How to Scrape Google Maps Without Python: A Complete Guide to the Best Google Maps Scraper

So you want to extract valuable business data from Google Maps?

Did you know that Python, which is a programming language, can help you to do that?

No God, as a language, Python is really appreciated by beginners and experts alike, and when it comes to web scraping, it comes in handy.

When we talk about web scraping, we mean collecting data in an automated way from a website. In our case, the website is Google Maps, and it's a goldmine of geographical and business data - from small business listings to enterprise-level location information, to be a bit more specific.

The beauty of Python is that there are already thousands of different libraries that can allow you to leverage the work already done by the community. So here are the seven best Python web scraping libraries in 2023.

The 7 Best Python Libraries for Web Scraping Google Maps

Number One: ZenRows - The Anti-Detection Specialist

Time, frustration, and resources - this library helps you to deal with the biggest problem faced by web scrapers: getting blocked. ZenRows can efficiently bypass CAPTCHAs and anti-bots. It can also scrape JavaScript-rendered pages and it works well with other libraries. However, it's not a free service - you have to pay for it.

Example ZenRows code:

import requests

# ZenRows API request
response = requests.get(
    'https://api.zenrows.com/v1/',
    params={
        'url': 'https://www.google.com/maps/search/restaurants+near+me',
        'apikey': 'YOUR_ZENROWS_API_KEY',
        'js_render': 'true',
        'antibot': 'true'
    }
)

print(response.text)

Number Two: Selenium - Dynamic Website Master

Selenium is great for scraping dynamic websites. A dynamic website is a website that changes depending on several factors. Moreover, Selenium is also supported by multiple browsers. However, unlike ZenRows, it can be time-consuming and resource-consuming.

Example Selenium code:

from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC

# Setup Chrome driver
driver = webdriver.Chrome()
driver.get("https://www.google.com/maps/search/restaurants+near+me")

# Wait for results to load
wait = WebDriverWait(driver, 10)
results = wait.until(EC.presence_of_all_elements_located((By.CSS_SELECTOR, "[data-result-index]")))

# Extract business names
for result in results:
    business_name = result.find_element(By.CSS_SELECTOR, "h3").text
    print(business_name)

driver.quit()

Number Three: Requests - The Beginner's Friend

As a fun fact, Requests was the very first library I have used to scrape data using Python. It implies that it's a perfect library for beginners. It's user-friendly, fast, and easily understandable. Nevertheless, it can't scrape interactive or dynamic sites with JavaScript.

Another fun fact is that Requests is often linked with Beautiful Soup. To begin with, we get the main content from the website thanks to the GET method using Requests. So we end up with a URL, which is a website we want to scrape, related to status code telling us whether the website can be scraped or not, and HTML data based on the source code.

Example Requests code:

import requests

# Basic request to Google Maps (limited functionality)
url = "https://www.google.com/maps/search/restaurants+near+me"
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
}

response = requests.get(url, headers=headers)

print(f"Status Code: {response.status_code}")
print(f"Content Length: {len(response.text)}")

# Note: This will return basic HTML without dynamic JavaScript content

Number Four: Beautiful Soup - The HTML Parser

We can now extract any data we want thanks to Beautiful Soup. It can be done using find and find_all methods. The idea is to identify specific data thanks to tags, values, or attributes - anything which can help us to target elements we need.

In other words, Beautiful Soup is powerful for parsing XML or HTML documents, but it has limited support and it does require multiple dependencies.

Example Beautiful Soup code:

import requests
from bs4 import BeautifulSoup

# Get HTML content using Requests
url = "https://www.google.com/maps/search/restaurants+near+me"
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
}

response = requests.get(url, headers=headers)
soup = BeautifulSoup(response.content, 'html.parser')

# Find all links (limited on dynamic Google Maps)
links = soup.find_all('a', href=True)
for link in links[:5]:  # First 5 links
    print(f"Link: {link.get('href')}")
    
# Note: Beautiful Soup works better on static HTML, not dynamic Google Maps

Number Five: Playwright - Cross-Browser Automation

Playwright provides excellent cross-browser automation, but it's resource-intensive and it has a steep learning curve.

Example Playwright code:

from playwright.async_api import async_playwright
import asyncio

async def scrape_google_maps():
    async with async_playwright() as p:
        browser = await p.chromium.launch(headless=False)
        page = await browser.new_page()
        
        # Navigate to Google Maps
        await page.goto("https://www.google.com/maps/search/restaurants+near+me")
        
        # Wait for search results
        await page.wait_for_selector('[data-result-index]')
        
        # Extract business data
        businesses = await page.query_selector_all('[data-result-index]')
        
        for business in businesses[:5]:  # First 5 results
            name = await business.query_selector('h3')
            if name:
                business_name = await name.text_content()
                print(f"Business: {business_name}")
        
        await browser.close()

# Run the async function
asyncio.run(scrape_google_maps())

Number Six: Scrapy - The Professional Framework

According to Ryan Mitchell from the book "Web Scraping with Python," Scrapy is one of the best frameworks for developing crawlers. Indeed, it seems like Scrapy is a framework used for scraping and crawling complex websites. On the other hand, it's not particularly user-friendly and it can't scrape dynamic web pages.

Example Scrapy code:

import scrapy

class GoogleMapsSpider(scrapy.Spider):
    name = 'google_maps'
    start_urls = ['https://www.google.com/maps/search/restaurants+near+me']
    
    def parse(self, response):
        # Extract business links (limited on dynamic content)
        business_links = response.css('a[href*="/maps/place/"]::attr(href)').getall()
        
        for link in business_links[:5]:  # First 5 links
            full_url = response.urljoin(link)
            yield scrapy.Request(
                url=full_url,
                callback=self.parse_business
            )
    
    def parse_business(self, response):
        yield {
            'name': response.css('h1::text').get(),
            'url': response.url,
        }

# To run: scrapy crawl google_maps
# Note: Scrapy has limitations with JavaScript-heavy sites like Google Maps

Number Seven: urllib3 - The Requests Alternative

urllib3 is an alternative to Requests. It's particularly known for its reliability and its performance optimizations. I remember I have tried that before, and to my mind, its syntax is more complicated compared to Requests.

Example urllib3 code:

import urllib3
from urllib.parse import urlencode

# Create a PoolManager instance
http = urllib3.PoolManager()

# Set up headers
headers = {
    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36'
}

# Make request to Google Maps
url = "https://www.google.com/maps/search/restaurants+near+me"
response = http.request('GET', url, headers=headers)

print(f"Status: {response.status}")
print(f"Content Length: {len(response.data)}")

# Decode response data
html_content = response.data.decode('utf-8')
print(f"First 200 characters: {html_content[:200]}")

# Note: urllib3 has more complex syntax but offers more control

The Problem with Traditional Python Scraping

Obviously, each library has its pros and cons, and the choice - your choice - depends on the requirements of your web scraping project. But what if I told you that there is a simpler, more cost-effective, faster, and above all, a no-code solution that allows you to scrape Google Maps in a couple of clicks without any technical experience required?

A solution that handles CAPTCHAs, manages proxies, and delivers a ready-to-use file for any Google Maps data extraction. Even better, imagine that you can get your extraction from an entire country, and we can do that thanks to Scrap.io - the best Google Maps scraper tool available today.

Introducing Scrap.io: The No-Code Solution

If you want to use Scrap.io, you can click the link in the description or you can simply type scrap.io in your search bar. You have access to a demo if you scroll down a little bit, and there is an interesting aspect here. We can notice that we can get email addresses and social media links thanks to Scrap.io.

It implies that Scrap.io doesn't only scrape Google Maps - it also scrapes and crawls the websites of companies in order to look for additional information. For those interested in the complete technical aspects of Google Maps scraping and API usage, there's a comprehensive guide available that covers everything from basic extraction to advanced review mining.

Getting Started with Scrap.io

To begin with, I invite you to create your own account. Once it is done, you have access to your dashboard. You click on it, and this is the most important tab of the software. This is the tab in which you will be able to retrieve all your leads.

You can filter your leads - you can filter them based on an activity, based on your category, but also based on a geographical location.

Categories and Geographic Targeting

What about the categories? Well, you have around 4,000 different categories listed on Google Maps. So if you want to get a full and comprehensive list, we'll leave you a link in the description.

I think we will keep it as it was - meaning as a restaurant - but you can obviously choose another category if you want to. Depending on your plan, you can also target an entire country, meaning that if I click on search right now, I might be able to target all restaurants in France. Let's change the country to United States - it will make a bit more sense.

I can also target a Level 1 division, meaning a particular state. Why is it called Level 1 division? It's simply because the Level 1 division depends on which country you picked up. For example, in France, a Level 1 division is what we call a region. Let's choose Tennessee.

Here we go! This is the same thing for the Level 2 division, which represents the county in our case. Or I can simply target a city - let's say Nashville. I click on search.

Advanced Filtering Capabilities

The funny thing is that I can filter my data even more. If I click on filter, you can see that you've got two kinds of filters: essential and advanced ones.

I might want to target restaurants as a primary category and not as secondary categories. Do I want to get closed restaurants? Maybe not. Restaurants with a website, with a phone number, with an email, with Facebook, Instagram, YouTube, Twitter, LinkedIn links. Does a restaurant claim its listing on Google Maps? What about the price range, the rating range, the number of reviews, the number of pictures?

You can choose a minimum and maximum value, whether there is a contact form on the website or ad pixels. Let's choose a random filter. I click on filter, and I will get fewer results one more time, but much more targeted ones. Maybe it was a bit too specific, so let's say I want to get all of them regarding ad pixels. Okay, it's better.

Exporting Your Data

If it sounds good to you, you can export your data. Then you can name your file, and if you click on Advanced options, you can set up a limit. So let's say I want to get the first 100. I have an overview of all the columns, all the fields I'm about to get together. I click on export, and it's going to take a bit of time.

So what I suggest is to wait for a couple of minutes or a couple of hours, depending on the size of your file. Then you come back, and you will be able to download your file in CSV or Excel format.

Analyzing the Extracted Data

I see you back once I have access to my Excel file. What do we have here? We have a hundred data rows, as expected. We start with the company's name, whether the restaurant is closed or not. We can notice that our filters worked perfectly.

The main type, the main category, and the secondary categories, the website, the phone number, the address, which is divided into different subtypes: the cities, the streets, the postal code, the state with the Level 1 plus Level 2 divisions, a country, the latitude and the longitude, the link of the Google Maps detailed page. Let's check it out - it sounds good to me.

We also have access to the email address when there is one, with a Facebook link, social media links in general, the price range, the review count, the rating, and the review per score - so combination of both columns. The number of photos with the URLs of some pictures. Let's check it out - it sounds correct.

For those who want to learn more about how to extract email addresses specifically from Google Maps listings, there are advanced techniques that can significantly boost your contact discovery rate beyond what traditional scraping methods offer.

Comprehensive Business Intelligence

The occupancy, which is related to this graph. Whether the restaurant has been claimed on Google Maps, their working hours, the characteristics - the characteristics that you can see in the About tab. Auto setting, curbside pickup, no-contact delivery, and/or door settings, curbside pickup, no-contact delivery, and so on.

If the company has got a website, what about its SEO? What about the website title with the meta keywords, description, image, generator? We can also scrape additional email addresses when they are available. Same thing with contact pages - let's have a look at one of them. Voilà! Same thing with additional social media links, and we end up with website technologies and ad pixels.

For example, we know that this company uses Facebook ads.

Conclusion: Python vs No-Code Solutions

This is the end of the video. I hope you have enjoyed it. As we have seen, you can use Python to scrape data from Google Maps. All you need to do is to spend a bit of time and to spend a bit of budget for managing proxies and bypassing CAPTCHAs.

Fortunately, on the other hand, you can also use Scrap.io and get your first extraction, your first CSV or Excel file, in five minutes. You can give a thumbs up, subscribe to the channel if you want to, and if you want to use Scrap.io, the link remains in the description. You can get your first 100 leads free of charge.

Scrap.io is a powerful Google Maps data scraper that offers 200 million businesses indexed across 195 countries, making it the most comprehensive solution for extracting business data from Google Maps without any coding knowledge. Unlike traditional alternatives such as OutScraper or other expensive tools, Scrap.io provides superior features and better value for businesses of all sizes.

If you're looking for even simpler alternatives, you might also consider Chrome extensions for Google Maps scraping which provide basic data extraction capabilities directly in your browser.

Frequently Asked Questions

Can you scrape Google Maps without coding?

Yes, absolutely! Tools like Scrap.io allow you to scrape Google Maps without any programming knowledge. As demonstrated in this guide, you can extract business data from Google Maps in just a couple of clicks. The platform handles all the technical aspects including CAPTCHAs, proxies, and data formatting, delivering ready-to-use CSV or Excel files.

What's the best Google Maps scraper tool?

Scrap.io stands out as the best Google Maps scraper tool for several reasons: it offers a no-code interface, handles 200 million business listings across 195 countries, provides advanced filtering options, and can extract comprehensive data including emails, phone numbers, social media links, and business details. Unlike Python libraries that require technical expertise, Scrap.io works out of the box.

Is scraping Google Maps legal?

Yes, scraping publicly available data from Google Maps is generally legal. Scrap.io is RGPD compliant and only extracts publicly available business information that you could manually copy from Google Maps. The platform follows legal guidelines for data extraction and commercial use is authorized for publicly available data. For a detailed analysis of the legal aspects of Google Maps scraping, including compliance requirements and best practices, there's a comprehensive legal guide available.

How to extract emails from Google Maps?

Scrap.io automatically extracts email addresses from Google Maps business listings by crawling the associated websites. The platform can find up to 5 email addresses per business and provides the source of each email (whether from Google Maps directly or from the business website). This process is completely automated and included in all extraction results.

What data can you get from Google Maps?

You can extract comprehensive business data including: business names, addresses, phone numbers, websites, email addresses, social media links (Facebook, Instagram, YouTube, Twitter, LinkedIn), Google Maps ratings and reviews, business hours, price ranges, photos, and website technologies used by businesses. Scrap.io also provides SEO data like meta descriptions and keywords.

How to scrape Google Maps reviews?

While this guide focuses on business data extraction, Scrap.io can access review counts and ratings from Google Maps. For more detailed review scraping, the platform offers specialized tools that can extract review text, reviewer information, and review metadata while respecting Google's terms of service.

Is there a free Google Maps scraper?

Scrap.io offers a 7-day free trial with 50 searches and 100 export credits, making it an excellent free option to start with. Additionally, they provide a free Chrome extension called Maps Connect that displays emails and social media links directly on Google Maps for unlimited use.

How to avoid getting blocked when scraping Google Maps?

Scrap.io handles all anti-blocking measures automatically, including CAPTCHA solving, proxy rotation, and rate limiting. The platform can process 5,000 requests per minute without getting blocked, thanks to its advanced infrastructure. This eliminates the need to worry about IP bans or detection that often occur with DIY Python scraping solutions.