Load the notebook with commands developed in steps and 1.2 and 1.3. (click on the link):

https://colab.research.google.com/drive/1OfDdV5wv_F8fkRpNWlcI8r553u0AsPZ1?usp=sharing

Inserting Total of immigration in the world GeoJSON data

The next code helps to insert the value on column 'Total' from data frame df into a new field called 'population' in the wdcs GeoJSON variable. 

# Creating a new field for each country: total of immigration per country

list_countries_names = list(df['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 total of immigration

    population_value = df.loc[df['Country'] == country_name,'Total'].values[0]

    # Inserting the 'Total' of immigration per country in the JSON variable

    country['properties']['population'] = float(population_value)

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

  else:

    country['properties']['population'] = 0.0

wdcs

A part of the new output could be seen:

{'type': 'FeatureCollection', 'features': [{'type': 'Feature', 'properties': {'name': 'Afghanistan', 'population': 58639.0}, 'geometry': {'type': 'Polygon', 'coordinates': [[[61.210817, 35.650072], [62.230651, 35.270664], [62.984662, 35.404041], [63.193538, 35.857166], ...

Verifying if the total population information had been inserted

The next code confirms if the previous command had been effective.

# Extracting properties of each country

for country in wdcs['features']:

  #print(country['properties'])

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

58639.0 

2113.0 

15699.0 

...

Drawing the population information in the political layer with the map

The next command will employ the new field 'Population' in GeoJSON to build a choropleth map.

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['Total']))

max_value = max(list(df['Total']))

feature_name = 'population'

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 pop.']),

        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