How to Scrape Google Maps With Python

Here, I’ll show you how to do it step by step, keeping it simple and easy to follow. Let’s dive in!

What is Google Maps Scraping?

A Google Maps scraper is a script or tool designed to automate the retrieval of information from Google Maps. The data extracted can serve various purposes, including:

• Market Research: Analyzing competitor information or exploring market trends.
• Lead Generation: Collecting contact details for outreach campaigns.
• Business Analytics: Gaining insights into customer feedback through reviews and ratings.

Scraping Google Maps, however, is more complex. The dynamic and interactive platform requires browser automation to access and extract data reliably.

What Data Can Be Extracted?

Here’s a list of the primary data fields you can scrape:

• Business Name: Identifies the organization or entity.
• Address: Physical location of the business.
• Phone Number: Contact details for customer inquiries.
• Website URL: Link to the business’s website.
• Business Hours: Opening and closing times.
• Ratings and Reviews: Average ratings and individual feedback.
• Images: Photos associated with the business.
• Tags and Categories: Additional descriptors include cuisine type or services offered.

Alternatives to Manual Scraping

Before we proceed to the scraping guide, I want to introduce some solutions that could be beneficial for you. The following services can help you scrape Google Maps at scale, some offer a free trial too:

1. Bright Data — Best overall for advanced scraping; features extensive proxy management and reliable APIs.
2. Octoparse — User-friendly no-code tool for automated data extraction from websites.
3. ScrapingBee — Developer-oriented API that handles proxies, browsers, and CAPTCHAs efficiently.
4. Scrapy — Open-source Python framework ideal for data crawling and scraping tasks.
5. ScraperAPI — Handles tough scrapes with advanced anti-bot technologies; great for developers.
6. Apify — Versatile platform offering ready-made scrapers and robust scraping capabilities.

Step 1: Setting Up Your Environment

Install Python
Ensure Python 3 is installed on your system. You can download it from python.org.

Create a Project Directory

Organize your work by creating a dedicated folder for your project:

mkdir google-maps-scraper
cd google-maps-scraper

Set Up a Virtual Environment

Virtual environments help keep dependencies isolated. Create one using:

python -m venv env
source env/bin/activate # On Windows, use `env\Scripts\activate`

Install Required Libraries

Install Selenium for browser automation:

pip install selenium

Step 2: Configuring Selenium

Selenium is a powerful library that automates browsers. Start by creating a Python script (scraper.py) and configuring Selenium to launch a Chrome browser.

from selenium import webdriver
from selenium.webdriver.chrome.service import Service
from selenium.webdriver.chrome.options import Options
options = Options()
options.add_argument(" - headless") # Run browser in the background
driver = webdriver.Chrome(service=Service(), options=options)
driver.get("https://www.google.com/maps")

This code initializes a headless Chrome browser, allowing it to interact with Google Maps programmatically. Add driver.quit() at the end of your script to ensure the browser closes after execution.

Step 3: Navigating the Google Maps Page

Once connected to Google Maps, you need to handle the GDPR cookie prompt (if applicable) and navigate to your desired search query.

Handle GDPR Prompt

from selenium.webdriver.common.by import By
from selenium.common.exceptions import NoSuchElementException
try:
accept_button = driver.find_element(By.CSS_SELECTOR, "[aria-label='Accept all']")
accept_button.click()
except NoSuchElementException:
print("No GDPR requirements detected")

Submit a Search Query

Use Selenium to fill in the search bar and click the search button:

from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
search_box = WebDriverWait(driver, 5).until(
EC.presence_of_element_located((By.CSS_SELECTOR, "#searchboxinput"))
)
search_box.send_keys("Italian restaurants")
search_button = driver.find_element(By.CSS_SELECTOR, "button[aria-label='Search']")
search_button.click()

Step 4: Extracting Business Data

The search results will display a list of businesses. These elements are dynamic, so we use Selenium’s WebDriverWait to ensure they load before attempting extraction.

business_items = WebDriverWait(driver, 10).until(
EC.presence_of_all_elements_located((By.XPATH, '//div[@role="feed"]//div[contains(@jsaction, "mouseover:pane")]'))
)

For each business, you can extract relevant details such as name, ratings, reviews, and more.

Extract Basic Details

for item in business_items:
name = item.find_element(By.CSS_SELECTOR, "div.fontHeadlineSmall").text
link = item.find_element(By.CSS_SELECTOR, "a[jsaction]").get_attribute("href")
print(f"Business: {name}, Link: {link}")

Extract Reviews and Ratings

import re
reviews_element = item.find_element(By.CSS_SELECTOR, "span[role='img']")
reviews_text = reviews_element.get_attribute("aria-label")
match = re.match(r"(\d+\.\d+) stars (\d+[,]*\d+) Reviews", reviews_text)
if match:
stars = float(match.group(1))
review_count = int(match.group(2).replace(",", ""))
print(f"Stars: {stars}, Reviews: {review_count}")

Extract Additional Information

Gather attributes like address, hours, and price range:

info_div = item.find_element(By.CSS_SELECTOR, ".fontBodyMedium")
spans = info_div.find_elements(By.XPATH, ".//span[not(@*) or @style]")
details = [span.text for span in spans if span.text.strip()]
print("Details:", details)

Step 5: Saving Data to CSV

Organize your scraped data into a structured format and save it to a CSV file.

Prepare Data for Export

data = []
for item in business_items:
# Collect data as shown above and append to a list
data.append({
"name": name,
"link": link,
"stars": stars,
"review_count": review_count,
"details": "; ".join(details),
})

Write to CSV

import csv
with open("business_data.csv", "w", newline="", encoding="utf-8") as file:
writer = csv.DictWriter(file, fieldnames=data[0].keys())
writer.writeheader()
writer.writerows(data)

Overcoming Challenges

Dynamic Content Loading

Google Maps relies heavily on JavaScript, making elements load asynchronously. Always use explicit waits (WebDriverWait) to avoid attempting to interact with elements before they appear.

Anti-Scraping Measures

Google might detect automated activity, resulting in CAPTCHAs or IP blocks. To mitigate this:

• Rotate IPs using proxies.
• Randomize delays between actions to mimic human behavior.
• Use browser profiles to reduce bot detection.

Ethical Considerations

Before scraping, review Google’s terms of service to ensure compliance. Unauthorized scraping may violate legal or ethical standards.

Scaling Up: Using APIs

For large-scale projects, it’s worth exploring APIs like Bright Data or ScrapeHero. These tools simplify the process of extracting data from Google Maps. They come with built-in features like IP rotation and anti-bot protection, saving you time and effort. You don’t need to worry about managing technical challenges — they handle it all for you. This makes them a great choice for businesses or researchers needing large amounts of data quickly. While these services aren’t free, they can be a worthwhile investment for large-scale needs. Always ensure your data collection aligns with ethical practices and any applicable legal guidelines.

Conclusion

Scraping Google Maps with Python is a practical way to automate data collection for research or business needs. By combining Selenium with Python’s robust libraries, you can build a scraper capable of extracting valuable information. Remember to stay ethical and explore scaling options for larger projects.