ajfriend

That said, this feels like an issue with rendering geometry rather than with the index itself. I’m curious to hear more about why you think the lack of congruency affects H3’s performance for spatial joins. Under the hood, it’s still a parent–child hierarchy very similar to S2’s — H3 children are topological rather than geometric children (even though they still mostly overlap).

    import duckboat as uck

    csv = 'https://raw.githubusercontent.com/allisonhorst/palmerpenguins/main/inst/extdata/penguins.csv'

    uck.Table(csv).do(
        "where sex = 'female' ",
        'where year > 2008',
        'select *, cast(body_mass_g as double) as grams',
        'select species, island, avg(grams) as avg_grams group by 1,2',
        'select * replace (round(avg_grams, 1) as avg_grams)',
        'order by avg_grams',
    )

I write a lot of SQL anyway, so this approach is nice in that I find I almost never need to look up function syntax like I would with Pandas, since it is just using DuckDB SQL under the hood, but removing the need to write `select * from ...` repeatedly. And when you're ready to exit the data exploration phase, its easy to gradually translate things back to "real SQL".

The whole project is pretty small, essentially just a light wrapper around DuckDB to do this expression chaining and lazy evaluation.