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How to Use the Google Maps API in Python: A Quick Guide

The Google Maps API opens up a world of powerful geospatial capabilities through a simple RESTful interface. With just a few lines of code, you can geocode addresses, calculate distances, display custom maps and much more.

In this comprehensive guide, you‘ll learn how to get started with the Google Maps API using Python. We‘ll cover:

  • Getting an API key
  • Geocoding addresses and fetching coordinates
  • Calculating distances and travel times
  • Overcoming API limits with web scraping
  • Scraping Google Maps data with Python

And plenty more! Let‘s dive in.

Introduction to the Google Maps API

The Google Maps API is a web service that provides access to Google‘s powerful mapping platform through a JSON/XML interface. It allows you to embed Google Maps on webpages, customize map styles, markers, polygons and overlays.

But the API also enables some incredibly useful geospatial capabilities like:

  • Geocoding – Converting street addresses to geographic coordinates
  • Reverse Geocoding – Converting geographic coords to closest address
  • Distance Matrix – Calculating travel distance and time between places
  • Directions – Getting point-to-point directions and routes
  • Places – Searching for and getting details of local places and businesses
  • Maps Static – Generating static map images

With over 15 APIs and 100+ capabilities, the scope is incredible. And the Google Maps API pricing is highly affordable – many capabilities are free up to usage limits.

That‘s where the Python integration comes in handy. You can access all these APIs through simple Python code, automate workflows, and build powerful geospatial applications.

Let‘s start by getting set up.

Getting a Google Maps API Key

To use any Google Maps API, you first need to get an API key which identifies your project. Here‘s how:

  1. Go to the Google Cloud Console and log in with your Google account.

  2. Click the Navigation Menu and select APIs & Services > Credentials.

  3. On the Credentials page, click Create Credentials, then select API key.

  4. Copy this new API key and save it somewhere safe. We‘ll be using it soon!

With the key, you can start enabling APIs you want to use. For this guide, we‘ll focus on two essential ones:

  • Geocoding API
  • Distance Matrix API

On the Credentials page, click Enable APIs and services at the top. Search for those APIs and enable them for your project.

Now we‘re ready to start using the APIs in Python.

Geocoding Addresses in Python

One of the most useful applications of the Google Maps API is converting street addresses to geographic coordinates (latitude and longitude). This process is called geocoding.

The Geocoding API lets you pass any address and get back its location coordinates. Here‘s a Python function to do just that:

import requests

def geocode_address(api_key, address):

  url = ‘‘

  params = {
    ‘address‘: address,
    ‘key‘: api_key

  response = requests.get(url, params=params)

  if response.status_code == 200:
    data = response.json()

    if data[‘status‘] == ‘OK‘:
      location = data[‘results‘][0][‘geometry‘][‘location‘]
      lat = location[‘lat‘]
      lng = location[‘lng‘]
      return lat, lng

      print(f"Error: {data[‘error_message‘]}")
      return None, None

    print(‘Request failed.‘)
    return None, None

This makes a request to the Geocoding API, passing the address as a parameter. If successful, it extracts the latitude and longitude coordinates from the response.

Let‘s test it out:

api_key = ‘YOUR_API_KEY‘ 

address = ‘1600 Amphitheatre Parkway Mountain View, CA 94043‘

lat, lng = geocode_address(api_key, address)

print(lat, lng)

This should print the coordinates 37.4224764, -122.0842499.

We now have a simple way to convert addresses to geographic coordinates in Python using the Google Maps Geocoding API!

Some use cases for geocoding:

  • Plot locations on a map from a list of addresses
  • Analyze geographic distribution of customers
  • Tag database records with lat/lng for proximity searches

But what if we want to go the other way – convert geographic coordinates to addresses?

Reverse Geocoding in Python

Reverse geocoding lets you fetch the closest address to a given lat/lng coordinate.

The Geocoding API handles this with the latlng parameter instead of address:

import requests

def reverse_geocode(api_key, lat, lng):

  url = ‘‘

  params = {
    ‘latlng‘: f‘{lat},{lng}‘,
    ‘key‘: api_key  

  response = requests.get(url, params=params)

  if response.status_code == 200:
    data = response.json()

    if data[‘status‘] == ‘OK‘:
      return data[‘results‘][0][‘formatted_address‘]

      print(f"Error: {data[‘error_message‘]}")
      return None

    print(‘Request failed.‘)  
    return None

Let‘s test it:

lat = 37.4224764
lng = -122.0842499

address = reverse_geocode(api_key, lat, lng)


This prints "1600 Amphitheatre Pkwy, Mountain View, CA 94043, USA".

Reverse geocoding is useful for plotting addresses on a map from latitude/longitude data.

Up next, let‘s see how to calculate distances between places with the Google Maps API.

Calculating Distance and Travel Time in Python

The Distance Matrix API provides travel distance and time for a matrix of origins and destinations.

You can pass a set of origins and destinations, and it will return the distance and duration between each origin-destination pair.

Here‘s a sample request:

import requests 

origins = ‘Vancouver+BC|Seattle‘
destinations = ‘San+Francisco|Victoria+BC‘

url = ‘‘

params = {
  ‘origins‘: origins,
  ‘destinations‘: destinations,
  ‘key‘: api_key

response = requests.get(url, params=params)
data = response.json()

This calculates the distance and time from Vancouver and Seattle to San Francisco and Victoria.

The rows contain the results for each origin point, and elements has the results for each destination.

Some usages of the Distance Matrix API:

  • Calculate drive time radius for a store location
  • Estimate order delivery times
  • Optimize routes for a fleet of drivers

This demonstrates just a fraction of what you can do with the Google Maps API. Let‘s look at overcoming some limitations next.

Overcoming Google Maps API Limits with Web Scraping

The Google Maps API provides excellent value with generous free limits. But they‘re still rate and usage limits.

Once you exceed them, your requests start getting throttled or blocked. For high-traffic apps, you may need to purchase premium plans.

An alternative is to scrape Google Maps data directly from browsers using Python libraries like Selenium, Beautiful Soup etc.

This avoids API limits completely since you‘re extracting data from the front-end rather than using the back-end APIs.

Scraping Google Maps gives you flexibility to extract any data you want:

  • Places names, photos, reviews, attributes etc.
  • Directions and routes
  • Traffic estimates and popular times
  • Street View imagery

Let‘s take a quick look at scraping some Google Maps data with Python.

Scraping Google Maps with Python and BeautifulSoup

To demonstrate web scraping Google Maps, we‘ll extract some restaurant names and reviews.

First we‘ll search for "restaurants in Vancouver" on Google Maps. Then scrape the place name, rating and sample reviews on the first page.

Our script will:

  1. Use Selenium to load the page
  2. Switch to BeautifulSoup to parse the HTML
  3. Find the place elements using CSS selectors
  4. Extract the name, rating and reviews

Here‘s the full code:

from selenium import webdriver
from bs4 import BeautifulSoup
import time

driver = webdriver.Chrome()

url = ‘‘

soup = BeautifulSoup(driver.page_source, ‘html.parser‘)

places =‘div.section-result-content‘)

data = []
for place in places:

  name = place.select_one(‘h3.section-result-title‘).text

  rating = place.select_one(‘span.section-result-rating‘).text

  reviews = [review.text for review in‘span.section-review-text‘)]

    ‘name‘: name,
    ‘rating‘: rating, 
    ‘sample_reviews‘: reviews[:2]



This will extract data like:

    ‘name‘: ‘The Flying Pig‘,
    ‘rating‘: ‘4.6‘,
    ‘sample_reviews‘: [‘Amazing food!‘, ‘Staff are so friendly.‘]
    ‘name‘: ‘The Eatery‘,
    ‘rating‘: ‘4.5‘,
    ‘sample_reviews‘: [‘Delicious!‘, ‘Great prices.‘]

The key advantage – no API limits! You can extract any data at scale.

Some tips for responsible web scraping:

  • Add random delays to avoid overloading servers
  • Identify as a normal browser, not a bot
  • Respect robots.txt rules
  • Use proxy rotation to avoid IP blocks

Check out our detailed guide for many more Google Maps scraping tips!

Key Takeaways

The Google Maps API enables powerful geospatial capabilities through a simple interface:

  • Geocode addresses and reverse geocode coordinates
  • Calculate distances and travel times
  • Search and detail places, directions and more

It integrates easily with Python using the requests module. But usage limits can pose challenges for larger workloads.

Web scraping provides a scalable alternative to extract geospatial data from Google Maps without limits.

Python libraries like Selenium and BeautifulSoup make it easy to scrape places, reviews, directions, traffic stats, Street View and more.

Hopefully this gives you a solid foundation for unlocking the full potential of Google Maps data using the API or scraping – the possibilities are endless!

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