Fix Python Properties with Descriptors

• * The Python Language Summit 2025*
• Fixing Python Properties
• * complexipy*
• * juvio*
• Extras
• Joke

About the show

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Connect with the hosts

• Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky)
• Brian: @brianokken@fosstodon.org / @brianokken.bsky.social
• Show: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky)

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Michael #1: The Python Language Summit 2025

• Write up by Seth Michael Larson
• How can we make breaking changes less painful?: talk by Itamar Oren
• An Uncontentious Talk about Contention: talk by Mark Shannon
• State of Free-Threaded Python: talk by Matt Page
• Fearless Concurrency: talk by Matthew Parkinson, Tobias Wrigstad, and Fridtjof Stoldt
• Challenges of the Steering Council: talk by Eric Snow
• Updates from the Python Docs Editorial Board: talk by Mariatta
• PEP 772 - Packaging Governance Process: talk by Barry Warsaw and Pradyun Gedam
• Python on Mobile - Next Steps: talk by Russell Keith-Magee
• What do Python core developers want from Rust?: talk by David Hewitt
• Upstreaming the Pyodide JS FFI: talk by Hood Chatham
• Lightning Talks: talks by Martin DeMello, Mark Shannon, Noah Kim, Gregory Smith, Guido van Rossum, Pablo Galindo Salgado, and Lysandros Nikolaou

Brian #2: Fixing Python Properties

• Will McGugan
• “Python properties work well with type checkers such Mypy and friends. … The type of your property is taken from the getter only. Even if your setter accepts different types, the type checker will complain on assignment.”
• Will describes a way to get around this and make type checkers happy.
• He replaces @property with a descriptor. It’s a cool technique.
• I also like the way Will is allowing different ways to use a property such that it’s more convenient for the user. This is a cool deverloper usability trick.

Brian #3: complexipy

• Calculates the cognitive complexity of Python files, written in Rust.
• Based on the cognitive complexity measurement described in a white paper by Sonar
• Cognitive complexity builds on the idea of cyclomatic complexity.
• Cyclomatic complexity was intended to measure the “testability and maintainability” of the control flow of a module. Sonar argues that it’s fine for testability, but doesn’t do well with measuring the “maintainability” part. So they came up with a new measure.
• Cognitive complexity is intended to reflects the relative difficulty of understanding, and therefore of maintaining methods, classes, and applications.
• complexipy essentially does that, but also has a really nice color output.
• Note: at the very least, you should be using “cyclomatic complexity”
  • try with ruff check --select C901
• But also try complexipy.
• Great for understanding which functions might be ripe for refactoring, adding more documentation, surrounding with more tests, etc.

Michael #4: juvio

• uv kernel for Jupyter
• ⚙️ Automatic Environment Setup: When the notebook is opened, Juvio installs the dependencies automatically in an ephemeral virtual environment (using uv), ensuring that the notebook runs with the correct versions of the packages and Python
• 📁 Git-Friendly Format: Notebooks are converted on the fly to a script-style format using # %% markers, making diffs and version control painless
• Why Use Juvio?
  • No additional lock or requirements files are needed
  • Guaranteed reproducibility
  • Cleaner Git diffs
• Powered By
  • uv – ultra-fast Python package management
  • PEP 723 – Python inline dependency standards

Extras

Brian:

• Test & Code in slow mode currently. But will be back with some awesome interviews.

Joke: The 0.1x Engineer

• via Balázs
• Also
  • StormTrooper vlog
  • BIGFOOT VLOG - ATTACKED BY WENDIGO!