Pydantic Crash Course

Type-safe Python development.

What you'll learn

Learn Pydantic from scratch and start building type-safe Python applications. This crash course covers the essential 80/20 of Pydantic.

• Understand the problem - Why Python's dynamic typing breaks things
• Type hints - The foundation Pydantic builds on
• Data models - Define and validate your data structures
• Configuration - Type-safe settings from environment variables
• Structured output - Parse and validate data from external APIs

Course chapters

Chapter	Topic	Description
1	Introduction	The problem Pydantic solves
2	Type Hints	Python's type system basics
3	Your First Model	BaseModel fundamentals
4	Validation and Fields	Control what data is acceptable
5	Nested Models	Handle complex data structures
6	Pydantic Settings	Configuration management
7	Structured Output	Parse and validate external data
8	Summary	Key learnings and next steps

Prerequisites

This course assumes you know basic Python:

• Variables and data types
• Functions and classes
• Dictionaries and lists
• Working with APIs

If you need a refresher, check out the Python course.

Getting started

This project uses uv for fast Python package management.

1. Clone this repository

2. Install uv (if you haven't already):

# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

# Windows
powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

3. Create environment and install dependencies:

uv sync

4. Run Python files with uv:

uv run python main.py

5. Start with Chapter 1: Introduction

Resources

• Pydantic Documentation
• Pydantic Settings
• Python for AI Course