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

7. Exception Handling – Advanced

7.1 try-except-else-finally Deep Dive

The full structure of exception handling in Python:

Python

try: # Code that might raise an exception risky_operation() except ExceptionType1 as e1: # Handle specific exception type 1 print(f"Type1 error: {e1}") except (Type2, Type3) as e23: # Handle multiple types print(f"Type2/3 error: {e23}") except Exception as e: # Catch-all (broad, use carefully) print(f"Unexpected error: {e}") # Optionally re-raise: raise else: # Runs only if NO exception occurred print("Success! No exceptions raised.") finally: # Always runs (cleanup), even on return/break/raise print("Cleanup: closing files, connections, etc.")

Key points to remember:

  • else → only executes if no exception was raised in try (great for code that should run only on success)

  • finally → always executes (cleanup, close files/connections, release locks)

  • Order matters: except blocks are checked from top to bottom → specific → general

  • Avoid bare except: (catches everything, including KeyboardInterrupt, SystemExit)

Real example – File processing with proper cleanup

Python

def process_file(filename): try: f = open(filename, "r") data = f.read() number = int(data.strip()) # might raise ValueError except FileNotFoundError: print("File not found!") return None except ValueError as ve: print(f"Invalid number format: {ve}") return None else: print("File read successfully!") return number * 2 finally: if 'f' in locals(): f.close() print("File closed in finally.") print(process_file("numbers.txt"))

7.2 Raising Custom Exceptions

Use raise to signal errors explicitly.

Basic raise

Python

if age < 0: raise ValueError("Age cannot be negative!")

Raise with custom message

Python

def divide(a, b): if b == 0: raise ZeroDivisionError("Cannot divide by zero – check your input!") return a / b

Re-raise (preserve original traceback)

Python

try: risky_code() except Exception as e: print("Logging error...") raise # re-raises the original exception with full traceback

Chaining with raise ... from (see 7.4)

7.3 Creating Custom Exception Classes

Custom exceptions make error handling clearer and more semantic.

Basic custom exception

Python

class InvalidAgeError(ValueError): """Raised when age is invalid (negative or unrealistic).""" pass def set_age(age): if age < 0: raise InvalidAgeError("Age cannot be negative") if age > 150: raise InvalidAgeError("Age seems unrealistic") print(f"Age set to {age}")

Advanced custom exception with attributes

Python

class PaymentFailedError(Exception): def init(self, amount, reason, transaction_id=None): self.amount = amount self.reason = reason self.transaction_id = transaction_id super().__init__(f"Payment of ₹{amount} failed: {reason}") def str(self): msg = f"Payment failed: {self.reason} (₹{self.amount})" if self.transaction_id: msg += f" - Transaction ID: {self.transaction_id}" return msg try: # Simulate payment gateway failure raise PaymentFailedError(5000, "Card declined", "TXN987654") except PaymentFailedError as e: print(e) # Payment failed: Card declined (₹5000) - Transaction ID: TXN987654 print(f"Amount lost: ₹{e.amount}")

Best practice:

  • Inherit from built-in exceptions (ValueError, TypeError, RuntimeError, etc.)

  • Add custom attributes for debugging/info

  • Write docstrings for clarity

7.4 Exception Chaining (__cause__, context)

Python automatically tracks exception chains:

  • context → the exception that was being handled when this one was raised

  • cause → explicit cause (set with raise ... from)

Automatic chaining (implicit)

Python

try: 1 / 0 except ZeroDivisionError: raise ValueError("Division failed") # ValueError.__context__ = ZeroDivisionError

Explicit chaining with from

Python

def load_config(): try: with open("config.json") as f: return json.load(f) except FileNotFoundError as fnf: raise RuntimeError("Configuration loading failed") from fnf try: load_config() except RuntimeError as re: print(re) # Configuration loading failed print(re.__cause__) # shows FileNotFoundError

Use from None to suppress context

Python

raise ValueError("Bad value") from None # hides previous context

When to use:

  • from → when new exception is a direct consequence (cleaner traceback)

  • Automatic context → when handling one error leads to another

7.5 Logging vs print() for Production

print() → good for debugging, bad for production

logging → standard for real applications

Basic logging setup (recommended)

Python

import logging import sys # Configure once at app startup logging.basicConfig( level=logging.DEBUG, # DEBUG / INFO / WARNING / ERROR / CRITICAL format="%(asctime)s [%(levelname)s] %(name)s: %(message)s", datefmt="%Y-%m-%d %H:%M:%S", handlers=[ logging.FileHandler("app.log"), logging.StreamHandler(sys.stdout) ] ) logger = logging.getLogger(__name__) # name = current module name def process_payment(amount): try: if amount <= 0: raise ValueError("Amount must be positive") logger.info(f"Processing payment of ₹{amount}") # simulate success logger.debug("Payment gateway response: success") return True except ValueError as ve: logger.error("Payment validation failed", exc_info=True) # includes traceback return False except Exception as e: logger.critical("Unexpected error in payment", exc_info=True) raise

Levels quick reference:

  • debug → detailed info (disabled in production)

  • info → normal operation messages

  • warning → something unexpected but not fatal

  • error → handled error (operation failed)

  • critical → severe error (app may crash)

Advantages over print():

  • Levels (turn off debug in production)

  • File + console output

  • Timestamps, module names, tracebacks

  • Configurable via logging.config (dictConfig, fileConfig)

  • Thread-safe

Pro tip: Use logger.exception("Message") instead of logger.error(..., exc_info=True) — shorter and includes traceback automatically.

Mini Project – Robust File Processor with Logging & Custom Exception

Python

import logging from contextlib import contextmanager logging.basicConfig(level=logging.INFO, format="%(asctime)s [%(levelname)s] %(message)s") class FileProcessingError(Exception): pass @contextmanager def safe_file_read(filename): try: with open(filename, "r") as f: yield f.read() except FileNotFoundError: raise FileProcessingError(f"File {filename} not found") except PermissionError: raise FileProcessingError(f"No permission to read {filename}") except Exception as e: raise FileProcessingError(f"Unexpected error reading {filename}: {e}") try: with safe_file_read("config.txt") as content: logging.info("File read successfully") print(content) except FileProcessingError as fpe: logging.error(str(fpe))

This completes the full Exception Handling – Advanced section — now you can build robust, production-ready Python applications that handle errors gracefully!

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