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Using legendgram as a map legend

With mapclassify, you can replace standard legend with one composed of a histogram of the values showed in the colors you can see in the map.

import geopandas as gpd
from libpysal import examples

import mapclassify

gdf = gpd.read_file(examples.get_path("NAT.shp")).to_crs(epsg=5070)
gdf.head()

	NAME	STATE_NAME	STATE_FIPS	CNTY_FIPS	FIPS	STFIPS	COFIPS	FIPSNO	SOUTH	HR60	...	BLK90	GI59	GI69	GI79	GI89	FH60	FH70	FH80	FH90	geometry
0	Lake of the Woods	Minnesota	27	077	27077	27	77	27077	0	0.000000	...	0.024534	0.285235	0.372336	0.342104	0.336455	11.279621	5.4	5.663881	9.515860	POLYGON ((49050.745 2838555.999, 49054.346 285...
1	Ferry	Washington	53	019	53019	53	19	53019	0	0.000000	...	0.317712	0.256158	0.360665	0.361928	0.360640	10.053476	2.6	10.079576	11.397059	POLYGON ((-1704187.741 2978490.561, -1690089.6...
2	Stevens	Washington	53	065	53065	53	65	53065	0	1.863863	...	0.210030	0.283999	0.394083	0.357566	0.369942	9.258437	5.6	6.812127	10.352015	POLYGON ((-1598348.829 2964085.947, -1605943.0...
3	Okanogan	Washington	53	047	53047	53	47	53047	0	2.612330	...	0.155922	0.258540	0.371218	0.381240	0.394519	9.039900	8.1	10.084926	12.840340	POLYGON ((-1713271.344 2979541.451, -1712880.7...
4	Pend Oreille	Washington	53	051	53051	53	51	53051	0	0.000000	...	0.134605	0.243263	0.365614	0.358706	0.387848	8.243930	4.1	7.557643	10.313002	POLYGON ((-1574802.183 3066600.159, -1545432.6...

5 rows × 70 columns

Using legendgrams with plots can be simple:

ax = gdf.plot("GI89")
ax.axis("off")
classifier = mapclassify.EqualInterval(gdf["GI89"], k=100)
classifier.plot_legendgram(ax=ax)

<AxesHostAxes: >

but you can also tweak quite a bit, like the size & location:

We’ll make the legend a little longer & shorter, as well as moving it to the lower right:

ax = gdf.plot("GI89")
ax.axis("off")
classifier = mapclassify.EqualInterval(gdf["GI89"], k=100)
classifier.plot_legendgram(ax=ax, legend_size=("60%", "12%"), loc="lower right")

<AxesHostAxes: >

We can clip the display to a smaller range to cut off any dangling long tails and use different scheme:

ax = gdf.plot("GI89", scheme="Quantiles")
ax.axis("off")
classifier = mapclassify.Quantiles(gdf["GI89"])
hax = classifier.plot_legendgram(
    ax=ax,
    legend_size=("50%", "12%"),
    loc="lower left",
    clip=(0.3, 0.5),
    vlines=True,
)

ret = hax.hist(classifier.y, bins=50, color="0.1")

ret[2]

<BarContainer object of 50 artists>

hax.get_ylim()

(np.float64(0.0), np.float64(237.0375))

When calling plot_legendgram() the most recently plotted cmap is used unless otherwise stipulated:

ax = gdf.plot("GI89", cmap="plasma", scheme="Quantiles")
ax.axis("off")
classifier = mapclassify.Quantiles(gdf["GI89"])
hax = classifier.plot_legendgram(
    ax=ax,
    legend_size=("15%", "30%"),
    loc="lower right",
    clip=(0.3, 0.4),
)

Further, you can work directly on the Axes, if you prefer very fine-grained control over the plot parameters. legendgram returns the Axes on which the legendgram was plotted, so you can modify it after the fact:

riverside_loc = gdf.iloc[2255]
riverside = gpd.GeoDataFrame(riverside_loc.to_frame().T)
riverside

	NAME	STATE_NAME	STATE_FIPS	CNTY_FIPS	FIPS	STFIPS	COFIPS	FIPSNO	SOUTH	HR60	...	BLK90	GI59	GI69	GI79	GI89	FH60	FH70	FH80	FH90	geometry
2255	Riverside	California	06	065	06065	6	65	6065	0	4.898903	...	5.43321	0.284	0.376737	0.383226	0.368177	9.769959	9.2	11.145612	12.67834	POLYGON ((-1841135.909 1346087.732, -1926639.5...

1 rows × 70 columns

ax = gdf.plot("GI89", cmap="Blues", figsize=(10, 10))
ax.axis("off")
classifier = mapclassify.EqualInterval(gdf["GI89"], k=100)
hax = classifier.plot_legendgram(ax=ax, legend_size=("65%", "25%"))

# riverside in red
riverside.plot("GI89", linewidth=2, edgecolor="r", ax=ax)
# mark Riverside's Gini in the legend
hax.vlines(riverside_loc["GI89"], 0, 1, color="r", transform=hax.transAxes)

<matplotlib.collections.LineCollection at 0x156340690>