Open a shapefile using Fiona, and plot its features using Matplotlib and Descartes

shapefile.py

import matplotlib.pyplot as plt
from matplotlib.collections import PatchCollection
from descartes import PolygonPatch
import fiona
from shapely.geometry import Polygon, MultiPolygon, shape

# We can extract the London Borough boundaries by filtering on the AREA_CODE key
mp = MultiPolygon(
    [shape(pol['geometry']) for pol in fiona.open('data/boroughs/boroughs.shp')
    if pol['properties']['AREA_CODE'] == 'LBO'])

# We can now do GIS-ish operations on each borough polygon!
# we could randomize this by dumping the polygons into a list and shuffling it
# or we could define a random colour using fc=np.random.rand(3,)
# available colour maps are here: http://wiki.scipy.org/Cookbook/Matplotlib/Show_colormaps
cm = plt.get_cmap('RdBu')
num_colours = len(mp)
 
fig = plt.figure()
ax = fig.add_subplot(111)
minx, miny, maxx, maxy = mp.bounds
w, h = maxx - minx, maxy - miny
ax.set_xlim(minx - 0.2 * w, maxx + 0.2 * w)
ax.set_ylim(miny - 0.2 * h, maxy + 0.2 * h)
ax.set_aspect(1)

patches = []
for idx, p in enumerate(mp):
    colour = cm(1. * idx / num_colours)
    patches.append(PolygonPatch(p, fc=colour, ec='#555555', alpha=1., zorder=1))
ax.add_collection(PatchCollection(patches, match_original=True))
ax.set_xticks([])
ax.set_yticks([])
plt.title("Shapefile polygons rendered using Shapely")
plt.savefig('data/london_from_shp.png', alpha=True, dpi=300)
plt.show()