Python Code Notes

A repository for Python code snippets, notes, and best practices. This collection covers essential Python concepts, syntax, and common libraries to help me for quick reference, learning, and keeping notes.

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Topics

1. Core Python Syntax

   • Variables, Data Types, and Type Conversion
   • Operators
   • Conditionals and Loops

2. Functions and Lambdas

   • Defining Functions
   • Lambda Expressions

3. Data Structures

   • Lists, Tuples, Sets, Dictionaries
   • List and Dictionary Comprehensions

4. Modules and Packages

   • Importing Modules
   • Custom Modules

5. Object-Oriented Programming (OOP)

   • Classes, Inheritance, Encapsulation, Polymorphism

6. File Handling

   • Reading, Writing, JSON and CSV

7. Error Handling

   • Exception Handling and Custom Exceptions

8. Advanced Python Concepts

   • Generators, Context Managers, Regular Expressions

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Setup Instructions

Clone the repository:

  git clone https://github.com/masumkhan081/python-code-notes.git

✅ Phase 1: Python Essentials (7–10 days) ⏱ Spend ~1 hour/day.

🎯 Must Learn: ✅ Data types: str, list, dict, tuple, set

✅ Control flow: if, for, while, comprehensions

✅ Functions: def, *args, **kwargs, lambdas

✅ Modules: import, from, math, random, os

✅ File I/O: open(), with, read/write basics

✅ Exception handling: try/except, finally

🛑 Skip: OOP internals, decorators/metaclasses unless used in Django/ML context.

✅ Phase 2: Python for Django (5–7 days) Goal: Understand the code you'll write daily in Django.

🎯 Must Learn: ✅ Classes & Objects (only what Django uses)

init, self, inheritance

Models and attributes

✅ Virtual Environments: venv, pip, requirements.txt

✅ Django Project Basics:

startproject, startapp, views, templates

URLs and routing

Django ORM: basic Model, QuerySet, filter(), get()

🛑 Skip: Advanced metaclasses, middleware, admin customization (for now)

✅ Phase 3: Python for Machine Learning (10–14 days, flexible) Don’t need to master Python, just enough to write ML code clearly.

🎯 Must Learn: ✅ Numpy & Pandas: array ops, indexing, filtering, aggregation

✅ Jupyter Notebooks: for experiment & quick dev

✅ Functions, loops, and list comprehensions for data prep

✅ Matplotlib / Seaborn (just enough for data viz)

🛑 Skip: Async programming, multithreading, Django templating (for now)