datagen VS python-tabulate

For fake data, shameless plug for https://github.com/MaterializeInc/datagen/tree/main

Building and testing a real-time fraud detection application requires a continuous stream of realistic data. But generating that data can be a challenge. That's why we recently created the Datagen CLI, a simple tool that helps you create believable fake data using the FakerJS API.

Generate fake data with a normalized schema of your choosing with this tool from Materialize, then denormalize it and build a warehouse model.

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.