Implement h3.compact and uncompact

[dahnj]

Implement h3.compact and uncompact

[DahnJ]

Hi @KoalaGeo glad you like the library!

Currently, I have no such plans.

What behaviour would you expect these functions to have in H3-Pandas?

compact and uncompact are set operations and applying them to a dataframe with potential additional data is not straightforward. I see two general options

1. The data would have to be removed and the returned dataframe would be index-only. This would not be difficult to implement. I can see how that's still potentially convenient, as it allows you to string further h3 operations.
2. The compacted/uncompacted cells would have to be matched back with the rows they originated from with some (dis)aggregation operation being performed on the data. k_ring_smoothing does something similar.

It would be helpful if you could elaborate on your usecase to drive the potential API.

[KoalaGeo]

I'm working with polygons, primarily categorical data (geology, rock type, formation name, descriptions etc).

I'm using polyfill / polyfill_resample, however it's resulting in massive numbers of polygons to accurately represent the input geometries - resolution 13 or 14 required.

I could do with the compact function at the end to simplify the data to fewer features using an index attribute eg rock type.