EuroSciPy 2024

Python offers decorator to implement re-usable code for cross-cutting task.
The support the separation of cross-cutting concerns such as logging, caching,
or checking of permissions.
This can improve code modularity and maintainability.

This tutorial is an in-depth introduction to decorators.
It covers the usage of decorators and how to implement simple and more advanced
decorators.
Use cases demonstrate how to work with decorators.
In addition to showing how functions can use closures to create decorators,
the tutorial introduces callable class instance as alternative.
Class decorators can solve problems that use be to be tasks for metaclasses.
The tutorial provides uses cases for class decorators.

While the focus is on best practices and practical applications, the tutorial
also provides deeper insight into how Python works behind the scene.
After the tutorial participants will feel comfortable with functions that take
functions and return new functions.

Parallel computing is essential for many performance-critical applications. Python provides many solutions for this problem. New versions of Python will support sub-interpreters and a, currently experimental, free-threading version without the Global Interpreter Lock (GIL).

This talk starts with a short overview over this topic, clarifying terms such parallel, concurrent, and distribute computing as well as CPU-bound, memory-bound, and IO-bound problems. The presentation explains how Python and its standard library support parallel programming tasks. In addition, many Python libraries provide very useful approaches and tools for parallel computing. An overview of important libraries provides guidance which library can be used for what type of parallel problem.

How do Python's new features such as sub-interpreters and free-threading without the Global Interpreter Lock (GIL) impact parallel Programming in Python? This talk address this question by providing examples where these features might help to make programs simpler and/or faster.