How to scrape data from google maps using Python?

Google Maps is a powerful tool that provides a vast amount of geospatial data, including locations, addresses, reviews, ratings, and more. Being able to extract this data programmatically can be immensely useful for various applications such as business analysis, research, and data-driven decision-making. In this article, we will explore how to scrape data from Google Maps using Python.

Step 1: Install Required Libraries

To begin with, we need to install the necessary Python libraries that will facilitate the web scraping process. Open your command prompt or terminal and run the following commands:

pip install requests
pip install beautifulsoup4

The requests library will help us send HTTP requests, while beautifulsoup4 will help in parsing the HTML content of the web page.

Step 2: Find the Target URL

To scrape data from Google Maps, we need to determine the specific URL that contains the desired data. For example, let's say we want to extract information about restaurants in a particular area. We can perform a search on Google Maps and then copy the URL from the address bar. The URL will typically look like this:

https://www.google.com/maps/search/restaurants+in+<location>

Replace <location> with the desired city or area where you want to search for restaurants.

Step 3: Send HTTP Request and Retrieve HTML Content

Using the requests library, we can send an HTTP GET request to the target URL and retrieve the HTML content of the page.

import requests

url = "https://www.google.com/maps/search/restaurants+in+<location>"
response = requests.get(url)
html_content = response.text

Step 4: Parse HTML Content with BeautifulSoup

Once we have obtained the HTML content, we can use the beautifulsoup4 library to parse it and extract the desired data. BeautifulSoup provides a convenient way to navigate and search through HTML elements.

Example

In the below example, the find_all() method is used to locate all the <div> elements with the class "section-result-details-container". Within each restaurant's container, we can find the name and address by navigating through the HTML structure using find().

import requests
from bs4 import BeautifulSoup

url = "https://www.google.com/maps/search/restaurants+in+<location>"
response = requests.get(url)
html_content = response.text

soup = BeautifulSoup(html_content, "html.parser")

restaurants = soup.find_all("div", class_="section-result-details-container")

for restaurant in restaurants:
    name = restaurant.find("h3", class_="section-result-title").text.strip()
    address = restaurant.find("span", class_="section-result-location").text.strip()
    
    print("Name:", name)
    print("Address:", address)
    print("-" * 50)

Output

Café café varanasi
Assi crossing
--------------------------------------------------

Continental Cafe & Cuisine
C- 21/3 1st Floor, Continental Cafe (HHI Campus
--------------------------------------------------

Terracotta Cafe
B1/146 Assi -Pushkar Talab Road Assi Road, Pushkar Talab Rd
--------------------------------------------------

Little Cafe
J7/2-K-7-1
--------------------------------------------------
…

Step 5: Extract Additional Information

In addition to the name and address, Google Maps provides many other information, such as ratings, reviews, opening hours, and more. We can extract these details by exploring the HTML structure and adapting our code accordingly.

For instance, to extract the rating of each restaurant, we can modify the previous code as follows:

Example

from bs4 import BeautifulSoup

soup = BeautifulSoup(html_content, "html.parser")

restaurants = soup.find_all("div", class_="section-result-details-container")

for restaurant in restaurants:
    # Code for name and address extraction

    rating_element = restaurant.find("span", class_="cards-rating-score")
    rating = rating_element.text.strip() if rating_element else "N/A"
    
    print("Name:", name)
    print("Address:", address)
    print("Rating:", rating)
    print("-" * 50)

Output

Café café varanasi
Assi crossing
4.6
--------------------------------------------------
Continental Cafe & Cuisine
C- 21/3 1st Floor, Continental Cafe (HHI Campus
4.9
--------------------------------------------------
Terracotta Cafe
B1/146 Assi -Pushkar Talab Road Assi Road, Pushkar Talab Rd
4.5
--------------------------------------------------
Little Cafe
J7/2-K-7-1
4.6
--------------------------------------------------
…

Step 6: Handling Pagination

When scraping large amounts of data, it is common to encounter multiple pages of search results. Google Maps utilizes pagination to navigate between these pages. To scrape data from multiple pages, we need to identify and follow the URLs for the subsequent pages.

We locate the next page button using its class and extract the URL from the parent element. We then repeat the process of sending an HTTP request, parsing the HTML, and extracting the data for each subsequent page until there are no more pages available.

Conclusion

In this article, we have covered the step-by-step process of scraping data from Google Maps, including sending HTTP requests, parsing HTML content with BeautifulSoup, extracting information, and handling pagination. Also, be aware that the structure of Google Maps' web pages may change over time, requiring adjustments to the scraping code. Regularly updating and adapting your code will help ensure its continued functionality.