76 lines
2.1 KiB
Text
76 lines
2.1 KiB
Text
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constructed with seaborn object-style plots instead.
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These kind of plots are much more structured for the workflow I use and the way I think about plotting,
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clearly delineating between a plot;
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some visual on the plot;
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some statistical transformation;
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some movement, labeling or scaling operation.
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They are also, however, fairly new and still considered experimental.
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They also don't allow *quite* the customization that the other plots do,
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and seem either a little buggy or I have not fully understood them yet in regards to ticks and labels.
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```{python}
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# | label: fig-groundlevel-so
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# | fig-cap: "Nuclear explosions, 1945-98"
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import seaborn.objects as so
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import matplotlib.dates as mdates
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above_cat = pl.Series(
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[
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"ATMOSPH",
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"AIRDROP",
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"TOWER",
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"BALLOON",
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"SURFACE",
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"BARGE",
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"ROCKET",
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"SPACE",
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"SHIP",
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"WATERSUR",
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"WATER SU",
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]
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)
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df_groundlevel = (
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df.with_columns(
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above_ground=pl.col("type").map_elements(
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lambda x: True if x in above_cat else False, return_dtype=bool
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))
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.group_by(pl.col("year", "country", "above_ground"))
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.agg(count=pl.len())
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.sort("year")
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)
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fig, ax = plt.subplots()
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ax.xaxis.set_tick_params(rotation=90)
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from seaborn import axes_style
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p = (
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so.Plot(df_groundlevel, x="year", y="count", color="country")
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.add(
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so.Bars(),
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so.Stack(),
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data=df_groundlevel.filter(pl.col("above_ground") == True).sort("country"),
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)
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.add(
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so.Bars(),
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so.Stack(),
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data=df_groundlevel.filter(pl.col("above_ground") == False).with_columns(
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count=pl.col("count") * -1
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).sort("country"),
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)
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.label(x="Year", y="Count")
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.scale(
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x=so.Continuous().tick(locator=mdates.YearLocator(base=5), minor=4).label(like="{x:.0f}"),
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# x=so.Nominal().tick(locator=mdates.YearLocator(base=5), minor=4), # this might work in the future
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)
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.theme({
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**axes_style("darkgrid"),
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"xtick.bottom": True,
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"ytick.left": True
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})
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.on(ax)
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.plot()
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)
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```
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