self-contained-runnable-python-package-template VS hatch

The approach I prefer is to not mess with setuptools etc at all in the first place, and simply make a nice executable package.

e.g. https://github.com/tpapastylianou/self-contained-runnable-py...

The title should be: How to create a "Python DISTRIBUTION package".

The term "python package" means something entirely different (or at the very least is ambiguous in a pypi/distribution context).

To add to the confusion, creating a totally normal, runnable python package in a manner that makes it completely self-contained such that it can be "distributed" in a standalone manner, while still being a totally normal boring python package, is also totally possible (if not preferred, in my view).

Shameless plug: https://github.com/tpapastylianou/self-contained-runnable-py...

Shameless plug: I use my own template, which organises things as runnable projects.

https://github.com/tpapastylianou/self-contained-runnable-py...

It serves my purposes very well (which is creating projects that represent standalone experiments).

Sharing in case someone else here finds it useful.

More recently I've modified this a bit to also generate nice html reports straight from the __main__.py file, independently of the underlying python code, and use this as lab books (where each lab book contains a single analysis and its report). I'll upload this template separately when I find the time.

Exciting stuff! I view Hatch [1] as becoming the Cargo for Python because it's already close and has an existing (and growing) user base but I can definitely see depending on this for resolution and potentially not even using pip after it becomes more stable.

[1]: https://hatch.pypa.io/latest/

I was inspired enough by the hatch sync idea that I created a PR to add that functionality to hatch: https://github.com/pypa/hatch/pull/1094

Another concept I learned was about build backends, an import step which is used to initialize and install any dependencies of the app you're packaging. Since the tutorial went with using Hatch that is also what I went with, though it didn't provide a lot of useful details especially because it didn't show how to add any dependencies, so I took a look at the docs which were very nice and simple to follow.

Try using hatch: https://hatch.pypa.io/latest/

I'm trying to learn Hatch, I currently use [Poetry](python-poetry.org/) to manage my dependencies, and while I'm overall happy with it, I really like the features I'm reading about with Hatch. I'm also working on learning CI pipelines & Dockerizing Python applications, and Hatch seems like a really useful tool to learn for this (and just as a general use tool).

Poetry 1.2 has been a pain. Which was the dev's fault though. Switching to something new while deprecating a related feature is just plain bad. I've been looking into modern alternatives like PDM and Hatch, but haven't used them (yet).

For example, when it comes to Python, one option is to use the same packaging system that a huge number of open-source libraries and tools are published with. You can use setuptools or Hatch to build a "packaged" version of your code, and publish it to either the public PyPi repository or an internal one that you set up. Then your users can use pip to install your package, automatically fetch its dependencies, and keep it up to date, just like any other Python module.

Don't forget the new PyPa tool on the block: Hatch.

See also: https://github.com/pypa/hatch