How to get data from Google Maps using Python

Have you ever wonder how to take advantage of Google Maps massive data? Since Google made available its API, this task has never been so easy. In this post, I will show you how to easily get the API key (for Geocoding and Places API) and query the information from Python and thus, be able to analyze it there.

Get the API Key

First, you have to get the API key:

1. Visit the Google Cloud Platform Console.
2. Click the project drop-down and select or create the project for which you want to add an API key.
3. Click on Google Maps > APIs.
4. Click on Places API > Enable.
5. Click on Geocoding API > Enable.
6. Click the menu button and select Credentials, and click Credentials in APIs & Services.
7. On the Credentials page, click Create credentials > API key. The API key created dialog display your newly created API key.
8. The new API key is listed on the Credentials page under API Keys.

Install the library googlemaps in python

1. Install the library:
   1. pip install googlemaps
2. Follow the next code and add your API Key on Activate Key:

# libraries
import googlemaps
from datetime import datetime
import pandas as pd

# API Key
gmaps = googlemaps.Client(key='Activate Key')

Since API uses the scheme “Pay-as-you-go pricing”, please ensure you understand the pricing structure. Also, please ensure to enable the billing of your project.

3. Next, you can search for directions and places using coordinates with this function reverse_geocode. For verification purposes, let’s try this: Times Square (Manhattan, NY 10036, United States, {40.757989, -73.985524}).
4. For knowing what should be specified as parameters, please visit the documentation of Geocoding API.

reverse_geocode_result = gmaps.reverse_geocode((40.757989,-73.985524))

Formated_Address = []
Latitude = []
Longitude = []
Place_ID = []
Types_ = []

for i in range(0,len(reverse_geocode_result)):
    Formated_Address.append(reverse_geocode_result[i]['formatted_address'])
    Latitude.append(reverse_geocode_result[i]['geometry']['location']['lat'])
    Longitude.append(reverse_geocode_result[i]['geometry']['location']['lng'])
    Place_ID.append(reverse_geocode_result[i]['place_id'])
    Types_.append(reverse_geocode_result[i]['types'])

results_Geo = pd.DataFrame(data={'Formated_Address':Formated_Address,
                                'Latitude':Latitude,
                                'Longitude':Longitude,
                                'Place_ID':Place_ID,
                                'Types_':Types_})

results_Geo.to_excel('datos_GEO_location.xlsx')

Results Geocode

Address	Latitude	Longitude	Place ID	Types
Times Square, New York, NY 10036, USA	40.758	-73.9856	ChIJ74T0qVVYwokRgupPU9hTtp0	[‘establishment’, ‘point_of_interest’, ‘transit_station’]
177 W 45th St, New York, NY 10036, USA	40.75792	-73.9852	ChIJz3MIqFVYwokRDQGAtbRCz_Y	[‘street_address’]
Bertelsmann Building, New York, NY 10036, USA	40.75796	-73.9847	ChIJd_cVoVVYwokRq4t_7F117HU	[‘premise’]
1539 Broadway, New York, NY 10036, USA	40.75797	-73.9856	EiYxNTM5IEJyb2Fkd2F5LCBOZXcgWW9yaywgTlkgMTAwMzYsIFVTQSIbEhkKFAoSCYUFmK5VWMKJEftBkwRJbYrKEIMM	[‘street_address’]
7th Ave, New York, NY 10036, USA	40.75815	-73.9854	ChIJowwgqVVYwokR3JwLCfUBRlQ	[‘route’]
Theater District, New York, NY, USA	40.75901	-73.9845	ChIJgzD7uFVYwokRXCoEdvGu-aA	[‘neighborhood’, ‘political’]
New York, NY 10036, USA	40.76026	-73.9933	ChIJByP4k1NYwokR7WYV3pZo1fc	[‘postal_code’]
Midtown Manhattan, New York, NY, USA	40.75493	-73.984	ChIJqXwSpAFZwokR28_WgZDMzb4	[‘neighborhood’, ‘political’]
Manhattan, New York, NY, USA	40.78306	-73.9712	ChIJYeZuBI9YwokRjMDs_IEyCwo	[‘political’, ‘sublocality’, ‘sublocality_level_1’]
New York County, New York, NY, USA	40.78306	-73.9712	ChIJOwE7_GTtwokRFq0uOwLSE9g	[‘administrative_area_level_2’, ‘political’]
New York, NY, USA	40.71278	-74.006	ChIJOwg_06VPwokRYv534QaPC8g	[‘locality’, ‘political’]
New York, USA	43.29943	-74.2179	ChIJqaUj8fBLzEwRZ5UY3sHGz90	[‘administrative_area_level_1’, ‘political’]
United States	37.09024	-95.7129	ChIJCzYy5IS16lQRQrfeQ5K5Oxw	[‘country’, ‘political’]

4. With the code above, you can store the results of the query into a pandas dataframe to have them available for analysis.
5. Now, we use the function places_nearby to look for places near to a specified coordinate.
6. For knowing what should be specified as parameters, please visit the documentation of Places API.

places_nearby_example = gmaps.places_nearby(location = (40.757989, -73.985524), radius = 100, type=['aquarium',
'art_gallery','atm','bakery','bank','bar','beauty_salon','bicycle_store','book_store','bowling_alley','bus_station',
'cafe','campground','car_dealer','car_rental','car_repair','car_wash','casino','cemetery','church','city_hall',
'clothing_store','convenience_store','courthouse','dentist','department_store','doctor','electrician',
'electronics_store','embassy','fire_station','florist','funeral_home','furniture_store','gas_station','gym',
'hair_care','hardware_store','hindu_temple','home_goods_store','hospital','insurance_agency','jewelry_store',
'laundry','lawyer','library','liquor_store','local_government_office','locksmith','lodging','meal_delivery',
'meal_takeaway','mosque','movie_rental','movie_theater','moving_company','museum','night_club','painter',
'park','parking','pet_store','pharmacy','physiotherapist','plumber','police','post_office','real_estate_agency',
'restaurant','roofing_contractor','rv_park','school','shoe_store','shopping_mall','spa','stadium','storage',
'store','subway_station','supermarket','synagogue','taxi_stand','train_station','transit_station','travel_agency',
'veterinary_care','zoo'])

res = len(places_nearby_example['results'])

results = places_nearby_example['results']

lat_ = []
lng_ = []
name_ = []
place_id_ = []
types_ = []
vicinity_ = []

for rr_ in range(0,res):
    lat = results[rr_]['geometry']['location']['lat']
    lng = results[rr_]['geometry']['location']['lng'] 
    name = results[rr_]['name']
    place_id = results[rr_]['place_id']
    types = results[rr_]['types']
    vicinity = results[rr_]['vicinity']

    lat_.append(lat)
    lng_.append(lng)
    name_.append(name)
    place_id_.append(place_id)
    types_.append(types)
    vicinity_.append(vicinity)

results = pd.DataFrame(data={'lat':lat_,
                                'lng':lng_,
                                'name':name_,
                                'place_id':place_id_,
                                'types':types_,
                                'vicinity':vicinity_})

results.to_excel('datos_places_location.xlsx')

Results Places

Latitude	Longitude	Name	Place ID	Types	Vicinity
40.71278	-74.006	New York	ChIJOwg_06VPwokRYv534QaPC8g	[‘locality’, ‘political’]	New York
40.75797	-73.9855	Times Square	ChIJmQJIxlVYwokRLgeuocVOGVU	[‘tourist_attraction’, ‘point_of_interest’, ‘establishment’]	Manhattan
40.75859	-73.9858	New York Marriott Marquis	ChIJiVXoAFVYwokREqPijh-d8xg	[‘lodging’, ‘point_of_interest’, ‘establishment’]	1535 Broadway, New York
40.75794	-73.9851	Planet Hollywood	ChIJf2_0qFVYwokRu7dmhsHUrpo	[‘restaurant’, ‘food’, ‘point_of_interest’, ‘establishment’]	1540 Broadway, New York
40.75797	-73.9859	Minskoff Theatre	ChIJB4cC_1RYwokRu6mRBD_lqO8	[‘point_of_interest’, ‘establishment’]	200 West 45th Street, New York
40.75824	-73.985	Disney Store	ChIJf2_0qFVYwokRcbTtlS60Vqg	[‘point_of_interest’, ‘clothing_store’, ‘store’, ‘establishment’]	1540 Broadway, New York
40.75755	-73.9861	Oakley Store	ChIJC4BM_FRYwokRT7tenPBxEUQ	[‘shoe_store’, ‘point_of_interest’, ‘clothing_store’, ‘store’, ‘establishment’]	1515 Broadway, New York
40.75837	-73.985	MAC Cosmetics	ChIJQSY1plVYwokRIiTnNMzktsE	[‘point_of_interest’, ‘store’, ‘establishment’]	1540 Broadway, New York
40.75792	-73.9852	Buca di Beppo Italian Restaurant	ChIJN3JuqFVYwokRXvoa9Q9wmnM	[‘restaurant’, ‘food’, ‘point_of_interest’, ‘establishment’]	1540 Broadway, New York
40.75788	-73.9862	MTV	ChIJAQES_lRYwokR0HBr86D5FRY	[‘point_of_interest’, ‘establishment’]	1515 Broadway, New York
40.75799	-73.9853	Sunglass Hut	ChIJJaibplVYwokRi6fDp2MjUYg	[‘shopping_mall’, ‘point_of_interest’, ‘store’, ‘establishment’]	1540 Broadway, New York
40.75766	-73.9863	PlayStation Theater	ChIJbX7w8FRYwokR7GPMrwmmgtE	[‘point_of_interest’, ‘establishment’]	1515 Broadway, New York
40.75788	-73.9862	IT2 Treasury Solutions Inc.	ChIJbX7w8FRYwokRMlxCK0A18Zg	[‘point_of_interest’, ‘establishment’]	1515 Broadway, New York
40.75809	-73.9861	Shimon Sandler	ChIJbX7w8FRYwokRKx3XTT7wwoM	[‘point_of_interest’, ‘establishment’]	1515 Broadway #627, New York
40.75779	-73.9862	Design Shoppny	ChIJSc3GAlVYwokRiDnoXdqZp10	[‘point_of_interest’, ‘establishment’]	1515 Broadway, New York
40.75779	-73.9862	TLD Assets	ChIJqWJv-1RYwokRLBeoQUiHzcM	[‘point_of_interest’, ‘establishment’]	1515 Broadway, New York
40.75791	-73.9864	LX Coach Bus Charter Co	ChIJbX7w8FRYwokR6Y-uT_fik7A	[‘point_of_interest’, ‘establishment’]	1515 Broadway, New York
40.75779	-73.9862	Ciplex New York	ChIJqWJv-1RYwokRwwLzQzds5ck	[‘point_of_interest’, ‘establishment’]	1515 Broadway, New York
40.75751	-73.9861	Viacom	ChIJqWJv-1RYwokRpWCF8nL4bUE	[‘point_of_interest’, ‘establishment’]	1515 Broadway 53rd floor, New York
40.75493	-73.984	Midtown Manhattan	ChIJqXwSpAFZwokR28_WgZDMzb4	[‘neighborhood’, ‘political’]	Manhattan

Results (Incomplete) for Place Near Query

Potential Applications

Finally, I would like to highlight the main applications that this analysis could have in businesses:

• Market Research on how well is a place for opening an establishment.
• Scheduling and planning of selling route in case of physical visits.
• Identify potential clients within an interesting area for a defined service or product.

References:

1. https://developers.google.com/places/web-service/get-api-key
2. https://developers.google.com/maps/billing/gmp-billing?hl=es#places-product