Example for geopandas polygon geometry: Use the Natural Earth dataset, change to Africa

examples/gallery/maps/choropleth_map.py

@@ -14,45 +14,28 @@
 """
 
 # %%
-import geodatasets
 import geopandas as gpd
 import pygmt
 
-# Read the example dataset provided by geodatasets.
-gdf = gpd.read_file(geodatasets.get_path("geoda airbnb"))
-print(gdf.head())
+provider = "https://naciscdn.org/naturalearth"
+world = gpd.read_file(f"{provider}/110m/cultural/ne_110m_admin_0_countries.zip")
 
-# %%
-fig = pygmt.Figure()
+# The dataset contains different attributes, here we focus on the population within
+# the different countries (column "POP_EST") for the continent "Africa".
+world["POP_EST"] *= 1e-6
+africa = world[world["CONTINENT"] == "Africa"].copy()
 
-fig.basemap(
-    region=gdf.total_bounds[[0, 2, 1, 3]],
-    projection="M6c",
-    frame="+tPopulation of Chicago",
-)
-
-# The dataset contains different attributes, here we select the "population" column to
-# plot.
+fig = pygmt.Figure()
+fig.basemap(region=[-19.5, 53, -37.5, 38], projection="M10c", frame="+n")
 
-# First, we define the colormap to fill the polygons based on the "population" column.
-pygmt.makecpt(
-    cmap="acton",
-    series=[gdf["population"].min(), gdf["population"].max(), 10],
-    continuous=True,
-    reverse=True,
-)
+# First, we define the colormap to fill the polygons based on the "POP_EST" column.
+pygmt.makecpt(cmap="acton", series=(0, 100), reverse=True)
 
 # Next, we plot the polygons and fill them using the defined colormap. The target column
 # is defined by the aspatial parameter.
-fig.plot(
-    data=gdf,
-    pen="0.3p,gray10",
-    fill="+z",
-    cmap=True,
-    aspatial="Z=population",
-)
+fig.plot(data=africa, pen="0.8p,gray50", fill="+z", cmap=True, aspatial="Z=POP_EST")
 
 # Add colorbar legend.
-fig.colorbar(frame="x+lPopulation", position="jML+o-0.5c+w3.5c/0.2c")
+fig.colorbar(frame="x+lPopulation (millions)", position="jML+o2c/-2.5c+w5c+ef0.2c+ml")
 
 fig.show()