Python: Novice to Pro Guide

1. Novice Level (The Basics)

1.1 Introduction

• What is Python? High-level, interpreted language known for readability.
• Setup: Installing Python, VS Code, running scripts (python script.py).

1.2 Syntax & Variables

• Comments (#, ''')
• Variables (Dynamic typing: x = 10, name = "Mahesh")
• Data Types: int, float, str, bool
• Input/Output: print(), input()

1.3 Control Flow

• if, elif, else
• Loops: for (iterating over ranges/lists), while
• break, continue

1.4 Basic Data Structures

• Lists: [1, 2, 3], slicing, methods (append, pop)
• Tuples: (1, 2), immutable
• Dictionaries: {'key': 'value'}, accessing, iterating
• Sets: {1, 2}, unique items

2. Intermediate Level (Building Logic)

2.1 Functions

• Definition: def my_func(param):
• Arguments: Positional, Keyword, Default, *args, **kwargs
• Scope: Local vs Global
• Lambda Functions

2.2 File Handling

• Reading/Writing: open(), with open(...) as f: (context managers)
• Modes: r (read), w (write), a (append)

2.3 Error Handling

• try, except, else, finally
• Raising exceptions: raise ValueError("Error")

2.4 Modules & Packages

• Importing: import math, from os import path
• Creating custom modules
• pip and virtual environments (venv)

2.5 OOP Basics

• Classes & Objects
• __init__ constructor
• Methods (self)
• Inheritance & Polymorphism

3. Advanced Level (Professional Development)

3.1 Advanced OOP

• Encapsulation (private variables _, __)
• Class Methods (@classmethod) vs Static Methods (@staticmethod)
• Magic Methods (__str__, __repr__, __len__)
• Properties (@property)

3.2 Decorators & Generators

• Decorators: Modifying function behavior (@login_required)
• Generators: yield, memory efficiency for large datasets
• Iterators: __iter__, __next__

3.3 Concurrency

• Threading vs Multiprocessing
• asyncio for asynchronous programming (async, await)

3.4 Pythonic Code

• List Comprehensions: [x**2 for x in range(10)]
• Context Managers (__enter__, __exit__)
• Type Hinting (def func(a: int) -> str:)

4. Expert Level (System Design & Optimization)

4.1 Metaprogramming

• type() to create classes dynamically
• Metaclasses

4.2 Performance Optimization

• Profiling (cProfile)
• Time Complexity analysis
• Using libraries like NumPy/Pandas for C-level speed

4.3 Testing

• unittest, pytest
• TDD (Test Driven Development) concepts
• Mocking

4.4 Deployment & Packaging

• Creating setup.py
• Dockerizing Python apps
• CI/CD pipelines for Python projects