Mapping value ranges to colours

[david-waterworth]

I know you can use data_color to colourise by values, but is there a way of defining ranges and assigning specific colours to specific buckets - i.e. I want values above 95% to be "good" (green), between say 80% and 95% to be "ok" (amber) and belw 80% "bad" (i.e. red).?

[jrycw]

@david-waterworth, here's a reference solution that came to mind.

import polars as pl
from great_tables import GT, html, style, loc
from great_tables.data import towny


towny_mini = pl.from_pandas(towny).head(10)

(
    GT(
        towny_mini[["name", "land_area_km2"]],
        rowname_col="name",
    )
    .tab_header(
        title="The Municipalities of Ontario",
        subtitle="The top 10 highest population density in 2021",
    )
    .tab_stubhead(label="Municipality")
    .fmt_number(columns=["land_area_km2"], decimals=1)
    .cols_label(
        land_area_km2=html("land area, <br>km<sup>2</sup>"),
    )
    .tab_style(
        style=style.fill(color="red"),
        locations=loc.body(
            columns="land_area_km2",
            rows=pl.col("land_area_km2").lt(pl.col("land_area_km2").quantile(0.8)),
        ),
    )
    .tab_style(
        style=style.fill(color="orange"),
        locations=loc.body(
            columns="land_area_km2",
            rows=pl.col("land_area_km2").ge(pl.col("land_area_km2").quantile(0.8)),
        ),
    )
    .tab_style(
        style=style.fill(color="green"),
        locations=loc.body(
            columns="land_area_km2",
            rows=pl.col("land_area_km2").ge(pl.col("land_area_km2").quantile(0.95)),
        ),
    )
)