LEARN COMPLETE PYTHON IN 24 HOURS
🟦 Advanced Python – Table of Contents
🔹 1. Python Intermediate Recap & Advanced Setup
1.1 Quick Review: Lists, Dicts, Functions, Modules
1.2 Virtual Environments & pip (venv, requirements.txt)
1.3 Code Formatting & Linting (Black, Flake8, isort)
1.4 Type Hints & Static Typing (typing module, mypy)
1.5 Debugging Techniques (pdb, logging, VS Code debugger)
🔹 2. Object-Oriented Programming (OOP) in Depth
2.1 Classes & Objects – Advanced Features
2.2 init, self, str, repr
2.3 Inheritance & super()
2.4 Method Overriding & Polymorphism
2.5 Encapsulation: Private & Protected Members
2.6 Properties (@property, @setter, @deleter)
2.7 Class Methods, Static Methods, @classmethod, @staticmethod
2.8 Multiple Inheritance & Method Resolution Order (MRO)
2.9 Abstract Base Classes (abc module)
2.10 Composition vs Inheritance
🔹 3. Advanced Data Structures & Collections
3.1 collections module: namedtuple, deque, Counter, defaultdict, OrderedDict
3.2 dataclasses (Python 3.7+)
3.3 Heapq – Priority Queues
3.4 Bisect – Binary Search & Insertion
🔹 4. Functional Programming Tools
4.1 Lambda Functions
4.2 map(), filter(), reduce()
4.3 List, Dict & Set Comprehensions
4.4 Generator Expressions
4.5 Generators & yield
4.6 Generator Functions
4.7 yield from
4.8 itertools module
🔹 5. Decorators & Higher-Order Functions
5.1 What are Decorators?
5.2 Writing Simple Decorators
5.3 Decorators with Arguments
5.4 @property, @classmethod, @staticmethod
5.5 @lru_cache (functools)
5.6 Chaining Decorators
5.7 Class Decorators
🔹 6. Context Managers & with Statement
6.1 Understanding Context Managers
6.2 Custom Context Managers (enter, exit)
6.3 @contextmanager
6.4 Common Use Cases
🔹 7. Exception Handling – Advanced
7.1 try-except-else-finally
7.2 Raising Custom Exceptions
7.3 Custom Exception Classes
7.4 Exception Chaining
7.5 Logging vs print()
🔹 8. File Handling & Data Formats
8.1 Reading/Writing Files
8.2 with Statement Best Practices
8.3 CSV – csv module
8.4 JSON – json module
8.5 Pickle
8.6 Large Files Handling
🔹 9. Concurrency & Parallelism
9.1 Threading vs Multiprocessing vs Asyncio
9.2 threading module
9.3 multiprocessing
9.4 asyncio – Async/Await
9.5 aiohttp
9.6 GIL & Use Cases
🔹 10. Mtaclasses & Advanced OOP
10.1 What are Metaclasses?
10.2 type() as Metaclass
10.3 Custom Metaclasses
10.4 new vs init
10.5 Use Cases
🔹 11. Design Patterns in Python
11.1 Singleton, Factory, Abstract Factory
11.2 Observer, Strategy, Decorator Pattern
11.3 Pythonic Alternatives
🔹 12. Performance Optimization
12.1 Time & Space Complexity
12.2 Profiling (cProfile, timeit)
12.3 Efficient Data Structures
12.4 Caching & Memoization
12.5 NumPy & Pandas
🔹 13. Testing in Python
13.1 unittest vs pytest
13.2 Unit Testing
13.3 Mocking
13.4 TDD Basics
🔹 14. Popular Libraries & Tools
14.1 requests
14.2 BeautifulSoup & Scrapy
14.3 pandas & NumPy
14.4 Flask / FastAPI
14.5 SQLAlchemy / Django ORM
🔹 15. Mini Advanced Projects & Best Practices
15.1 CLI Tool (argparse / click)
15.2 Async Web Scraper
15.3 Decorator-based Logger
15.4 Thread-Safe Counter
15.5 Data Pipeline
15.6 PEP 8, PEP 257, Git Workflow
11. Design Patterns in Python
11.1 Singleton, Factory, Abstract Factory
Singleton Pattern Ensures a class has only one instance and provides a global point of access.
Classic way (using metaclass)
Python
class SingletonMeta(type): instances = {} def call_(cls, args, kwargs): if cls not in cls._instances: instance = super().__call__(args, **kwargs) cls._instances[cls] = instance return cls._instances[cls] class DatabaseConnection(metaclass=SingletonMeta): def init(self): self.connected = False def connect(self): if not self.connected: print("Connecting to database...") self.connected = True db1 = DatabaseConnection() db2 = DatabaseConnection() print(db1 is db2) # True – same instance
Pythonic alternative (module-level singleton) Most common in Python — just use a module!
Python
# database.py connection = None def getconnection(): global connection if connection is None: print("Creating new connection...") connection = "DB Connection Object" return connection
Factory Pattern Creates objects without specifying the exact class.
Python
class Button: def render(self): pass class WindowsButton(Button): def render(self): return "Render Windows-style button" class MacButton(Button): def render(self): return "Render Mac-style button" class GUIFactory: def create_button(self): pass class WindowsFactory(GUIFactory): def create_button(self): return WindowsButton() class MacFactory(GUIFactory): def create_button(self): return MacButton() # Usage def create_ui(factory: GUIFactory): button = factory.create_button() print(button.render()) factory = WindowsFactory() # or MacFactory() create_ui(factory)
Abstract Factory Creates families of related objects.
Pythonic: Use factories returning multiple related objects (often dicts or tuples).
Python
def get_theme_factory(theme): if theme == "dark": return { "button": "DarkButton", "checkbox": "DarkCheckbox", "background": "#1e1e1e" } else: return { "button": "LightButton", "checkbox": "LightCheckbox", "background": "#ffffff" } theme = get_theme_factory("dark") print(theme["background"]) # #1e1e1e
11.2 Observer, Strategy, Decorator Pattern
Observer Pattern (Publish-Subscribe) One object notifies many dependents when its state changes.
Pythonic way: Use callbacks / event system
Python
class Subject: def init(self): self._observers = [] def attach(self, observer): self._observers.append(observer) def detach(self, observer): self._observers.remove(observer) def notify(self, message): for observer in self._observers: observer.update(message) class NewsPublisher(Subject): def publish(self, headline): print(f"New headline: {headline}") self.notify(headline) class Subscriber: def init(self, name): self.name = name def update(self, message): print(f"{self.name} received: {message}") pub = NewsPublisher() sub1 = Subscriber("Anshuman") sub2 = Subscriber("Rahul") pub.attach(sub1) pub.attach(sub2) pub.publish("Python 3.14 released!") # Output: # New headline: Python 3.14 released! # Anshuman received: Python 3.14 released! # Rahul received: Python 3.14 released!
Modern Pythonic alternatives:
Use blinker library (signals)
Use asyncio events / queues
Use property + callbacks
Strategy Pattern Define a family of algorithms, encapsulate each, make them interchangeable.
Pythonic way: Pass functions / lambdas
Python
def discount_strategy(order_total): return order_total * 0.9 # 10% off def premium_strategy(order_total): return order_total - 500 if order_total > 5000 else order_total def checkout(total, discount_func): final = discount_func(total) print(f"Original: ₹{total} → Final: ₹{final}") checkout(6000, discount_strategy) # Final: ₹5400.0 checkout(6000, premium_strategy) # Final: ₹5500
Decorator Pattern Dynamically add responsibilities to objects.
Python already has decorators — use @ syntax!
Python
def add_tax(func): def wrapper(price): return func(price) * 1.18 # 18% GST return wrapper @add_tax def get_book_price(): return 500 print(get_book_price()) # 590.0
11.3 Pythonic Alternatives to Classic Patterns
Python's dynamic features make many GoF patterns unnecessary or over-engineered.
Classic PatternPythonic AlternativeWhy better in Python?SingletonModule-level variable / functionModules are naturally singletonsFactory / Abstract FactorySimple functions returning objects / dicts of factoriesFirst-class functions, duck typingStrategyPass functions / lambdas / callables as argumentsFunctions are first-class citizensDecoratorBuilt-in @decorator syntaxClean, readable, standardObserverCallbacks, blinker, asyncio events, property settersSimpler than full subject-observer hierarchyVisitorfunctools.singledispatch or match-casePattern matching + multimethodsCommandCallable objects / lambdasNo need for heavy class hierarchyTemplate Methodabc abstract methods + inheritanceOr use composition + hooks
Example: Pythonic Command Pattern
Python
def save_file(): print("File saved") def send_email(): print("Email sent") commands = { "save": save_file, "email": send_email } def execute(command_name): cmd = commands.get(command_name) if cmd: cmd() else: print("Unknown command") execute("save") # File saved execute("email") # Email sent
Final advice (2026):
Prefer simple functions, decorators, composition, and duck typing over heavy class hierarchies.
Use design patterns as inspiration — not rigid rules.
When in doubt → ask: "Can I solve this with a function or decorator instead of a big class structure?"
This completes the full Design Patterns in Python section — now you know how to apply classic patterns in a clean, Pythonic way!!
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