Geopandas 减少图例大小（并删除地图下方的空白区域）

Question

I would like to know how to change the legend automatically generated by Geopandas. Mostly I would like to reduce its size because it's quite big on the generated image. The legend seems to take all the available space.

Additional question, do you know how to remove the empty space below my map ? I've tried with

pad_inches = 0, bbox_inches='tight' 

but I still have an empty space below the map.

Thanks for your help.

Answer 1

This works for me:

some_geodataframe.plot(..., legend=True, legend_kwds={'shrink': 0.3})

Other options here: https://matplotlib.org/api/_as_gen/matplotlib.pyplot.colorbar.html

Answer 2

To show how to get proper size of a colorbar legend accompanying a map created by geopandas ' plot() method I use the built-in ' naturalearth_lowres ' dataset.

The working code is as follows.

import matplotlib.pyplot as plt
import geopandas as gpd

world = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
world = world[(world.name != "Antarctica") & (world.name != "Fr. S. Antarctic Lands")]  # exclude 2 no-man lands

plot as usual, grab the axes 'ax' returned by the plot

colormap = "copper_r"   # add _r to reverse the colormap
ax = world.plot(column='pop_est', cmap=colormap, \
                figsize=[12,9], \
                vmin=min(world.pop_est), vmax=max(world.pop_est))

map marginal/face deco

ax.set_title('World Population')
ax.grid() 

colorbar will be created by ...

fig = ax.get_figure()
# add colorbar axes to the figure
# here, need trial-and-error to get [l,b,w,h] right
# l:left, b:bottom, w:width, h:height; in normalized unit (0-1)
cbax = fig.add_axes([0.95, 0.3, 0.03, 0.39])   
cbax.set_title('Population')

sm = plt.cm.ScalarMappable(cmap=colormap, \
                norm=plt.Normalize(vmin=min(world.pop_est), vmax=max(world.pop_est)))

at this stage, 'cbax' is just a blank axes, with un needed labels on x and y axes blank-out the array of the scalar mappable 'sm'

sm._A = []

draw colorbar into 'cbax'

fig.colorbar(sm, cax=cbax, format="%d")

# dont use: plt.tight_layout()
plt.show()

Read the comments in the code for useful info.

The resulting plot: