Easy Way To Create Google Maps Extractor With Python in 2025

These days it is hard to imagine a successful business without the proper lead generation strategy and while there are a lot of different sources where you can acquire leads, nothing beats the good old Google Maps. In these guide we will explore, why would you need data specifically from Google Maps, which advantages it gives you over your competitors and finally how to build your very own Google Maps extractor using nothing but python.

Why Scrape Google Maps?

Google Maps are considered to be a treasure trove of targeted business leads, which contains an integral data about your potential clients, such as business names, contact details, customer reviews, and geographic locations. Using a reliable way of scarping data from Google Maps gives you a competitive edge, allowing you to see opportunities, which your competitors might miss and adapt your outreach strategy accordingly. Moreover, Google Maps leads are abundant and cost-efficient making it ideal for businesses seeking high-value data on budget.

How To Scrape Data From Google Maps?

There are several ways which can be used to extract data from google maps, but we are going to focus on specifically Python.

Before we start you need to have following things installed on our system:

Python 3.8+
WebDriver - Download ChromeDriver matching your Chrome browser version and add it to your system PATH or project folder
Basic Python Knowledge
Familiarity with HTML structure
Installed libraries using pip:

bash

pip install selenium beautifulsoup4 pandas

Implementing Python Google Maps Extractor

Let's create a new project with the name google_maps_extractor and inside execute the following commands:

bash

python -m venv maps_scraper
source maps_scraper/bin/activate  # Mac/Linux
maps_scraper\Scripts\activate  # Windows

This commands will create a virtual environment and activate it. Great, new we can start building or application. Next create a new python file with the name index.py and post the following code:

python

import time
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from bs4 import BeautifulSoup
import pandas as pd

# Set up Selenium with headless Chrome
options = Options()
options.add_argument('--headless')  # Run without opening browser
options.add_argument('--disable-gpu')
driver = webdriver.Chrome(options=options)

# Navigate to Google Maps
driver.get('https://www.google.com/maps')
time.sleep(2)  # Wait for page to load

# Search for businesses (e.g., hardware shops in New York)
search_query = 'hardware shops near New York, NY'
search_box = driver.find_element(By.ID, 'searchboxinput')
search_box.send_keys(search_query)
search_box.send_keys(Keys.ENTER)
time.sleep(3)  # Wait for results

# Scroll to load more results
for _ in range(3):  # Adjust range for more/less scrolling
    driver.execute_script("document.querySelector('.section-scrollbox').scrollTop += 1000")
    time.sleep(2)

# Get page source and parse with BeautifulSoup
soup = BeautifulSoup(driver.page_source, 'html.parser')
businesses = soup.select('div[role="list"] a')  # Select business links

# Extract data
data = []
for business in businesses:
    name = business.get('aria-label', 'N/A')
    if not name or 'Sign in' in name:
        continue
    link = business.get('href', 'N/A')
    
    # Navigate to business detail page
    driver.get(link)
    time.sleep(2)
    detail_soup = BeautifulSoup(driver.page_source, 'html.parser')
    
    # Extract address and phone (if available)
    address = 'N/A'
    phone = 'N/A'
    details = detail_soup.select('div[class*="fontBodyMedium"]')
    for detail in details:
        text = detail.text
        if 'Address:' in text:
            address = text.replace('Address:', '').strip()
        if 'Phone:' in text:
            phone = text.replace('Phone:', '').strip()
    
    data.append({
        'Business Name': name,
        'Address': address,
        'Phone': phone,
        'URL': link
    })

# Save to CSV
df = pd.DataFrame(data)
df.to_csv('google_maps_leads.csv', index=False, encoding='utf-8')
print("Data saved to google_maps_leads.csv")

# Close the browser
driver.quit()

Wow, that is a lot to take it at once, so let's dive into key implementation details.

python

import time
from selenium import webdriver
from selenium.webdriver.chrome.options import Options
from selenium.webdriver.common.by import By
from selenium.webdriver.common.keys import Keys
from bs4 import BeautifulSoup
import pandas as pd

Imports essential Python libraries for Google Maps scraping: Selenium for browser automation, BeautifulSoup for HTML parsing, pandas for CSV export, and time/Keys for delays and keyboard simulation.

python

options = Options()
options.add_argument('--headless')  # Run without opening browser
options.add_argument('--disable-gpu')
driver = webdriver.Chrome(options=options)

Configures headless Chrome browser for efficient background scraping without opening a visible window.

python

driver.get('https://www.google.com/maps')
time.sleep(2)  # Wait for page to load
search_query = 'hardware shops near New York, NY'
search_box = driver.find_element(By.ID, 'searchboxinput')
search_box.send_keys(search_query)
search_box.send_keys(Keys.ENTER)
time.sleep(3)  # Wait for results

Opens Google Maps, searches for businesses using a specific query, and waits for results to load.

python

for _ in range(3):  # Adjust range for more/less scrolling
    driver.execute_script("document.querySelector('.section-scrollbox').scrollTop += 1000")
    time.sleep(2)

Scrolls the results panel to load additional business listings beyond the initial results.

python

soup = BeautifulSoup(driver.page_source, 'html.parser')
businesses = soup.select('div[role="list"] a')  # Select business links

Parses the page HTML and extracts links to individual business pages.

python

for business in businesses:
    name = business.get('aria-label', 'N/A')
    if not name or 'Sign in' in name:
        continue
    link = business.get('href', 'N/A')
    driver.get(link)
    time.sleep(2)
    detail_soup = BeautifulSoup(driver.page_source, 'html.parser')
    address = 'N/A'
    phone = 'N/A'
    details = detail_soup.select('div[class*="fontBodyMedium"]')
    for detail in details:
        text = detail.text
        if 'Address:' in text:
            address = text.replace('Address:', '').strip()
        if 'Phone:' in text:
            phone = text.replace('Phone:', '').strip()
    data.append({
        'Business Name': name,
        'Address': address,
        'Phone': phone,
        'URL': link
    })

Visits each business page and extracts key data (name, address, phone) for lead generation.

python

df = pd.DataFrame(data)
df.to_csv('google_maps_leads.csv', index=False, encoding='utf-8')
print("Data saved to google_maps_leads.csv")

Converts scraped data into a CSV file for easy analysis and marketing use.

python

driver.quit()

Closes the browser session to free system resources and complete the scraping process.

Finally let's run it and get our results:

bash

python scrape_google_maps.py

Limitations

Although this approach is more than viable for small-scale data gathering, it has several major drawbacks:

Anti-Bot Measures - Google may block frequent requests or trigger CAPTCHAs. The script uses delays, but proxies or APIs may be needed for scale.
Dynamic Selectors - Google Maps’ HTML structure changes, so selectors like div[role="list"] a may need updates.
Limited Data Fields - The script, though effective for basic data, omits fields like reviews or ratings, limiting its utility for advanced lead generation when users explore how to scrape data from Google Maps.

While these limitations can make scaling or maintaining a custom Google Maps extractor challenging, they don’t have to derail your lead generation efforts. By leveraging advanced Google Maps scraping tools or professional solutions, you can bypass hurdles like anti-bot measures and dynamic selectors, streamlining how to scrape data from Google Maps. Fortunately, our Google Maps Scraper provides a powerful, user-friendly solution to automate and enhance your scraping process, delivering comprehensive business data with ease.

With our custom state of the art extraction engine it is fast, cheap and dumb easy to use for anyone.

Let see how you can extract millions of results in the metter of hours in just this 5 simple steps:

Step 1 - Choose your category

Step 2 - Specify your targeted locations

Step 3 - Choose additional filters to tailor the data to your needs

Dashboard Google Maps additional options step

Step 4 - Start your scraper and wait till it will finish gathering info for you

Step 5 - Download your data in three formats and enjoy your new leads

Dashboard Google Maps modal window with different download options

Using our service you do no need to think about all of the complicated implementation details and let the professional do all the work for you.

Final Thoughts

Whether you choose to build and maintain your own scraper or leverage existing tools, the key is understanding your data needs and choosing the approach that best fits your budget, technical expertise, and scale requirements. The foundation you've learned here will serve you well regardless of which path you take. Thanks for reading!