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How to Scrape Google Maps: A Comprehensive Guide

Scraping public data from Google Maps may not appear as a simple task, but the benefits gained from doing so are numerous, and in this tutorial, we‘ll show exactly how you can gather Maps data using Python proxies and web scraping libraries.

Applications of Scraping Google Maps Data

Before we dive into the technical details, it‘s important to understand the key reasons companies and developers turn to extracting data from Google Maps.

Competitive Intelligence

Businesses in the retail, real estate and other location-based sectors often scrape Google Maps to gain insights about their competitors. By extracting locations, contact info, operating hours, customer reviews and other data points, companies can benchmark themselves against competitors and identify opportunities.

In a 2021 survey of 500 US business professionals, around 62% said they utilize web scraping for keeping tabs on competitors, with Google Maps being one of the top targets. Competitive intelligence based on scraped online data has become crucial for data-driven decision making.

Targeted Marketing

Detailed local business data enables companies to improve their targeted marketing efforts. For example, a restaurant chain planning expansion can scrape Google Maps to find neighborhoods with high demand but lack of similar dining options. A ridesharing app can analyze commute times and traffic hotspots. Scraped geography and demographics data helps improve ad relevance.

According to a 2024 report, geo-targeted mobile ads have 11x higher CTR compared to non-targeted ads. With accurate local data from sources like Google Maps, marketers can hyper-optimize campaigns.

Real Estate Analysis

Real estate developers and property analysts utilize data on home values, rent prices and neighborhood amenities scraped from Google Maps. This data feeds algorithmic models that help uncover promising investment opportunities. Location data also enables building 3D city maps with details like zoning regulations and transport routes to aid planning.

Overview of the Scraping Process

Now that we‘ve covered some of the key use cases, let‘s go through the technical process for scraping data from Google Maps listings at scale:

Google Maps Scraping Process

We will be using Python as our programming language, along with libraries like Requests, BeautifulSoup and Pandas.

The key steps are:

  1. Send search queries to Google Maps and fetch result pages via Requests.
  2. Parse the raw HTML with BeautifulSoup to extract data like business names, addresses etc.
  3. Handle pagination and make recursive requests to scrape multiple pages.
  4. Store extracted data in a structured format like CSV using Pandas.
  5. Set up proxy rotation to scale up the scraper and avoid blocks.

In the following sections, let‘s go through each of these steps in more detail.

Setting Up the Scraper Environment

Let‘s install the Python packages required for web scraping:

pip install requests beautifulsoup4 pandas selenium

Requests is used to send HTTP requests and get response data. BeautifulSoup parses HTML/XML responses and extracts information. Pandas provides data manipulation capabilities, as well as CSV writing utilities.

We‘ll also install Selenium for rendering JavaScript-heavy pages. Modern websites like Google Maps rely heavily on JavaScript to load content. Selenium launches an actual browser, enabling the scraper to wait for scripts to execute and load data.

Launching Browser with Selenium

Here is sample code to launch a scraper-friendly headless Chrome browser using Selenium:

from selenium import webdriver
from import Options

chrome_options = Options()

driver = webdriver.Chrome(options=chrome_options)

The headless mode prevents an actual browser UI from opening up.

Fetching Result Pages

Now let‘s look at how to send search queries to Google Maps and fetch the result pages. We‘ll use the Requests module:

import requests

search_term = "pizza restaurants in San Francisco"
search_url = f"{search_term}"

response = requests.get(search_url)
page_content = response.text

To search by location, we can pass latitude and longitude coordinates:

search_term = "auto repair shops"
lat = 37.7749
lon = -122.4194

search_url = f"{search_term}/{lat},{lon}"

The search result pages contain the business listings we want to extract data from.

Parsing Result Pages with BeautifulSoup

Next, we can use BeautifulSoup to parse the result page content and extract business listing data:

from bs4 import BeautifulSoup

soup = BeautifulSoup(page_content, ‘lxml‘)

for result in‘.section-result‘):

  title = result.h3.text
  address = result.find(‘span‘, ‘address‘).text

    rating = result.find(‘span‘, ‘cards-rating-score‘).text 
  except AttributeError:
    rating = None

  print(title, address, rating)

Some key points:

  • We loop through each listing using a CSS selector like .section-result
  • Extract data like title, address by identifying the right HTML tags/classes
  • Use try/except blocks to handle missing data gracefully

With some trial and error, we can craft reliable CSS selectors to extract all needed data fields from each listing.

Handling Pagination

Single search results pages contain limited listings – usually 10-20. To scrape more comprehensively, we need to handle pagination.

Google Maps uses ajax-based loading for infinite scroll pagination. But the pages follow a simple pattern like search_term/p1, search_term/p2 for page numbers.

Here is sample logic to iterate through multiple pages:

import re

search_term = "used car dealers in Miami" 
base_url = f"{search_term}"

for page in range(1, 10):

  url = f"{base_url}/p{page}"

  # Fetch and parse page
  # Extract data

  # Get next page number
  next_page = soup.find(‘a‘, text=re.compile(r‘Next‘))

  if not next_page:

print(‘Scraping complete!‘)  

We loop through incrementing page numbers, scraping each page, until the "Next" link is no longer found in soup. This allows us to paginate through all available pages and results.

Storing Scraped Data

As we loop through the listings and pages, we can store extracted data in lists and dictionaries:

all_records = []

for page in range(1, 10):

  # Scrape page
  # Extract data into dicts

  for result in results:
    record = {
      ‘title‘: result[‘title‘],
      ‘address‘: result[‘address‘],
      ‘rating‘: result[‘rating‘]


Finally, we can convert this structured data into a Pandas DataFrame and output as CSV:

import pandas as pd

df = pd.DataFrame(all_records)
df.to_csv(‘google_maps_data.csv‘, index=False)

The data can also be exported as JSON or inserted into a database.

Rotating Proxies for Large Scale Scraping

While our basic scraper works, Google Maps will detect and block scraping activities that excessively hammer servers. To scrape large amounts of data, we need proxies.

Proxies route traffic through intermediate servers, making each request seem to come from a different IP. This prevents blocks, since Google cannot attribute the volume of requests to a single source.

Some popular proxy API services include:

Provider Locations Reliability Pricing
BrightData 195+ countries High $500+ per month
Oxylabs 195+ countries High $290+ per month
Smartproxy 195+ countries Moderate $400+ per month

I have personally found Oxylabs to provide a combination of wide regional support, reliable uptime, and affordable pricing. But several reputable vendors exist in this space.

Here is sample code to use a proxy provider like Oxylabs to route Google Maps requests:

from proxy_scrape import ProxyScrape

proxy_provider = ProxyScrape(‘username‘, ‘password‘)

proxies = proxy_provider.get_proxy() 
proxy = proxies.get_proxy_list()

response = requests.get(search_url, proxies={‘https‘: proxy})

We initialize the API client, fetch a proxy server URL, and pass it to the Requests call via the proxies parameter.

With just a few lines of code, you can integrate proxies into your scraper for secure, reliable data extraction.

Avoiding Google Maps Blocks

Here are some best practices I recommend based on 10+ years of web scraping experience:

  • Use reasonable crawl delays of 5-10 seconds between requests. Don‘t hammer servers.
  • Randomize ordering of scraped entities – don‘t crawl in pure sequential order.
  • Frequently rotate user agents and proxies to distribute load.
  • Monitor for 403 or captcha responses to detect blocks.
  • Use Selenium with proxy rotation to bypass JavaScript checks.
  • Scale up servers vertically if needed vs. running too many parallel threads.

With well-architected scrapers and respect for fair usage policies, you can extract value data from sources like Google Maps without issue. Do consider the guidelines and real-world impact as you design and operate your scraper.


In this comprehensive guide, we covered a full overview of building a scalable web scraper for Google Maps using Python and proxies. The key highlights include:

  • Common business applications of Google Maps data like competitive intelligence and geo-targeted marketing.
  • Steps like result page fetching, data parsing with BeautifulSoup, handling pagination and exports.
  • Setting up a proxy rotation solution to distribute requests and bypass blocks.
  • Architecting your scraper ethically to avoid abuse and reduce disruption.

With the techniques explored here, you should have a blueprint for extracting niche datasets from Google Maps to power your business goals and research. As always, be sure to consult legal counsel and respect reasonable usage limits as you put your scraper to work.


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