Simple script to paint in an interactive map a set of addreses from a csv file.

interactive_map_from_addresses.py

# !pip install requests urllib3 geopy pandas folium
# partially taken from:
# https://towardsdatascience.com/pythons-geocoding-convert-a-list-of-addresses-into-a-map-f522ef513fd6\

import requests
import json
import urllib
from geopy.geocoders import Nominatim, GoogleV3
import pandas as pd
import folium
from folium.plugins import MarkerCluster
from pathlib import Path

# MapQuest is also avialable in geocoders, it's free.
# Requires signup to get an API key

base_url= "https://maps.googleapis.com/maps/api/geocode/json?"
AUTH_KEY = "<PUT YOUR TOKEN HERE>"
geolocator = GoogleV3(api_key=AUTH_KEY)


# just a test
# parameters = {"address": "Tuscany, Italy", "key": AUTH_KEY}
# print(f"{base_url}{urllib.parse.urlencode(parameters)}")
# r = requests.get(f"{base_url}{urllib.parse.urlencode(parameters)}")
# data = json.loads(r.content)
# data

# data file
data_file = "./oficinas_registro.csv"
# data with geolocalization data
geoloc_processed_file = "./oficinas_registro_geoloc.csv"
# output map file
map_file = "./oficinas_registro_map.html"
popup_field = "DENOMINACION OFICINA DE REGISTRO"
tooltip_field = "DENOMINACION OFICINA DE REGISTRO"


# Preprocess data to get addresses field
def add_address_field(data):
  data["address"] = \
    data[["TIPO VIA","NOMBRE VIA"]]\
      .dropna(axis=1)\
      .astype(str)\
      .agg(" ".join, axis=1) + \
    data[["N? VIA","CP","RESTO DIRECCION","LOCALIDAD","PROVINCIA","ISLA","COMUNIDAD AUTONOMA"]]\
      .dropna(axis=1)\
      .astype(str)\
      .agg(", ".join, axis=1)

  return data


# Add geolocalization data, requires API KEY
def apply_geoloc(data, geolocator):
  data["loc"] = data["address"].apply(geolocator.geocode)
  data["point"] = data["loc"].apply(
    lambda loc: tuple(loc.point) if loc else None
  )
  data[["lat", "lon", "altitude"]] = pd.DataFrame(
    data["point"].to_list(), index=data.index
  )
  return data


def load_data(geoloc_processed_file, data_file, geolocator):
  if Path(geoloc_processed_file).exists():
    data = pd.read_csv(geoloc_processed_file)
  else:
    data = add_address_field(pd.read_csv(data_file))
    data = apply_geoloc(data, geolocator)
    data.to_csv(geoloc_processed_file)

  return data


def build_map(data, popup_field, tooltip_field):
  # center to the mean of all points
  m = folium.Map(location=data[["lat", "lon"]].mean().to_list(), zoom_start=2)

  # if the points are too close to each other, cluster them, create a cluster overlay with MarkerCluster
  marker_cluster = MarkerCluster().add_to(m)

  # draw the markers and assign popup and hover texts
  # add the markers the the cluster layers so that they are automatically clustered
  for i,r in data.iterrows():
    location = (r["lat"], r["lon"])
    folium.Marker(
      location=location,
      popup = r[popup_field],
      tooltip=r[tooltip_field]
    ).add_to(marker_cluster)

  return m


data = load_data(geoloc_processed_file, data_file, geolocator)
m = build_map(data, popup_field, tooltip_field)
m.save(map_file)