Multichoropleth.py

# multi choropleth

import pandas as pd  
import os  
pd.set_option('precision', 10)  
os.chdir("C:/Users/Dome/Desktop/nu/Wahl- und Strukturdaten/Tabellen/")  
ma = pd.read_csv("MASTERENG.csv",float_precision='round_trip')  
import folium  
import webbrowser  
  
state_geo="C:/Users/Dome/Desktop/nu/Wahl- und Strukturdaten/Wahlkreise.geojson"  
  
# a loop might be implemented here, but be useless, as the file size   
# would grow too much   
# the final file size is around 80 Mb
  
m = folium.Map(location=[51, 10], zoom_start=5) # focus on germany  
   
# create 4 choropleth maps  
m.choropleth(geo_data=state_geo, name='Index Delta 2017-2013', data=df, columns=["District No.","deltaindex"],  
 key_on='feature.properties.WKR_NR', fill_color='PuOr', fill_opacity=0.8,  
 line_opacity=0.3, legend_name='Index Delta 2017-2013', highlight=True)  
  
m.choropleth(geo_data=state_geo, name='Voting Index 2017', data=df, columns=["District No.","selfindex17"],  
 key_on='feature.properties.WKR_NR', fill_color='PuOr', fill_opacity=0.8,  
 line_opacity=0.3, legend_name='Voting Index 2017', highlight=True)  
  
m.choropleth(geo_data=state_geo, name='Voting Index 2013', data=df, columns=["District No.","selfindex13"],  
 key_on='feature.properties.WKR_NR', fill_color='PuOr', fill_opacity=0.8,  
 line_opacity=0.3, legend_name='Voting Index 2013', highlight=True)  
  
m.choropleth(geo_data=state_geo, name='AfD Votes 2017', data=df, columns=["District No.","afd17"],  
 key_on='feature.properties.WKR_NR', fill_color='YlOrBr', fill_opacity=0.8,  
 line_opacity=0.3, legend_name='AfD Votes 2017 in %', highlight=True)  
  
folium.LayerControl().add_to(m) # useful for multiple layers  
  
# Save to html in your directory  
m.save("C:/Users/Dome/Desktop/nu/Karten/t1.html")  
# open html file in your standard browser  
webbrowser.open("C:/Users/Dome/Desktop/nu/Karten/t1.html")