>>> import geopandas, libpysal, mapclassify
>>> gdf = geopandas.read_file(libpysal.examples.get_path("NAT.shp"))
>>> ax = gdf.plot("GI89")
>>> classifier = mapclassify.EqualInterval(gdf["GI89"])
>>> classifier.plot_legendgram(ax=ax)

>>> import mapclassify as mpc, geopandas as gpd, matplotlib.pyplot as plt
>>> from legendgram import legendgram
>>> data = gpd.read_file(ps.examples.get_path('south.shp'))
>>> data.crs = {'init': 'epgs:4269'}
>>> data.to_crs(epsg=5070)
>>> f,ax = plt.subplots()
>>> data.plot('DV80', k=10, ax=ax, scheme='Quantiles')
>>> bins = mpc.Quantiles(data['DV80'].values, k=10).bins
>>> from legendgram import legendgram
>>> import palettable.matplotlib as palmpl
>>> legendgram(f,ax,data['DV80'],bins,pal=palmpl.Viridis_10,
               legend_size=(.5,.2), # legend size in fractions of the axis
               loc = 'upper left' # matplotlib-style legend locations
               clip = (2,10) # clip the displayed range of the histogram
               )