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I'm not sure how much I like the logging as a user. While saving logs to file would help debugging a lot, I'm not used to Python packages that log to console when using it interactively. I'd consider turning off the logging to console by default.

I like to see the console as well (as a developer), but I think you are right about the users. I turned it off by default but added a log_to_console boolean argument to configure_logging() function. Setting this to True also prints the log to the console using rich.logging.RichHandler()

The result when loading from csv vs h5 differs. The csv result has an extra columns, and ends up with the body parts in the dataframe itself, but the .h5 has these in the column headings. I'd expect that the representation of the data should be the same regardless of which file format it was loaded from.

Yes, I am aware of that and was planning to take care of it later, since I plan to store the data into a custom object anyway. However, I might as well write a convenience function now, converting from the csv-imported to the h5-imported version.

I have now also added a conversion from the CSV-imported table to the h5-imported one, so both types of files now return the same dataframe.

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I like to see the console as well (as a developer), but I think you are right about the users. I turned it off by default but added a log_to_console boolean argument to configure_logging() function. Setting this to True also prints the log to the console using rich.logging.RichHandler()

In the end, I purged the log_to_console boolean to simplify from the logging config and to remove the rich dependency (which I am not using for anything else at the moment)