Advanced Python: Metaprogramming

• Intended for advanced Python users who want to really dig into how Python works.
• Covers techniques used commonly in large, popular, mature libraries such as Django, Starlette, FastAPI, Pydantic, or Textual, as well as in the Python standard library in modules such as dataclasses, functools, collections, or enum.
• Discusses the risks and rewards of using these sophisticated techniques, in terms of readability, correctness, and maintainability.
• Deep dives into class construction, library design, and metaprogramming techniques including better library design and debugging.

To implement complex systems, frameworks, or libraries, or to debug tricky issues in said systems, you need a thorough understanding of how Python really works under the hood. This course tackles topics not typically covered in Python classes, such as class construction, what a metaclass is at its core, library design, metaprogramming and monkey-patching, and practical ways to tackle complex problems using these strategies.

Metaprogramming is code that writes code. In Python, this is accomplished through a variety of techniques, such as the use of metaclasses, class decorators, object model methods like init_subclass, and mechanisms such as eval and exec. These techniques are in heavy use in popular, large, mature libraries like Django or Starlette, FastAPI, Pydantic, or Textual. If you need to debug code that uses any of these frameworks, then you may need to understand the common metaprogramming techniques. Additionally, if you want to write large, complex libraries, metaprogramming approaches allow you to eliminate crude, error-prone manual techniques and provide opportunities for creating interfaces that users find easy and intuitive.