python-tabulate VS jupyter-book

pandas.DataFrame().to_latex() [1] and tabulate [2] support latex table output.

[1] https://pandas.pydata.org/docs/reference/api/pandas.DataFram...

[2] https://github.com/astanin/python-tabulate/blob/master/tabul...

pandas uses tabulate

As a general piece of advice, the tabulate package is useful for neatly formatting spreadsheet-style data, as is the pandas package, although tabulate is much simpler to use.

There are ways to do it manually with padding/alignment, but using the built-in csv module to read the file and tabulate to format it is probably the easiest way.

If you're not against a third-party library, consider tabulate.

You mean output to the terminal? You could use a module like tabulate or pandas to do that for you. You could also write a quick function yourself that does the same thing; that would be a fairly easy project. Just transpose the data, calculate the max length in each column, then print row by row while padding to the max length. Probably 8 lines of code.

Sphinx supports ReStructuredText and Markdown.

MyST-Markdown supports MathJaX and Sphinx roles and directives. https://myst-parser.readthedocs.io/en/latest/

jupyter-book supports ReStructuredText, Jupyter Notebooks, and MyST-Markdown documents:

You can build Sphinx and Jupyter-Book projects with the ReadTheDocs container, which already has LaTeX installed: https://github.com/executablebooks/jupyter-book/issues/991

myst-templates/plain_latex_book:

your task is very very broad

you mention you don't want to deal with AWS, if it's because of ad-hoc installation concerns and nothing else you can just run your notebooks in ready-made solutions like Google Colab, or Jupyter-book in Github ( https://github.com/executablebooks/jupyter-book ))

that would cover a lot of use cases right away without next to no learning curve

If you don't want to deal with AWS or similar, in that case:

- if it's a static notebook then you can obviously render it and serve the web content (might seem obvious but needs to be considered)

- if it's dynamic but has light hardware requirements, you can try jupyterlite which runs in the browser and should do a pyodine (webassembly CPython kernel) can do: https://jupyterlite.readthedocs.io/en/latest/try/lab/

- otherwise, you can try exposing a dockerised jupyter env ( as in https://github.com/MKAbuMattar/dockerized-jupyter-notebook/b... ) or even better a nixified one ( https://github.com/tweag/jupyenv )

there might be other approaches I'm missing, but I think that's pretty much it that doesn't entail some proprietary solution or an ad-hoc installation as you've been doing

Note: If you want to present a cleaner version of the notebook without assertions, you can use Jupyter book to render it into a site and use the remove-cell tag to omit assertions from the output.

See this thread for example.

And maybe take a look at executablebooks/jupyter-book.

Our team has transferred from Sphinx for documentation to JupyterBook. There have been some growing pains with it but I prefer the look of the output and being able to play with the examples on Colab or Binder at the click of a button is a great feature.