Python:在Dataframe中从Shapefile到多边形的迭代到彩色Cartopy贴图 [英] Python: Iteration over Polygon in Dataframe from Shapefile to color cartopy map

问题描述

我正在根据某些值在Cartopy地图上为国家/地区着色.我正在使用Geopandas和来自以下文件的shapefile:

I'm coloring countries on a cartopy map according to certain values. I'm using geopandas and a shapefile from: https://www.naturalearthdata.com/

While iterating over the dataframe df to get the geometry of certain countries I encountered a problem. I can get the geometry of Countries with a Multipolygon geometry, but I can't get do it with countries with a polygon geometry e.g. Belgium or Austria.

Here is my code:

#imports
import matplotlib.pyplot as plt
import matplotlib
import cartopy
from cartopy.io import shapereader
import cartopy.crs as ccrs
import geopandas
import numpy as np

# get natural earth data (http://www.naturalearthdata.com/)
# get country borders
resolution = '10m'
category = 'cultural'
name = 'admin_0_countries'
shpfilename = shapereader.natural_earth(resolution, category, name)

# read the shapefile using geopandas
df = geopandas.read_file(shpfilename)


# Set up the canvas
fig = plt.figure(figsize=(20, 20))
central_lon, central_lat = 0, 45
extent = [-10, 28, 35, 65]
ax = plt.axes(projection=cartopy.crs.Orthographic(central_lon, central_lat))
ax.set_extent(extent)
ax.gridlines()

# Add natural earth features and borders
ax.add_feature(cartopy.feature.BORDERS, linestyle='-', alpha=0.8)
ax.add_feature(cartopy.feature.OCEAN, facecolor=("lightblue"))
ax.add_feature(cartopy.feature.LAND, facecolor=("lightgreen"), alpha=0.35)
ax.coastlines(resolution='10m')

# Countries and value
countries = ['Sweden', 'Netherlands', 'Ireland', 'Denmark', 'Germany', 'Greece', 'France', 'Spain', 'Portugal', 'Italy', 'United Kingdom', 'Austria']
value = [47.44, 32.75, 27.53, 23.21, 20.08, 18.08, 17.23, 13.59, 12.13, 5.66, 22.43, 7]

# Normalise values
value_norm = (value-np.nanmin(value))/(np.nanmax(value) - np.nanmin(value))

# Colourmap
cmap = matplotlib.cm.get_cmap('YlOrBr')


for country, value_norm in zip(countries, value_norm):
    # read the borders of the country in this loop
    poly = df.loc[df['ADMIN'] == country]['geometry'].values[0]
    # get the color for this country
    rgba = cmap(value_norm)
    # plot the country on a map
    ax.add_geometries(poly, crs=ccrs.PlateCarree(), facecolor=rgba, edgecolor='none', zorder=1)

# Add a scatter plot of the original data so the colorbar has the correct numbers
dummy_scat = ax.scatter(value, value, c=value, cmap=cmap, zorder=0)
fig.colorbar(mappable=dummy_scat, label='Percentage of Do and Dont`s [%]', orientation='horizontal', shrink=0.8)

plt.show()
fig.savefig("Länderübersicht.jpg")

How can I iterate over, or rather color, these countries or do I have to get another shapefile? Thanks!

解决方案

Taking inspiration from the error code TypeError: 'Polygon' object is not iterable I started from the assumption that we need some kind of iterable, such as a list of polygons. Drawing from this answer I found the function shapely.geometry.MultiPolygon does the job. You simply pass it a list of polygons. Add a little logic to take this action only when a Polygon rather than a MultiPolygon is detected and we have:

poly = df.loc[df['ADMIN'] == country]['geometry'].values[0]
if type(poly) == shapely.geometry.polygon.Polygon:
    simple_poly = df.loc[df['ADMIN'] == country]['geometry'].values[0]
    list_polys = [poly, poly]
    poly = shapely.geometry.MultiPolygon(list_polygons)

This is a rather hacky solution that will print the polygon twice, so be aware if you later decide to make it transparent or something. Alterantively, in place of [poly, poly] you could use [poly, some_other_poly_outside_map_area].

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