Learning Lab

A structured, practice-driven repository documenting my journey toward becoming a Python Backend Engineer.

---

Table of Contents

• About
• Repository Structure
• Learning Roadmap
• Recent updates
• Notes
• Exercises
• Tooling
• Quality Gates
• Development
• Learning Philosophy
• Current Focus
• Long-Term Goal
• License

---

About

This repository documents my journey toward becoming a Python Backend Engineer.

The objective is not only to learn Python syntax but also to build production-quality backend applications using modern engineering practices.

Every lesson includes:

• 📖 Notes
• 💻 Practical exercises
• 🧪 Pytest test cases
• 🔍 Code reviews
• 🛠️ Static type checking
• ✅ Best practices

---

Repository Structure

learning-lab/
│
├── .github/
│   └── workflows/
│
├── python_fundamentals/
│   ├── exercises/
│   ├── notes/
│   └── tests/
│
├── pyproject.toml
├── uv.lock
└── README.md

---

Learning Roadmap

Python Fundamentals

• Functions & Validation
• *args & **kwargs
• Decorators
• Context Managers
• contextlib
• Type Hints
• Dataclasses
• Inventory Mini Project
• Pytest Basics
• TypedDict
• OOP Fundamentals
• Pydantic Basics
• Advanced Pydantic – EmailStr & field_validator
• Advanced Pydantic – computed_field, model_validate, runtime parsing

---

Upcoming

• Nested Models (Pydantic)
• File Handling
• Logging
• SQLite
• SQL
• SQLAlchemy 2.0
• Alembic
• FastAPI
• Authentication
• Docker
• PostgreSQL
• GitHub Actions (workflows & CI hardening)
• Testing with Fixtures
• Dependency Injection
• Clean Architecture

---

Recent updates

• Completed focused work on Advanced Pydantic features: computed_field, model_validate and improving examples to be type-checker friendly.
• Fixed CI type-check failures (mypy/pyright) across example exercises by:
  • Using model_validate({...}) when intentionally providing string inputs or extra fields in examples.
  • Removing unused # type: ignore comments.
  • Calling computed_field descriptors (e.g. product.subtotal()) so static analyzers infer correct return types.
• Merged fixes in PR #29: #29

---

Notes

Day	Topic	Status
01	Functions & Validation	✅
02	*args & **kwargs	✅
03	Decorators	✅
04	Context Managers	✅
05	contextlib	✅
06	Type Hints	✅
07	Dataclasses	✅
08	Inventory Manager	✅
09	Pytest Basics	✅
10	TypedDict	✅
11	OOP Fundamentals	✅
12	Pydantic Basics	✅
13	Advanced Pydantic – EmailStr & field_validator	✅
24	Advanced Pydantic – computed_field & Decimal	✅
25	Date & DateTime (Pydantic parsing)	✅
26	model_dump() & Serialization	✅

---

Exercises

Current exercises include:

• Pricing Calculator
• Inventory Manager
• Context Managers
• Decorators
• Product Manager (OOP)
• Pydantic Basics
• Email Validation
• Custom Field Validators
• User Validation

---

Tooling

Tool	Purpose
Python 3.12+	Programming Language
uv	Package & Environment Management
Ruff	Linter & Formatter
MyPy	Static Type Checking
Pytest	Testing Framework
Pydantic	Runtime Data Validation
pre-commit	Git Hooks

---

Quality Gates

Every change must pass the following quality checks before being committed.

uv run ruff check .
uv run ruff format --check .
uv run mypy .
uv run pytest -v
uv run pre-commit run --all-files

---

Development

Install dependencies

uv sync

Run all quality checks

uv run pre-commit run --all-files

Run tests

uv run pytest -v

Run MyPy

uv run mypy .

Run Ruff

uv run ruff check .

Check formatting

uv run ruff format --check .

Automatically fix lint issues

uv run ruff check . --fix

---

Learning Philosophy

Every lesson follows the same engineering workflow.

Learn
    ↓
Understand
    ↓
Practice
    ↓
Exercise
    ↓
Write Notes
    ↓
Code Review
    ↓
Ruff
    ↓
MyPy
    ↓
Pytest
    ↓
pre-commit
    ↓
Git Commit
    ↓
Push

---

Current Focus

Currently learning:

• Advanced Pydantic (computed_field, model_validate)
• Runtime Validation & parsing
• Static typing compatibility with Pydantic examples

Next milestone:

• Nested Models
• FastAPI Request Validation

---

Long-Term Goal

Build production-ready backend applications using:

• FastAPI
• SQLAlchemy 2.0
• PostgreSQL
• Docker
• GitHub Actions
• Clean Architecture
• Dependency Injection
• Automated Testing

---

License

This repository is maintained for educational purposes and continuous backend engineering practice.