Type-safe Python development.
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
| 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 |
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.
This project uses uv for fast Python package management.
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Clone this repository
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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"- Create environment and install dependencies:
uv sync- Run Python files with uv:
uv run python main.py- Start with Chapter 1: Introduction
