choropleth_map.py

#group the starbucks dataframe by zip code and count the number of stores in each zip code
numStoresSeries = df.groupby('zip').count().id
#initialize an empty dataframe to store this new data
numStoresByZip = pd.DataFrame()
#populate the new dataframe with a 'zipcode' column and a 'numStores' column
numStoresByZip['zipcode'] = [str(i) for i in numStoresSeries.index]
numStoresByZip['numStores'] = numStoresSeries.values

#initialize the LA County map
laMap = folium.Map(location=[34.0522,-118.2437], tiles='Stamen Toner', zoom_start=9)

#draw the choropleth map. These are the key components:
#--geo_path: the geojson which you want to draw on the map [in our case it is the zipcodes in LA County]

#--data: the pandas dataframe which contains the zipcode information 
# AND the values of the variable you want to plot on the choropleth

#--columns: the columns from the dataframe that you want to use 
#[this should include a geospatial column [zipcode] and a variable [numStores]

#--key_on: the common key between one of your columns and an attribute in the geojson. 
#This is how python knows which dataframe row matches up to which zipcode in the geojson

laMap.choropleth(geo_path='laZips.geojson', data=numStoresByZip, columns=['zipcode', 'numStores'], \
                 key_on='feature.properties.zipcode', fill_color='YlGn', fill_opacity=1)

laMap.save('laChoropleth.html')