Intro

• platformdirs
• poethepoet - “Poe the Poet is a batteries included task runner that works well with poetry or with uv.”
• Python Pandas Ditches NumPy for Speedier PyArrow
• pointblank: Data validation made beautiful and powerful
• Extras
• Joke

About the show

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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 platformdirs
• At 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 Belderbos
• Tasks 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 rules
• Ruff 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 that
• I’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?