# Geocode with geoPy:
from geopy.geocoders import Nominatim
geolocator = Nominatim()
Testing the geocode function:
location = geolocator.geocode("London")
print location.address
London, Greater London, England, United Kingdom
print(location.latitude, location.longitude)
(51.5073219, -0.1276473)
Now use on the countries of interest:
countries = [u"C\xf4te d'Ivoire (Ivory Coast)", u'Italy', u'USA',
u'South Africa', u'Philippines', u'Democratic Republic of the Congo',
u'Gabon', u'Sudan (South Sudan)', u'Uganda', u'England', u'Russia']
locations = [geolocator.geocode(c) for c in countries]
longitudes = [l.longitude for l in locations]
print longitudes
[-5.5679458, 12.674297, -100.4458825, 24.991639, 122.7312101, 23.8222636, 11.6899699, 29.6667897, 32.2166578, -0.540240236617432, 97.7453061]
latitudes = [l.latitude for l in locations]
print latitudes
[7.9897371, 42.6384261, 39.7837304, -28.8166236, 12.7503486, -2.9814344, -0.8999695, 7.8699431, 1.5333554, 52.7954791, 64.6863136]
dict_countries = {"country": countries, "lat": latitudes, "lon": longitudes}
print dict_countries
{'lat': [7.9897371, 42.6384261, 39.7837304, -28.8166236, 12.7503486, -2.9814344, -0.8999695, 7.8699431, 1.5333554, 52.7954791, 64.6863136], 'country': [u"C\xf4te d'Ivoire (Ivory Coast)", u'Italy', u'USA', u'South Africa', u'Philippines', u'Democratic Republic of the Congo', u'Gabon', u'Sudan (South Sudan)', u'Uganda', u'England', u'Russia'], 'lon': [-5.5679458, 12.674297, -100.4458825, 24.991639, 122.7312101, 23.8222636, 11.6899699, 29.6667897, 32.2166578, -0.540240236617432, 97.7453061]}
import pandas as pd
df_countries = pd.DataFrame(dict_countries)
df_countries.head()
| country | lat | lon | |
|---|---|---|---|
| 0 | Côte d'Ivoire (Ivory Coast) | 7.989737 | -5.567946 |
| 1 | Italy | 42.638426 | 12.674297 |
| 2 | USA | 39.783730 | -100.445882 |
| 3 | South Africa | -28.816624 | 24.991639 |
| 4 | Philippines | 12.750349 | 122.731210 |
df_countries.to_csv("data/out/countries_ebola.csv", encoding="utf-8", index_col=False)
# Plot with geoplotlib
import geoplotlib
from geoplotlib.utils import read_csv
data_eb = read_csv("data/out/countries_ebola.csv")
print data_eb
DataAccessObject(['', 'country', 'lat', 'lon'] x 11)
geoplotlib.dot(data_eb)
geoplotlib.show()
# Plot with Folium
import folium
from pandas import read_csv
countries_ebola = read_csv("data/out/countries_ebola.csv")
countries_ebola.head()
| Unnamed: 0 | country | lat | lon | |
|---|---|---|---|---|
| 0 | 0 | Côte d'Ivoire (Ivory Coast) | 7.989737 | -5.567946 |
| 1 | 1 | Italy | 42.638426 | 12.674297 |
| 2 | 2 | USA | 39.783730 | -100.445882 |
| 3 | 3 | South Africa | -28.816624 | 24.991639 |
| 4 | 4 | Philippines | 12.750349 | 122.731210 |
map_eb = folium.Map(zoom_start=2, location=[40.486037, -24.090964])
for i in range(len(countries_ebola)):
map_eb.simple_marker([countries_ebola['lat'][i], countries_ebola['lon'][i]],
popup=countries_ebola['country'][i], marker_color='green')
map_eb
map_eb.create_map(path='data/out/ebola_countries.html')