While looking at a new feature for Gramps, it struck me that the add-on was storing CSV data in a Gramps Note and some of the bugs were standard parsing problems. And that this new Note type probably needed to embed headers for future-proofing. Particularly since the DNA segment map data could come from different sources… where the same (untransformed) data can be expected but in differing order and with differing header labels. Which is the most simple crosswalk situation.
Then I thought about the CSV Import module for Gramps that already did a lot of flexibility work with external CSVs: parsing handling blanks lines, multi-section with differing content validation, header recognition, and so forth.
With a little bit of surfing I discovered Python has CSV libraries that build header dictionaries. Should we be using those?
Are there standard libraries Gramps already uses that developers should be leveraging rather than writing their own parsers?
Data Mining Libraries
Data Processing & Modeling Libraries
(example: Using Nucleus and TensorFlow for DNA Sequencing Error Correction)