#437 Python Language Summit 2025 Highlights

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Jun 23, 2025

Dive into the highlights from the Python Language Summit 2025, where discussions ranged from managing breaking changes and improving concurrency to the exciting prospects of Python on mobile. Explore the challenges of Python's property systems and how different tools handle type checking. Unpack Complexipy, a innovative library for measuring cognitive complexity, and discover Juvio, which enhances command execution in Jupyter notebooks. The blend of technical insights and humor makes for an engaging exploration of Python's future.

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INSIGHT

Python Summit 2025 Highlights

The Python Language Summit 2025 focused heavily on concurrency, breaking changes, and packaging governance.
Free-threaded Python is now stable, and Python on Mobile reached tier three support, showing forward momentum in Python's ecosystem.

ADVICE

Fix Python Properties with Descriptors

Use descriptors instead of @property decorators to resolve type checker complaints with mismatched getter and setter types.
This approach keeps type checkers like MyPy happier without hurting the developer experience.

INSIGHT

Python Properties Are Cumbersome

Python properties are cumbersome and non-intuitive, especially due to separated getters and setters.
A more integrated property definition, like in C#, would improve readability and maintainability in Python.

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About the show

Sponsored by Posit: pythonbytes.fm/connect

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Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky)
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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
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