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#437 Python Language Summit 2025 Highlights

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Manage episode 490405982 series 1305988
Content provided by Michael Kennedy and Brian Okken. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Michael Kennedy and Brian Okken or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Topics covered in this episode:
Watch on YouTube
About the show

Sponsored by Posit: pythonbytes.fm/connect

Connect with the hosts

Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too.

Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it.

Michael #1: The Python Language Summit 2025

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

  continue reading

442 episodes

Artwork

#437 Python Language Summit 2025 Highlights

Python Bytes

1,841 subscribers

published

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Manage episode 490405982 series 1305988
Content provided by Michael Kennedy and Brian Okken. All podcast content including episodes, graphics, and podcast descriptions are uploaded and provided directly by Michael Kennedy and Brian Okken or their podcast platform partner. If you believe someone is using your copyrighted work without your permission, you can follow the process outlined here https://ppacc.player.fm/legal.
Topics covered in this episode:
Watch on YouTube
About the show

Sponsored by Posit: pythonbytes.fm/connect

Connect with the hosts

Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Monday at 10am PT. Older video versions available there too.

Finally, if you want an artisanal, hand-crafted digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it.

Michael #1: The Python Language Summit 2025

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

  continue reading

442 episodes

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