def create_cartogram(filename):
"""
This function produces a cartogram map.
"""
data_raw = gpd.read_file(filename)
map = gplt.cartogram(data_raw,
projection=gcrs.Robinson(),
scale=data_raw['NUMPOINTS'],
hue=data_raw['NUMPOINTS'],
cmap='Reds',
k=5,
figsize=(30, 15)
)
return map
import warnings
@pytest.fixture(scope="module")
def countries():
return gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))
@pytest.mark.mpl_image_compare
@pytest.mark.parametrize("proj", [
gcrs.PlateCarree(),
gcrs.LambertCylindrical(),
gcrs.Mercator(),
gcrs.Miller(),
gcrs.Mollweide(),
gcrs.Robinson(),
gcrs.Sinusoidal(),
pytest.param(gcrs.InterruptedGoodeHomolosine(), marks=pytest.mark.xfail),
pytest.param(gcrs.Geostationary(), marks=pytest.mark.xfail),
gcrs.NorthPolarStereo(),
gcrs.SouthPolarStereo(),
gcrs.Gnomonic(),
gcrs.AlbersEqualArea(),
gcrs.AzimuthalEquidistant(),
gcrs.LambertConformal(),
gcrs.Orthographic(),
gcrs.Stereographic(),
pytest.param(gcrs.TransverseMercator(), marks=pytest.mark.xfail),
gcrs.LambertAzimuthalEqualArea(),
gcrs.WebMercator()
])
countries_gdf = countries_gdf.merge(SHDI_first, on='GDLCODE', how='left')
countries_gdf['shdi'].fillna(-1, inplace=True)
scheme = mapclassify.UserDefined(countries_gdf['shdi'],
bins=[
0, 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, 0.6,
0.65, 0.7, 0.75, 0.8, 0.85, 0.9, 1.0
])
background_color = '#d1e5f0'
missing_color = 'grey'
fig, ax = plt.subplots(
figsize=(20, 10),
facecolor=background_color,
subplot_kw={
'projection': gcrs.Robinson(),
# https://scitools.org.uk/cartopy/docs/latest/crs/projections.html
'facecolor': background_color
})
plt.title('Human Development Index by World Subdivisions',
fontdict={
'fontsize': 20,
'fontweight': 'bold'
})
cmap = plt.cm.get_cmap('Blues', len(scheme.bins) + 1)
cmap_list = [rgb2hex(cmap(i)) for i in range(cmap.N)]
cmap_list.insert(0, missing_color)
cmap_world = ListedColormap(cmap_list)
gplt.polyplot(
gdf = gdf.merge(df, on='name', how="left")
#print(gdf.info())
# ## Let's explore the total number of participants
# ### 2018 year:
# In[ ]:
df[['Foreign State', '2018_Total']].sort_values(by='2018_Total',ascending= False)
# In[ ]:
gplt.choropleth(gdf, hue=gdf['2018_Total'],projection=gcrs.Robinson(),
cmap='Purples', linewidth=0.5, edgecolor='gray',
k=None, legend=True, figsize=(20, 8))
plt.title("Total number of participants in 2018")
# ### 2007 for comparision:
# In[ ]:
gplt.choropleth(gdf, hue=gdf['2007_Total'],projection=gcrs.Robinson(),
cmap='Purples', linewidth=0.5, edgecolor='gray',
k=None, legend=True, figsize=(20, 8))
plt.title("Total number of participants in 2007")
# ## Number of winners