#435 Stop with .folders in my ~/
Python Bytes
Optimizing File Management with Python Tools
This chapter explores the challenges of file organization across operating systems and introduces the Python module DIRS for better directory management. It also highlights the benefits of the new task runner 'Poe the Poet' in Python development, emphasizing its integration with 'pyproject.toml' for streamlined task definitions.
- 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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