Choropleth Geopandas County

geopandas-county.py

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/fips-unemp-16.csv", dtype={"fips": str})
df.rename(columns={'fips':'GEOID'}, inplace=True)

# read US county shapefile
county_map = gpd.read_file('data/cb_2018_us_county_500k/cb_2018_us_county_500k.shp')
county_map = county_map[~county_map.STATEFP.isin(['02','15','72','60','66','69','78','11'])]
county_map = county_map.to_crs("EPSG:2163")

# read US state shapefile
state_map = gpd.read_file('data/cb_2018_us_state_500k/cb_2018_us_state_500k.shp')
state_map = state_map[~state_map.STATEFP.isin(['02','15','72','60','66','69','78','11'])]
# fixing projection
state_map = state_map.to_crs('EPSG:2163')

# merging datasets
df_county_map = pd.merge(df,county_map,on='GEOID')
df_county_map = gpd.GeoDataFrame(df_county_map)

fig, ax = plt.subplots(1, figsize=(10, 6),dpi=300,facecolor='w',edgecolor='w')
# colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes('right', size='5%', pad=0.1)
#plot
gax = df_county_map.plot(ax=ax,cax=cax, column='unemp', cmap='Reds', legend=True, edgecolor='grey', linewidth=0.5,legend_kwds={'shrink':0.6})
# plot state boundaries
state_map.boundary.plot(ax=gax, color='black', linewidth=0.5,zorder=1)
ax.set_axis_off()
# label the colorbar
cax.tick_params(labelsize=10)
cax.set_ylabel('Unemployment Rate',fontsize=10)
cax.yaxis.set_label_position('left')
plt.show()