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Python Bytes

Python Bytes

著者: Michael Kennedy and Brian Okken
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Python Bytes is a weekly podcast hosted by Michael Kennedy and Brian Okken. The show is a short discussion on the headlines and noteworthy news in the Python, developer, and data science space.Copyright 2016-2025 政治・政府
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  • #437 Python Language Summit 2025 Highlights
    2025/06/23
    Topics covered in this episode: * The Python Language Summit 2025*Fixing Python Properties* complexipy** juvio*ExtrasJokeWatch on YouTube About the show Sponsored by Posit: pythonbytes.fm/connect Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky)Brian: @brianokken@fosstodon.org / @brianokken.bsky.socialShow: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) 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 Write up by Seth Michael LarsonHow can we make breaking changes less painful?: talk by Itamar OrenAn Uncontentious Talk about Contention: talk by Mark ShannonState of Free-Threaded Python: talk by Matt PageFearless Concurrency: talk by Matthew Parkinson, Tobias Wrigstad, and Fridtjof StoldtChallenges of the Steering Council: talk by Eric SnowUpdates from the Python Docs Editorial Board: talk by MariattaPEP 772 - Packaging Governance Process: talk by Barry Warsaw and Pradyun GedamPython on Mobile - Next Steps: talk by Russell Keith-MageeWhat do Python core developers want from Rust?: talk by David HewittUpstreaming the Pyodide JS FFI: talk by Hood ChathamLightning 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 SonarCognitive 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 C901But 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 painlessWhy Use Juvio? No additional lock or requirements files are neededGuaranteed reproducibilityCleaner Git diffsPowered By uv – ultra-fast Python package managementPEP 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 BalázsAlso StormTrooper vlogBIGFOOT VLOG - ATTACKED BY WENDIGO!
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    34 分
  • #436 Slow tests go last
    2025/06/16
    Topics covered in this episode: * Free-threaded Python no longer “experimental” as of Python 3.14*typed-ffmpegpyleak* Optimizing Test Execution: Running live_server Tests Last with pytest*ExtrasJokeWatch on YouTube About the show Sponsored by PropelAuth: pythonbytes.fm/propelauth66 Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky)Brian: @brianokken@fosstodon.org / @brianokken.bsky.socialShow: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) 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. Brian #1: Free-threaded Python no longer “experimental” as of Python 3.14 “PEP 779 ("Criteria for supported status for free-threaded Python") has been accepted, which means free-threaded Python is now a supported build!” - Hugo van KemenadePEP 779 – Criteria for supported status for free-threaded PythonAs noted in the discussion of PEP 779, “The Steering Council (SC) approves PEP 779, with the effect of removing the “experimental” tag from the free-threaded build of Python 3.14.”We are in Phase II then.“We are confident that the project is on the right path, and we appreciate the continued dedication from everyone working to make free-threading ready for broader adoption across the Python community.”“Keep in mind that any decision to transition to Phase III, with free-threading as the default or sole build of Python is still undecided, and dependent on many factors both within CPython itself and the community. We leave that decision for the future.”How long will all this take? According to Thomas Wouters, a few years, at least: “In other words: it'll be a few years at least. It can't happen before 3.16 (because we won't have Stable ABI support until 15) and may well take longer.” Michael #2: typed-ffmpeg typed-ffmpeg offers a modern, Pythonic interface to FFmpeg, providing extensive support for complex filters with detailed typing and documentation.Inspired by ffmpeg-python, this package enhances functionality by addressing common limitations, such as lack of IDE integration and comprehensive typing, while also introducing new features like JSON serialization of filter graphs and automatic FFmpeg validation.Features : Zero Dependencies: Built purely with the Python standard library, ensuring maximum compatibility and security.User-Friendly: Simplifies the construction of filter graphs with an intuitive Pythonic interface.Comprehensive FFmpeg Filter Support: Out-of-the-box support for most FFmpeg filters, with IDE auto-completion.Integrated Documentation: In-line docstrings provide immediate reference for filter usage, reducing the need to consult external documentation.Robust Typing: Offers static and dynamic type checking, enhancing code reliability and development experience.Filter Graph Serialization: Enables saving and reloading of filter graphs in JSON format for ease of use and repeatability.Graph Visualization: Leverages graphviz for visual representation, aiding in understanding and debugging.Validation and Auto-correction: Assists in identifying and fixing errors within filter graphs.Input and Output Options Support: Provide a more comprehensive interface for input and output options, including support for additional codecs and formats.Partial Evaluation: Enhance the flexibility of filter graphs by enabling partial evaluation, allowing for modular construction and reuse.Media File Analysis: Built-in support for analyzing media files using FFmpeg's ffprobe utility, providing detailed metadata extraction with both dictionary and dataclass interfaces. Michael #3: pyleak Detect leaked asyncio tasks, threads, and event loop blocking with stack trace in Python. Inspired by goleak.Use as context managers or function dectoratorsWhen using no_task_leaks, you get detailed stack trace information showing exactly where leaked tasks are executing and where they were created.Even has great examples and a pytest plugin. Brian #4: Optimizing Test Execution: Running live_server Tests Last with pytest Tim Kamanin“When working with Django applications, it's common to have a mix of fast unit tests and slower end-to-end (E2E) tests that use pytest's live_server fixture and browser automation tools like Playwright or Selenium. ”Tim is running E2E tests last for Faster feedback from quick testsTo not tie up resources early in the test suite.He did this with custom “e2e” markerImplementing a pytest_collection_modifyitems hook function to look for tests using the live_server fixture, and for them automatically add the e2e marker to those testsmove those tests to the endThe reason for the marker is to be able to Just run e2e tests with -m e2eAvoid running them sometimes with -m "not ...
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    37 分
  • #435 Stop with .folders in my ~/
    2025/06/09
    Topics covered in this episode: platformdirspoethepoet - “Poe the Poet is a batteries included task runner that works well with poetry or with uv.”Python Pandas Ditches NumPy for Speedier PyArrowpointblank: Data validation made beautiful and powerfulExtrasJokeWatch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python TrainingThe Complete pytest CoursePatreon Supporters Connect with the hosts Michael: @mkennedy@fosstodon.org / @mkennedy.codes (bsky)Brian: @brianokken@fosstodon.org / @brianokken.bsky.socialShow: @pythonbytes@fosstodon.org / @pythonbytes.fm (bsky) 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: platformdirs A small Python module for determining appropriate platform-specific dirs, e.g. a "user data dir".Why the community moved on from appdirs to platformdirsAt AppDirs: Note: This project has been officially deprecated. You may want to check out pypi.org/project/platformdirs/ which is a more active fork of appdirs. Thanks to everyone who has used appdirs. Shout out to ActiveState for the time they gave their employees to work on this over the years.Better than AppDirs: Works today, works tomorrow – new Python releases sometimes change low-level APIs (win32com, pathlib, Apple sandbox rules). platformdirs tracks those changes so your code keeps running.First-class typing – no more types-appdirs stubs; editors autocomplete paths as Path objects.Richer directory set – if you need a user’s Downloads folder or a per-session runtime dir, there’s a helper for it.Cleaner internals – rewritten to use pathlib, caching, and extensive test coverage; all platforms are exercised in CI.Community stewardship – the project lives in the PyPA orbit and gets security/compatibility patches quickly. Brian #2: poethepoet - “Poe the Poet is a batteries included task runner that works well with poetry or with uv.” from Bob BelderbosTasks are easy to define and are defined in pyproject.toml Michael #3: Python Pandas Ditches NumPy for Speedier PyArrow Pandas 3.0 will significantly boost performance by replacing NumPy with PyArrow as its default engine, enabling faster loading and reading of columnar data.Recently talked with Reuven Lerner about this on Talk Python too.In the next version, v3.0, PyArrow will be a required dependency, with pyarrow.string being the default type inferred for string data.PyArrow is 10 times faster.PyArrow offers columnar storage, which eliminates all that computational back and forth that comes with NumPy. PyArrow paves the way for running Pandas, by default, on Copy on Write mode, which improves memory and performance usage. Brian #4: pointblank: Data validation made beautiful and powerful “With its … chainable API, you can … validate your data against comprehensive quality checks …” Extras Brian: Ruff rulesRuff users, what rules are using and what are you ignoring?Python 3.14.0b2 - did we already cover this?Transferring your Mastodon account to another server, in case anyone was thinking about doing thatI’m trying out Fathom Analytics for privacy friendly analytics Michael: Polars for Power Users: Transform Your Data Analysis Game Course Joke: Does your dog bite?
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    26 分

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