This behavior closely mirrors the observer pattern, where components are notified when the state changes.

3. Mediator Method

The mediator pattern centralizes communication between objects, reducing the dependencies between them. Instead of objects interacting directly, they communicate through a Mediator that manages their interactions. 

This is especially valuable in systems with multiple interconnected components, where direct communication can become chaotic.

Analogy: Think of an air traffic control tower. Planes don’t communicate directly with each other; instead, they interact with the tower (the Mediator) to coordinate takeoffs and landings.

Example: Component Communication in React

# Mediator Pattern Example: Chat Room

class Mediator:
    def notify(self, sender, event):
        pass

class ChatRoom(Mediator):
    def __init__(self):
        self.users = []

    def register(self, user):
        self.users.append(user)
        user.mediator = self

    def notify(self, sender, message):
        for user in self.users:
            if user != sender:
                user.receive(message)

class User:
    def __init__(self, name):
        self.name = name
        self.mediator = None

    def send(self, message):
        print(f"{self.name} sends: {message}")
        self.mediator.notify(self, message)

    def receive(self, message):
        print(f"{self.name} receives: {message}")

# Usage
chat_room = ChatRoom()

alice = User("Alice")
bob = User("Bob")
charlie = User("Charlie")

chat_room.register(alice)
chat_room.register(bob)
chat_room.register(charlie)

alice.send("Hello, everyone!")  # All users except Alice receive the message
bob.send("Hi Alice!")  # All users except Bob receive the message

Output:

Alice sends: Hello, everyone!
Bob receives: Hello, everyone!
Charlie receives: Hello, everyone!
Bob sends: Hi Alice!
Alice receives: Hi Alice!
Charlie receives: Hi Alice

In large React applications, context APIs and Redux act as mediators, managing communication between components. 

As in the example, rather than components directly passing props up and down, the mediator facilitates centralized state management.

4. Memento Method

The memento pattern captures an object’s state at a specific time, allowing you to restore it later without exposing the implementation details. 

It’s a powerful tool for creating undo/redo functionality, managing checkpoints, or implementing rollback mechanisms in systems like text editors, games, or databases.

Analogy: Think of a video game with a save/load feature. When you save your game, the system captures the exact state (your progress, resources, etc.), allowing you to return to that state even after making irreversible actions.

Code Example with Database and Checkpoints:

# Memento Pattern Example: Database Transaction Checkpoints

class Memento:
    def __init__(self, state):
        self.state = state  # Captures the state of the database or object.

class Database:
    def __init__(self):
        self.state = {}  # Simulates the database as a dictionary.

    def set(self, key, value):
        self.state[key] = value

    def get(self, key):
        return self.state.get(key, None)

    def save_checkpoint(self):
        return Memento(self.state.copy())  # Save a copy of the current state.

    def rollback(self, memento):
        self.state = memento.state  # Restore to a saved state.

# Usage
db = Database()

# Initial state
db.set("user", "Alice")
db.set("age", 30)
checkpoint = db.save_checkpoint()  # Save the state

# Update state
db.set("user", "Bob")
db.set("age", 25)
print("Updated State:", db.state)  # Output: {'user': 'Bob', 'age': 25}

# Rollback to checkpoint
db.rollback(checkpoint)
print("Rolled Back State:", db.state)  # Output: {'user': 'Alice', 'age': 30}

Output:

Updated State: {'user': 'Bob', 'age': 25}
Rolled Back State: {'user': 'Alice', 'age': 30}

Code Explanation:

• Memento: Stores the state of the object or system. In the example, it holds a snapshot of the database state.
• Database: Simulates a database and provides methods to save and restore states. It uses the Memento object to manage checkpoints.
• Usage:
  • A checkpoint is saved before risky operations, capturing the current state.
  • If a rollback is needed, the system restores the previous state using the Memento, ensuring consistency.

5. Strategy Method

The strategy pattern defines a family of algorithms and allows them to be swapped dynamically without altering the client's use of them. This makes it easy to change the behavior of a class without modifying its structure.

Analogy: Think of a travel app. Depending on the situation, you can choose a travel strategy — flight, train, or car. The app allows you to switch between strategies without changing the core functionality.

Code Example:

# Strategy Pattern Example: Payment Methods

class PaymentStrategy:
    def pay(self, amount):
        pass

class CreditCardPayment(PaymentStrategy):
    def pay(self, amount):
        return f"Paid {amount} using Credit Card."

class PayPalPayment(PaymentStrategy):
    def pay(self, amount):
        return f"Paid {amount} using PayPal."

class ShoppingCart:
    def __init__(self, strategy):
        self.strategy = strategy

    def set_payment_strategy(self, strategy):
        self.strategy = strategy

    def checkout(self, amount):
        return self.strategy.pay(amount)

# Usage
cart = ShoppingCart(CreditCardPayment())
print(cart.checkout(100))

cart.set_payment_strategy(PayPalPayment())
print(cart.checkout(200))

Output:

Paid 100 using Credit Card.
Paid 200 using PayPal.

Code Explanation:

1. PaymentStrategy (Abstract Strategy): Defines the interface for all payment methods.
2. CreditCardPayment and PayPalPayment (Concrete Strategies): Implement the pay method with specific logic.
3. ShoppingCart: Dynamically switches between strategies during runtime.

6. Template Method

The Template method pattern defines the skeleton of an algorithm in a base class and allows subclasses to redefine specific steps without changing its structure.

Analogy: Think of making tea or coffee. The steps are similar (boil water, brew, pour into a cup, and add condiments), but the brewing process differs for tea and coffee.

Code Example:

# Template Pattern Example: Data Analysis

class DataProcessor:
    def process(self):
        self.load_data()
        self.analyze_data()
        self.report_data()

    def load_data(self):
        pass

    def analyze_data(self):
        pass

    def report_data(self):
        return "Report generated."

class CSVProcessor(DataProcessor):
    def load_data(self):
        return "CSV data loaded."

    def analyze_data(self):
        return "CSV data analyzed."

class JSONProcessor(DataProcessor):
    def load_data(self):
        return "JSON data loaded."

    def analyze_data(self):
        return "JSON data analyzed."

# Usage
csv_processor = CSVProcessor()
print(csv_processor.process())  # Steps followed and report generated.

json_processor = JSONProcessor()
print(json_processor.process())  # Steps followed and report generated.

Output:

Report generated.
Report generated.

Code Explanation:

• DataProcessor (Template): Provides the overall process, delegating specific steps to subclasses.
• CSVProcessor and JSONProcessor: Implement data-specific loading and analysis.
• Usage: Ensures consistency in the process while allowing flexibility in details.

7. Visitor Method

The visitor pattern separates algorithms from the objects on which they operate, making it easy to add new operations without modifying existing structures.

Analogy: Think of tax auditors visiting businesses. Each business type (restaurant, factory) implements a way to interact with auditors, but the auditors bring their own methods to evaluate taxes.

Code Example:

# Visitor Pattern Example: Shopping Cart Discounts

class Item:
    def accept(self, visitor):
        pass

class Book(Item):
    def accept(self, visitor):
        return visitor.visit_book(self)

class Electronics(Item):
    def accept(self, visitor):
        return visitor.visit_electronics(self)

class DiscountVisitor:
    def visit_book(self, book):
        return "Book: 10% discount applied."

    def visit_electronics(self, electronics):
        return "Electronics: 15% discount applied."

# Usage
book = Book()
electronics = Electronics()

visitor = DiscountVisitor()
print(book.accept(visitor))
print(electronics.accept(visitor))

Output:

Book: 10% discount applied.
Electronics: 15% discount applied.

Code Explanation:

• Item (Element): Base class for all items in the cart.
• Book and Electronics (Concrete Elements): Implement the accept method for visitor interaction.
• DiscountVisitor: Defines operations specific to each type of item.

8. State Method

The state pattern allows an object to alter its behavior when its internal state changes, giving the illusion of changing its class.

Analogy: Consider a phone. Depending on its state (locked, unlocked, in-call), its behavior changes when you press buttons.

Code Example:

# State Pattern Example: Phone States

class State:
    def press_button(self):
        pass

class LockedState(State):
    def press_button(self):
        return "Phone is locked. Unlock first."

class UnlockedState(State):
    def press_button(self):
        return "Phone is ready for use."

class Phone:
    def __init__(self):
        self.state = LockedState()

    def change_state(self, state):
        self.state = state

    def press_button(self):
        return self.state.press_button()

# Usage
phone = Phone()
print(phone.press_button())

phone.change_state(UnlockedState())
print(phone.press_button())

Output:

Phone is locked. Unlock first.
Phone is ready for use.

Code Explanation:

• State (Interface): Defines the behavior of each state.
• LockedState and UnlockedState (Concrete States): Implement specific behaviors for their respective states.
• Phone: Transitions between states dynamically during runtime.

9. Iterator Method

The iterator design pattern provides a way to access the elements of a collection sequentially without exposing its underlying representation.

Analogy: Think of a playlist. The Iterator lets you move through songs one at a time without needing to know how the playlist is stored internally.

Code Example:

# Iterator Pattern Example: Book Collection

class BookCollection:
    def __init__(self):
        self.books = []

    def add_book(self, book):
        self.books.append(book)

    def __iter__(self):
        return iter(self.books)

# Usage
collection = BookCollection()
collection.add_book("Book 1")
collection.add_book("Book 2")

for book in collection:
    print(book)

Output:

Book 1
Book 2

Code Explanation:

• BookCollection: Implements an iterator to traverse its books list.
• Usage: Simplifies iteration without exposing how the collection is managed.

10. Chain of Responsibility Method

The chain of responsibility method passes a request along a chain of handlers until one of them handles it. It decouples the sender and receiver of requests.

Analogy: Imagine a customer service call being routed through levels (agent, supervisor, manager) until it is resolved.

Code Example:

# Chain of Responsibility Pattern Example: Technical Support

class SupportHandler:
    def __init__(self, next_handler=None):
        self.next_handler = next_handler

    def handle_request(self, issue):
        if self.next_handler:
            return self.next_handler.handle_request(issue)
        return "Issue not resolved."

class Agent(SupportHandler):
    def handle_request(self, issue):
        if issue == "simple":
            return "Agent resolved the issue."
        return super().handle_request(issue)

class Supervisor(SupportHandler):
    def handle_request(self, issue):
        if issue == "complex":
            return "Supervisor resolved the issue."
        return super().handle_request(issue)

# Usage
chain = Agent(Supervisor())
print(chain.handle_request("simple"))  # Output: Agent resolved the issue.
print(chain.handle_request("complex"))  # Output: Supervisor resolved the issue.

Output:

Agent resolved the issue.
Supervisor resolved the issue.

Code Explanation:

• SupportHandler: Base handler that delegates unhandled requests to the next handler.
• Agent and Supervisor: Handle specific types of issues.
• Usage: Dynamically adds handlers and processes requests efficiently.

There you go! These classifications will help you not only write efficient and maintainable code but also adopt a mindset of solving problems systematically. 

Curious to see how far Python can take you? Explore upGrad’s Python programming course and learn how to apply these patterns to real-world challenges and beyond!

Also Read: Top 10 Reasons Why Python is So Popular With Developers in 2025

Next, let’s explore the advantages of bringing these patterns into use!

Benefits of Using Design Patterns in Python for Better Coding

Programming is as much an art as it is a science. While creativity is essential, efficiency, clarity, and consistency are equally important in writing code that stands the test of time.

Using design patterns in Python isn't just about solving problems; it's about solving them in the most effective, scalable, and maintainable way possible. Let’s explore the key benefits and their practical use cases:

Language Neutral

One of the most potent aspects of design patterns is their language neutrality. These patterns are not tied to any specific programming language, which makes them a universal toolkit for developers. 

Once you understand a pattern, you can apply it across various languages like Python, Java, C++, or even JavaScript.

Use Cases and Benefits:

1. Cross-Team Collaboration:
Imagine working in a multi-language environment where different teams use different programming languages. Knowing that design patterns behave similarly across languages simplifies communication and fosters better collaboration.

For example, a web developer working in JavaScript can communicate seamlessly with a backend developer using Python by referencing the observer pattern in their design discussions.

2. Easier Learning Curve Across Languages:
Once you master design patterns in Python, transitioning to other languages becomes smoother. The patterns remain the same, and only the syntax changes.

For instance, a Python developer moving to Java doesn’t need to relearn patterns like decorator or adapter — the concept remains unchanged.

3. Scalability Across Projects:
Design patterns allow you to write code that aligns with global best practices, ensuring your solutions are adaptable across diverse platforms and languages.

Tried and Tested Solution

Design patterns in Python are tried and tested solutions to common problems, refined over decades by seasoned developers. They save you from the guesswork of implementation.

Use Cases and Benefits:

1. Increased Reliability:
Types such as proxy or factory design pattern in Python have been used in countless real-world applications, proving their reliability.

Example: The singleton pattern is frequently used to manage database connections in large-scale applications. Its proven implementation ensures resource control and thread safety.

2. Faster Problem Solving:
With design patterns, you don’t need to brainstorm solutions from scratch. They provide a blueprint that can be adapted to your specific use case.

Need to implement a notification system? You can use the observer pattern to notify multiple components when a state changes.

3. Code Consistency:
Following established patterns ensures your code is consistent, making it easier for teams to review, debug, and extend.

Teams adhering to patterns like composite for hierarchical structures (e.g., file systems) create predictable and manageable codebases.

Decrease Technical Risk

Software projects are inherently risky — tight deadlines, evolving requirements, and unforeseen challenges can derail development. Design patterns mitigate technical risks by promoting robust, predictable, and scalable solutions.

Use Cases and Benefits:

1. Reduced Debugging and Testing Effort:
Design patterns are battle-tested, meaning they minimize the likelihood of introducing bugs. Their predefined structures also make testing straightforward.

2. Future-Proof Code:
Patterns like decorator and bridge allow you to add features or modify existing ones without rewriting the entire system. This adaptability reduces risks when requirements change.

3. Scalability Without Rework:
Patterns like flyweight reduce resource consumption in large-scale applications, preventing performance bottlenecks.

Easy to Use

Despite their sophistication, design patterns are surprisingly easy to use, thanks to their simplicity and extensive library support. Patterns provide a clear structure, making them accessible even to developers learning them for the first time.

Use Cases and Benefits:

1. Clear Documentation and Community Support:
Python's extensive community provides tutorials, libraries, and Python frameworks incorporating design patterns, reducing the learning curve.

Frameworks like Django internally use template methods and factory method pattern in Python, which developers can leverage without extensive manual coding.

Also Read: Python Frameworks: Top 7 Python Frameworks To Learn

2. Accelerated Learning:
Beginners can quickly grasp and implement patterns through Python’s intuitive syntax and clear examples.

3. Reduced Complexity:
Patterns abstract complex logic, allowing developers to focus on functionality rather than implementation details.

Also Read: Top 30 Python Pattern Programs You Must Know About

Why do you think learning design patterns in Python is crucial? Understand this in the section ahead!

Why Learning Design Patterns in Python is Essential for Developers?

In the fast-paced world of software development, writing functional code isn’t enough — you need to write smart code. Learning design patterns in Python equip you with proven solutions to common problems, making your code scalable, maintainable, and efficient. 

Here’s why you should learn design patterns in Python:

• Sharper Problem-Solving Skills: Design patterns help you identify and apply structured solutions to complex problems, saving time and effort.
• Boost Career Opportunities: Employers value developers who understand design patterns. It demonstrates strong software design and architecture skills.
• Team Collaboration Made Easy: Design patterns act as a shared language, simplifying communication and ensuring team alignment on solutions.
• Future-Proof Your Code: Patterns create flexible, scalable solutions that adapt easily to evolving project requirements.

• Stand Out in Interviews: Knowing patterns shows you think beyond code and focus on design, making you a strong candidate.

Mastering design patterns is not just a technical skill but a career-defining advantage. They are practical tools designed to make your development process smarter.

Want to elevate your problem-solving game? Enroll in upGrad’s complete guide to problem-solving and unlock techniques to tackle challenges like a pro!

Also Read: Career Opportunities in Python: Everything You Need To Know [2025]

But when should you use these design patterns in Python? Let’s see ahead!

When to Use Design Patterns in Python for Optimal Solutions?

Design patterns in Python are tools you deploy strategically when specific needs arise. Their true value lies in enhancing the efficiency, scalability, and clarity of your code, but the timing and context of their application are critical. 

So, when exactly should you reach for a design pattern? Let’s explore unique scenarios where it becomes indispensable.

1. When Code Complexity Begins to Escalate

As projects grow, maintaining and extending code becomes more challenging. Without structured solutions, it’s easy for codebases to turn into unmanageable tangles.

When to Use:

• You find yourself rewriting similar logic in different places.
• Your classes have too many responsibilities or dependencies.

Example:
In a media application, the facade pattern can create a unified interface to manage video, audio, and subtitles, reducing complexity for users and developers.

2. When Scaling Becomes a Priority

When your application’s architecture needs to accommodate growing user demands or evolving features, a poorly planned design can limit scalability.

When to Use:

• Your system must support adding features without breaking existing functionality.
• You need to manage resources efficiently in high-load environments.

Example:
A chess game using the flyweight pattern shares intrinsic properties of pieces like color or size, drastically reducing memory usage when scaling to thousands of moves.

3. When Decoupling Is Essential

Tightly coupled code is challenging to modify, test, or extend. Patterns help you decouple components while maintaining robust interactions.

When to Use:

• You need components to interact without direct dependencies.
• Changes in one module should not affect others.

Example:
In a messaging app, the mediator pattern enables chat participants to communicate via a central hub, decoupling individual users and streamlining logic.

Also Read: Loose Coupling vs Tight Coupling in Java: Difference Between Loose Coupling & Tight Coupling

4. When Reusability Becomes a Concern

Reusable components reduce development time and effort across projects. Patterns make it easier to design and share modular solutions.

When to Use:

• You need components that can be used in multiple contexts or applications.
• Avoiding code duplication is a priority.

Example:
An analytics dashboard using the strategy pattern can effortlessly switch between different data visualization algorithms (bar chart, pie chart, heatmap).

5. When Team Collaboration Needs Simplification

In collaborative environments, aligning on design and ensuring smooth handoffs between developers is critical. Patterns standardize the approach.

When to Use:

• Teams need a shared vocabulary for solutions.
• Consistent, standardized designs are required for clarity.

Example:
A large team developing a shopping app can use the builder pattern to standardize product creation logic, ensuring consistency across features like carts, wishlists, and search results.

Also Read: Workplace Communication: Key Types, Methods, and Its Importance

Knowing when to use design patterns in Python is as important as understanding how to use them. But knowing when not to use them is just as crucial. 

Let’s explore more of it ahead!

When to Avoid Design Patterns in Python: Key Considerations

Design patterns are powerful but not always the right solution. Like any tool, they must be applied thoughtfully, considering the problem at hand. Sometimes, simplicity is the ultimate sophistication. 

Overusing or misapplying them can lead to complexity, wasted time, and confusion rather than clarity and efficiency. 

Here’s when to avoid design patterns in Python, explained with insights.

1. Over-Engineering

Over-engineering occurs when you try to solve a problem unnecessarily complexly. Adding layers of abstraction or introducing design patterns to problems that don’t need them can complicate your codebase.

Pro Tip: Use patterns to simplify problems, not complicate them. Always ask yourself: “Is this pattern adding real value here?”

2. Premature Optimization

Premature optimization is when you try to make your code as efficient as possible before you fully understand the problem or requirements. Applying design patterns prematurely often wastes time and effort.

Pro Tip: First, focus on getting a functional solution. Optimize when you have evidence that performance is an issue.

3. Unfamiliarity

If you’re unfamiliar with a design pattern or its appropriate use cases, implementing it can backfire. Misusing patterns can lead to inefficient, buggy, or overly complex code.

Pro Tip: Learn the theory and practice of a pattern thoroughly before applying it. When in doubt, choose simpler, more direct approaches.

4. Project Constraints

Every project operates under constraints like deadlines, budget, or team expertise. Introducing design patterns can sometimes consume resources that would be better spent delivering a functional solution.

Pro Tip: Understand the constraints of your project and prioritize delivering value over architectural perfection.

5. Changing Requirements

In projects with frequently changing requirements, introducing rigid design patterns can make your code inflexible and harder to adapt.

Pro Tip: In volatile projects, favor simplicity and adaptability. Design patterns are most effective in stable or well-defined requirements.

Recognizing when not to use design patterns is a hallmark of a skilled Python developer. Always approach them with a critical eye, ensuring they enhance your code rather than hinder it!

Also Read: Python Developer Salary in India in 2025 [For Freshers & Experienced]

Modern Design Practices in Python

Today, software design must keep pace with demands for scalability, modularity, and intelligence. Traditional design patterns have laid a strong foundation, but modern practices push these patterns to adapt and evolve. 

They are no longer just about solving object-oriented challenges; they’re now key to crafting distributed systems and handling complex decision-making processes.

Let’s explore how design patterns in Python align with these advanced practices.

Microservices Design Patterns

Microservices architecture, characterized by breaking down applications into small, independently deployable services, relies heavily on design patterns to manage communication, consistency, and scalability. 

Python’s simplicity and robust ecosystem make it a natural fit for implementing microservices patterns.

• API Gateway Pattern
  An API Gateway is a single entry point for clients in a microservices environment. It uses principles akin to the facade pattern to handle request routing, composition, and policy enforcement.

Example: Instead of exposing dozens of service endpoints to clients, an API Gateway consolidates them into a unified, manageable interface.

• Event Sourcing and CQRS
  Combined with CQRS (Command Query Responsibility Segregation), observer patterns enable systems to handle high-scale read and write operations without bottlenecks.

Example: An e-commerce platform processes order updates across inventory, payments, and notifications in near real-time through event-driven patterns.

• Service Discovery
  Patterns like Proxy and Adapter help implement service discovery mechanisms that dynamically locate and communicate with services, which is critical for microservices running in large-scale, distributed environments.

AI-Influenced Design Patterns

AI and machine learning systems bring unique challenges that traditional patterns are evolving to address, such as managing data pipelines, shared resources, and inference workflows.

• Pipeline Patterns for Machine Learning
  AI workflows involve data preprocessing, model training, and evaluation. Using a combination of builder and strategy patterns, developers can create modular pipelines where each component is reusable and interchangeable.

Example: A data pipeline built using the Builder pattern might allow for flexible swapping between logistic regression and deep learning models depending on the dataset.

• Singleton for Model Management
  In production AI systems, models are resource-intensive, and maintaining multiple instances can be costly. The singleton pattern ensures that a single shared instance of a model is loaded into memory. 

Example: A recommendation system shares a single pre-trained model instance across multiple user sessions, avoiding redundant computations.

• Observer for Dynamic Updates
  In AI-driven systems like real-time fraud detection, observer patterns enable the model to adapt dynamically as new data arrives, ensuring decisions remain accurate.

Also Read: Fraud Detection in Machine Learning: What You Need To Know

By understanding these modern adaptations, you gain the ability to build solutions that are not only robust but also future-ready.

Master Design Patterns in Python with upGrad

Mastering design patterns in Python can seem frightening, but with the proper guidance, it becomes a transformative experience. This is where upGrad steps in, empowering you with industry-aligned programs, expert mentorship, and a structured learning path for your goals. 

Whether you’re a student taking your first steps into Python or a working professional looking to upskill, upGrad offers the resources you need to thrive. Some of the top relevant courses include: 

• Learn Basic Python Programming
• Learn Python Libraries: NumPy, Matplotlib & Pandas
• Case Study using Tableau, Python, and SQL
• Object Oriented Analysis and Design for Beginners
• Introduction to Design Thinking

The future belongs to developers who can think beyond code and design efficient, scalable systems. Don’t wait to take the next step.

Book a career counseling session with upGrad today and discover how their programs can transform your skills and career!

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