python-tabulate VS textual

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.

The Textual project has a lot of screenshots in its documentation. These screenshots are built with the docs, so they are always up to date.

https://textual.textualize.io/

Textual[0] does this for CLI apps. That’s not for full GUI apps, but it’s very DOM-like, uses CSS selectors, etc. and a cool option when it meets your needs.

[0] https://github.com/Textualize/textual

For future projects that need a TUI beyond normal printing to a terminal, I'd recommend taking a look at Textual.

https://jupyterbook.org/en/stable/content/code-outputs.html#...

`less -R` is not the default.

FWIW, textual (and urwid) does ANSII escape codes well: https://github.com/Textualize/textual

  touch file$'\n'name

After installing logmerger, you can run a self-contained demo by running logmerger --demo, to view two log files before and after they are merged, and to play with the user-interface features provided by textual.

I think it just survived naturally, filling in the cracks left by Java / C++.

And not the era of Textual (https://textual.textualize.io/) is here, python may get the spotlight even more.