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https://github.com/openai/openai-cookbook/blob/main/examples...
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CodeRabbit
CodeRabbit: AI Code Reviews for Developers. Revolutionize your code reviews with AI. CodeRabbit offers PR summaries, code walkthroughs, 1-click suggestions, and AST-based analysis. Boost productivity and code quality across all major languages with each PR.
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I think I mention this all the time when this comes up, but I learned the most 'best practices' through using ruff.
https://docs.astral.sh/ruff/
I just installed and enabled all the rules by setting
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Everything @simonw has worked on, honestly: https://github.com/simonw
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Simon Willison's github would be a great place to get started imo -
https://github.com/simonw/datasette
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https://github.com/simonw/sqlite-utils
So, his code might not be a good place to find best patterns (for ex, I don't think they are fully typed), but his repos are very pragmatic, and his development process is super insightful (well documented PRs for personal repos!). Best part, he blogs about every non-trivial update, so you get all the context!
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elements-of-python-style
Goes beyond PEP8 to discuss what makes Python code feel great. A Strunk & White for Python.
I had 2 suggestions (plus a blog post) in my style guide here:
https://github.com/amontalenti/elements-of-python-style#some...
The style guide itself, published a few years back, also has some suggestions with small code snippets.
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If you're looking for some best practices related but limited to machine learning application code, you could have a look at [Beyond Jupyter](https://github.com/aai-institute/beyond-jupyter)
Here's an excerpt from the readme:
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
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Peter Norvig's work is great to learn from https://github.com/norvig/pytudes
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I'd suggest Flask or some of the smaller projects in the Pallets ecosystem:
https://github.com/pallets/flask
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I don't know if I can consider my code "Great" but I dedicated way too many months on a prometheus library where I focused on quality since I did it for me.
It's relatively small and I think the main take away would be the use of Protocols for the pluggable backend system. I hope you get something out of it :)
https://github.com/Llandy3d/pytheus
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I've recently looked at tasktiger https://github.com/closeio/tasktiger. It's a simple queue system that helped me understand how workers and schedulers work.
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Check out https://github.com/recursion-computing/starcel-panda3D for bleeding edge Python OS development