How to Build Google Maps Scraper Using Python and Selenium in 2024?

This comprehensive guide empowers you to build Google Maps Scraper Using Python and Selenium. Imagine automatically collecting valuable data like business names, addresses, and reviews! This guide breaks down the process step-by-step, providing clear explanations alongside helpful code snippets. You’ll learn how to navigate Google Maps with Selenium, allowing you to target specific locations and search for businesses. The guide will also cover techniques for extracting desired information from the webpages, giving you the tools to build your own scraper and automate data collection from Google Maps. Remember, it’s important to be respectful of Google’s terms of service when scraping data.

Introduction to Web Scraping and Selenium

Web scraping involves extracting data from websites, enabling us to gather information programmatically. Selenium is a powerful tool for automating web browser interactions, making it ideal for web scraping tasks. By combining Python’s versatility with Selenium’s automation capabilities, we can create robust web scrapers for various purposes.

Google Maps Scraper Using Python and Selenium

Let’s dive into the code you provided and explain each section in detail.

Importing required libraries

import csv
from bs4 import BeautifulSoup
from selenium import webdriver
from import Service
from import ChromeDriverManager
from selenium.common.exceptions import WebDriverException
from urllib.parse import urlparse, parse_qs
import re

Here, we import necessary libraries including csv for CSV file handling, BeautifulSoup for HTML parsing, and Selenium for web automation.

Initialize the ChromeDriver using ChromeDriverManager

service = Service(ChromeDriverManager().install())
url = ""

We initialize the ChromeDriver using ChromeDriverManager, a convenient tool for managing WebDriver executables. Then, we specify the URL of Google Maps.

chrome_options = webdriver.ChromeOptions()
driver = webdriver.Chrome(service=service, options=chrome_options)

# Manually enter credentials in the browser
input("Please enter your credentials manually. Then press Enter to start scraping...")

In this section, we configure Chrome options and initialize the WebDriver. We prompt the user to enter their credentials manually, as Google Maps may require authentication.

Find all tags with href containing

def parse_url(url):
latitude = longitude = place_id = ''
# Extract latitude, longitude, and place ID using regex
latitude_match ='3d([-.\d]+)', url)
longitude_match ='4d([-.\d]+)', url)
place_id_match ='19s([^!?]+)', url)

# Parsing latitude, longitude, and place ID
if latitude_match:
    latitude =
if longitude_match:
    longitude =
if place_id_match:
    place_id =

return latitude, longitude, place_id

This function parse_url extracts latitude, longitude, and place ID from Google Maps URLs using regular expressions.

Save parsed components to a CSV file

output_csv_file = "urls.csv"
processed_urls = set()
with open(output_csv_file, "w", newline='', encoding='utf-8') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(['URL', 'Latitude', 'Longitude', 'Place ID'])

    while True:
        # Extract the complete HTML soup of the page
        soup = BeautifulSoup(driver.page_source, 'html.parser')

        # Find all <a> tags with href containing ""
        google_maps_links = soup.find_all('a', href=lambda href: href and "" in href)

        # Extract href attributes and clean URLs, removing duplicates
        for a in google_maps_links:
            href = a['href'].strip('"')
            if href not in processed_urls:
                latitude, longitude, place_id = parse_url(href)
                writer.writerow([href, latitude, longitude, place_id])

        # Prompt the user to press Enter to scrape more URLs or 'q' to quit
        user_input = input("Press Enter to scrape more URLs or 'q' to quit...")

        if user_input.lower() == 'q':

except WebDriverException as e:
    print(f"WebDriverException occurred: {e}")

except Exception as e:
    print(f"Other Exception occurred: {e}")

In this part, we scrape Google Maps URLs, extract the required data, and save it to a CSV file. We handle user input to control the scraping process and gracefully handle exceptions.


we ensure that the WebDriver is closed to release system resources and inform the user that all parsed URLs are saved in the CSV file.


This is the output we get

Conclusion(Google Maps Scraper Using Python and Selenium)

By following this guide and leveraging the provided code, you can create a robust Google Maps Scraper Using Python and Selenium. This scraper enables you to effortlessly retrieve location-based data from Google Maps for various applications, including business analytics, research, and location-based services.

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