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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.

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

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)