Load the notebook with commands developed in step 2.5. (click on the link) and reload all steps:

https://colab.research.google.com/drive/1xHbcvflGxg-T2cCXkhvGE2Ubyr9t5_hE?usp=sharing

Insert the field 'Combined_Score' in the JSON

The next code helps to insert the field 'Combined_Score' in the JSON variable wdcs.

# Creating a new field for each #country: 'combined_score' of risk per country

list_countries_names = list(df_risk['country'])

# Selecting each country available in the JSON data

for country in wdcs['features']:

  # Obtaining the name of each country

  country_name = country['properties']['name']

  # Verifying if the country name in JSON exists in the dataframe with the immigration data

  if country_name in list_countries_names:

    # Extracting the Combined Score of risk

    combined_score_value = df_risk.loc[df_risk['country'] == country_name,'Combined_Score'].values[0]

    # Inserting the 'combined_score_value' of risk score per country in the JSON variable

    country['properties']['combined_score'] = float(combined_score_value)

  # If not found the country name, then insert the value zero in the JSON variable.

  else:

    country['properties']['combined_score'] = 0.0

wdcs

{'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'properties': {'name': 'Afghanistan', 'combined_score': 0.6702986215}, 'geometry': {'type': 'Polygon', 'coordinates': [[[61.210817, 35.650072], ..., [61.210817, 35.650072]]]}, 'id': 'AFG'}, {'type': 'Feature', 'properties': {'name': 'Angola', 'combined_score': 0.767313165}, 'geometry': {'type': 'MultiPolygon', 'coordinates': [[[[16.326528, -5.87747], ...

Verifying if the total risk score had been inserted

The next code helps to show if the new value of the risk score had been correctly inserted into the JSON variable.

# Extracting properties of each country

for country in wdcs['features']:

  #print(country['properties'])

  print(country['properties']['combined_score'])

0.6702986215 0.767313165 0.372736933 0.1938966 0.6777903135 0.203256629 0.0 0.0 0.393912523 0.198900957 0.245246763 ...

Drawing the country's risk in the political layer with the map

The next code shows in a map the new data per country, i.e., 'combined_score'.

import folium

import matplotlib

#import branca.colormap as cm

fill_opacity = 0.7

border_color = 'black'

color_list = ['Purples','Reds','Wistia','Greens','Blues']

color_map_name = color_list[1]

custom_cm = matplotlib.colormaps.get_cmap(color_map_name)

min_value = min(list(df_risk['Combined_Score']))

max_value = max(list(df_risk['Combined_Score']))

feature_name = 'combined_score'

def get_color(custom_cm, val, vmin, vmax):

    return matplotlib.colors.to_hex(custom_cm((val-vmin)/(vmax-vmin)))

# Creating a new map

map = folium.Map(location=[30,10], zoom_start=3, tiles = 'cartodbpositron')

# Creating a new Layer using population data

coro = folium.GeoJson(

        wdcs,

        style_function=lambda feature: {

            #'fillColor':  '#65b3d0',

            'fillColor': get_color(custom_cm, feature['properties'][feature_name], vmin=min_value, vmax=max_value),

            'color': border_color,

            'fillOpacity': fill_opacity,

        },

        tooltip = folium.GeoJsonTooltip(fields=[feature_name], aliases=['Country Risk']),

        name = feature_name

    )

# Creating a new feature group to put population layer inside it

pop_group = folium.FeatureGroup(name=feature_name, show=True)

# Adding population layer to feature group

pop_group.add_child(coro)

# Inserting feature group into the map

map.add_child(pop_group)

# Inserting Layer control in the map

map.add_child(LayerControl(colapsed=False))

map