#435 Stop with .folders in my ~/
Python Bytes
The Emotional Toll of Unshipped Projects
This chapter explores the emotional impact of working on software projects that never get deployed, through a moving story from an experienced team member. It also contrasts the timelines of software and hardware development, shedding light on the ambitious yet often delayed nature of space exploration endeavors.
- 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:
Joke: Does your dog bite?
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