Geocoded markers with Geopy and Folium

In a previous post I have already demonstrated how to use Nominatim in Python (using the Geopy module) to geocode a location name into longitude and latitude coordinates.

In this post I want to show how one can geocode a list of locations using Geopy.

For this I will start by using the Pandas module for reading in a simple and brief csv-file containing location names as specified by entries in a country, city and street column:

# importing pandas
import pandas
# read in csv file containing location data
data = pandas.read_csv("locations.csv")
# display table from csv file
0GermanyBerlinAlexanderplatz 110
1GermanyBerlinDircksenstrasse 25
2GermanyBerlinRathausstrasse 116

Let’s check the datatype of the tabular data:


Now that I have read in the data I will geocode locations and assign geocoded coordiantes to a new column. Since the data is a pandas DataFrame I can make use of the apply() methode to apply the relevant Nominatim geocoding service to every address in the dataframe.

First I need to converge all column entries into addresses, adding those into a new column in the tabular DataFrame. Then, I can create a service object referencing the Nominatim Geopy service and apply that service to every location, returning the geocoded result into an additional new column:

# merging country, city and street into a single address string
data["addresses"] = data["country"] + ", " + data["city"] + ", " + data["street "]
# import the geopy module
import geopy
# create a service object
service = geopy.Nominatim(user_agent = "myGeocoder")
# geocode every address, using .apply() methode for pandas DataFrame
from geopy.extra.rate_limiter import RateLimiter
data["coordinates"] = data["addresses"].apply(RateLimiter(service.geocode,min_delay_seconds=1))
# display tabular data
0GermanyBerlinAlexanderplatz 110Germany, Berlin, Alexanderplatz 1(Alexanderstraße, Spandauer Vorstadt, Mitte, B…
1GermanyBerlinDircksenstrasse 25Germany, Berlin, Dircksenstrasse 2(2, Dircksenstraße, Luisenstadt, Mitte, Berlin…
2GermanyBerlinRathausstrasse 116Germany, Berlin, Rathausstrasse 1(1-14, Rathausstraße, Spandauer Vorstadt, Mitt…

Let us check the data type of the “coodinate” column entries:


The geocoded locations are of type Geopy Location. Objects of the Location class posses various attributes. One of them being latitude and another one being longitude:


I now calculate the “mean” longitude and latitude scores. I want to use them as center point of my Folium location marker map plot:

# extracting longitude and latitude values to separate lists
longs = [coord.longitude for coord in data["coordinates"]]
lats = [coord.latitude for coord in data["coordinates"]]
# calculating mean longitude and latitude values
import statistics
meanLong = statistics.mean(longs)
meanLat = statistics.mean(lats)
# display result
print("meanLong = " + str(meanLong) + "; meanLat = " + str(meanLat))
meanLong = 13.412910038576356; meanLat = 52.52100943333333
[[13.4144809, 13.4136431, 13.410606115729072],
 [52.5228654, 52.5208149, 52.519348]]

Using the Folium module I can now create markers for the locations and plot them on map tiles:

# import folium
import folium
# create a base map centered around Berlin
mapObj = folium.Map(location = [meanLat,meanLong], zoom_start = 15)
# create marker object for Berlin, one by one for every location in data DataFrame
for i in range(0,data.shape[0]): # .shape[0] for Pandas DataFrame is the number of rows
    # create marker for location i 
    markerObj = folium.Marker(location = [lats[i],longs[i]])
    # add marker to map
# display map

Leave a Reply

Leave a Reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.