constructed with seaborn object-style plots instead.
These kind of plots are much more structured for the workflow I use and the way I think about plotting,
clearly delineating between a plot;
some visual on the plot;
some statistical transformation;
some movement, labeling or scaling operation.
They are also, however, fairly new and still considered experimental.

They also don't allow *quite* the customization that the other plots do,
and seem either a little buggy or I have not fully understood them yet in regards to ticks and labels.

```{python}
# | label: fig-groundlevel-so
# | fig-cap: "Nuclear explosions, 1945-98"
import seaborn.objects as so
import matplotlib.dates as mdates

above_cat = pl.Series(
    [
        "ATMOSPH",
        "AIRDROP",
        "TOWER",
        "BALLOON",
        "SURFACE",
        "BARGE",
        "ROCKET",
        "SPACE",
        "SHIP",
        "WATERSUR",
        "WATER SU",
    ]
)
df_groundlevel = (
    df.with_columns(
    above_ground=pl.col("type").map_elements(
        lambda x: True if x in above_cat else False, return_dtype=bool
    ))
    .group_by(pl.col("year", "country", "above_ground"))
    .agg(count=pl.len())
    .sort("year")
)

fig, ax = plt.subplots()
ax.xaxis.set_tick_params(rotation=90)

from seaborn import axes_style
p = (
    so.Plot(df_groundlevel, x="year", y="count", color="country")
    .add(
        so.Bars(),
        so.Stack(),
        data=df_groundlevel.filter(pl.col("above_ground") == True).sort("country"),
    )
    .add(
        so.Bars(),
        so.Stack(),
        data=df_groundlevel.filter(pl.col("above_ground") == False).with_columns(
            count=pl.col("count") * -1
        ).sort("country"),
    )
    .label(x="Year", y="Count")
    .scale(
        x=so.Continuous().tick(locator=mdates.YearLocator(base=5), minor=4).label(like="{x:.0f}"),
        # x=so.Nominal().tick(locator=mdates.YearLocator(base=5), minor=4), # this might work in the future
    )
    .theme({
        **axes_style("darkgrid"),
        "xtick.bottom": True,
        "ytick.left": True
    })
    .on(ax)
    .plot()
)
```