US Military

import pandas as pd, folium
import impl as u

US Bases around the World

Data from here, itself based on David Vine's work.

clat,clon=33, 40

m = folium.Map(location=[clat, clon], zoom_start=7)

df = pd.read_csv("https://raw.githubusercontent.com/meflynn/troopdata/master/data-raw/basedata.csv",encoding = "ISO-8859-1", engine='python')
df = df[['countryname','basename','lat','lon']]
df = df.dropna()

for index, row in df.iterrows():
    folium.Marker([row['lat'], row['lon']],tooltip=row['basename'] + " " + row['countryname']
                  ).add_to(m)


m.save('usbases-out.html')

Output

Navy

import pandas as pd, folium
m = folium.Map(location=[33,40], zoom_start=4)
df = u.usnavy()
for index, row in df.iterrows():
    folium.Marker([row['lat'], row['lon']],tooltip=row['name'] + " Heading: " + row['bearing'] + " Speed (kn): " + row['speed']
                  ).add_to(m)

m.save('usnavy-out.html')

Output

Nuclear Bomb, Missile Sites

import pandas as pd, folium

df = pd.read_csv('nuke.csv')

clat,clon=33, -111
m = folium.Map(location=[clat, clon], zoom_start=4)

def split_nth(s, sep, n):
    n_split_groups = []
    groups = s.split(sep)
    while len(groups):
          n_split_groups.append(sep.join(groups[:n]))
          groups = groups[n:]
    return n_split_groups

for index, row in df.iterrows():
    descr = str(row['description'])
    descr = "<br>".join(split_nth(descr, " ", 5))
    s = str(row['bombs'])  + " bombs, " + descr
    s = s.replace("nan","")
    folium.Marker(        
        [row['latitude'], row['longitude']], tooltip=s
    ).add_to(m)

m.save('nuke-out.html')    

Output