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Google Maps is a web mapping service from Google. It provides access to detailed Maps, street views, and route planning for your preferred location. For instance, if you are traveling from Miami to California for the first time, you can use Google Maps to track your movement, the best route, and the estimated time of arrival. In addition, Google Maps allows you to visualize stable landmarks as you travel along a route, especially if you are going by road.
Therefore, Google Maps data extraction involves collecting information from the platform for various purposes, including analysis, visualization, and integration into other applications. This guide will explore how to scrape Google Maps with Python, its benefits, and the role of NetNut proxies.
Let us dive in!
Components of Google Maps
Here are some components of Google Maps that work together to provide a comprehensive navigation experience:
Maps tiles
Google Maps displays Maps in the form of a titled image. However, the images dynamically load as the user interacts with the Maps. Bear in mind that each tile represents a small portion of the Maps at a specific zoom level.
Street view
Google Maps provides street view imagery, which allows users to explore streets and their landmarks. This feature is especially important when you are new to a place and you need to navigate the street. In addition, this data can be used to extract data related to locations for various applications. The street view feature allows you to recognize landmarks in real-time. Subsequently, this feature provides a 360-degree scan of the place including buildings, malls, and people.
Traffic data
One significant feature of Google Maps is that it provides access to real-time traffic data. In other words, you can get updates on accidents, roadblocks, closures, or congestion. Subsequently, this data can be extracted for traffic monitoring applications.
Geocoding
Another feature of Google Maps is geocoding, which allows you to convert an address to geographic coordinates- latitude and longitude and vice versa. Therefore, this data can be collected for mapping and location-based applications.
Directories
A significant feature of Google Maps is that it allows you to find stores, restaurants, or businesses at airports, malls, or transit stations. From the Directory tab, you can see businesses and facilities in a specific building. In addition, you can get details about the business from this feature to help you decide if you want to visit the place.
Application of Google Maps Data
Now, you may wonder why you need to learn how to scrape Google Maps data when it is all about directions. Google Maps is a rich source of data, and it is continuously updated with restaurants, cafes, bars, hotels, pharmacies, historical landmarks, and many more. In other words, Google Maps covers every category that may interest you. Subsequently, the data extracted from Google Maps can be applied to market research, business analysis, and more.
Here are some of the applications of Google Maps data:
Business analysis
One of the applications of scraping Google Maps is that it provides data for business analysis. You can get a list of businesses in the target area and their addresses. In addition, you can collect information regarding ratings and reviews that give you valuable insights into the reputation of a particular business. This data is crucial because it can be leveraged by businesses to analyze their competitors, evaluate customer satisfaction, and optimize their marketing strategies. In addition, the data can be used for lead generation or to create directories.
Market research
Collecting data from Google Maps is crucial in market research. It can provide insight into the competitive situation of a chosen product or service within a particular region. In addition, you can understand how distribution occurs within a specific region of interest.
Market research allows companies to collect data that provides insight into current trends and competitor monitoring. Therefore, Google Maps scraping is a critical aspect of the research. It provides accurate information to facilitate decision-making that could alter the direction of operations. Google Maps scraping provides high-quality, large-volume, and insightful data for optimal market analysis
Location-based services
Scraping Google Maps is essential for those involved in location-based services. For example, you can get a list of interests like museums, food stalls, or hotels within a target location. As a result, you can leverage this data to offer location-based services to users. This can be applied to create applications that show points of interest in the area.
Geospatial analysis
Google Maps contains a large volume of data related to locations. Therefore, urban planners and geographers may collect data from Google Maps for city planning, traffic analysis, and environmental issues. Subsequently, geospatial analysis can be used to make informed decisions about city development projects.
Navigation and routing
One of the uses of Google Maps is navigation and routing. Subsequently, you can scrape data from Google Maps to create customized navigation and routing. This can be used to plan logistic operations, transportation routes, and optimize efficiency of transportation and routing in general.
Real Estate Analysis
Collecting data from Google Maps can be used for real estate analysis. Data such as location, property details, and surrounding landmarks can be used to analyze trends, identify opportunities, and make informed decisions.
Event planning
Planning an event involves choosing the best location. You can get access to this data by scraping Google Maps. Subsequently, data obtained from the platform can be used to plan events, choose venues, and provide real-time directions to the specific area.
How to scrape Google Maps With Python
There are several ways to extract data from Google Maps, and your choice depends on the volume of data, application, budget, and technical expertise. One of the most common ways to collect data from Google Maps is to use APIs. Although this method is quite efficient, it has several limitations, including data access restrictions, request rate limits, and high costs for collecting large volumes of data.
An alternative is to use a web scraping framework to build a scraper. However, you need to integrate proxies into your scraping code to optimize its functionalities. This guide will explore how to use the Selenium Wire library, Webdriver Manager (to manage the browser drivers), and Beautiful Soup (HTML parsing library). In addition, our target website will be Google Maps results for restaurants in New York that serve Italian dishes.
Step 1: Set up the coding environment
The first step is to prepare your coding environment to ensure you can write and run scripts. There are two popular options to set your coding environment- a basic text editor and an Integrated Development Environment (IDE). The basic text editor, often used with a command-line tool, allows you to create, modify, and manage text-based documents and examples include Notepad, Vim, Atom, and Sublime Text. On the other hand, an Integrated Development Environment is a tool that allows for building, testing, and editing to increase developer productivity and examples include PyCharm and Microsoft Visual Studio.
The next step is to download the latest version of Python from the official website. To install the necessary libraries, use the code below on the Terminal (Linux and macOS) or on Command Prompt (Windows):
pip install selenium selenium-wire webdriver-manager beautifulsoup4
- For macOS:
pip3 install selenium selenium-wire webdriver-manager beautifulsoup4
- Create a new file to store your Python codes and import these libraries as shown below:
from seleniumwire import webdriver
from selenium.webdriver.chrome.service import Service
from webdriver_manager.chrome import ChromeDriverManager
from selenium.webdriver.common.by import By
from selenium.webdriver.common.action_chains import ActionChains
from selenium.webdriver.common.keys import Keys
from bs4 import BeautifulSoup
import time
import csv
Step 2: Get residential proxies
The use of proxies is one of the best practices regarding how to scrape Google Maps. Proxies work by masking your actual IP address to ensure anonymity and security. Subsequently, they route your network traffic through different IP addresses to prevent fingerprinting, which could eventually lead to an IP block. Many websites, including Google Maps, have anti-scraping measures designed to identify suspicious traffic from IP addresses and block them. Therefore, using proxies ensures you can avoid IP ban to guarantee access to real-time data.
In addition, proxies allow you to scrape Google Maps without worries about geographical restrictions and rate limits. For this guide, we will discuss how to integrate our residential proxies in the Python script. However, there are other proxy types, including Mobile, Datacenter, and ISP proxies.
Go to NetNut official page to create an account and claim your one week free trial. Once you have signed up, you will be directed to speak with one of our experts to guide you through the preliminary stages.
On your NetNut dashboard, configure your parameters by selecting the authentication method, location, session type, and protocols. Once this is done, copy your NetNut credentials, as you will need them to integrate with your Python script.
Here is the proxy integration structure where you will need to insert your username and password:
proxy_username = ‘username’
proxy_password = ‘password’
seleniumwire_options = {
‘proxy’: {
‘auth=(“USERNAME”: “PASSWORD”), # Replace with your API user credentials. For example- Authorization: Basic base64(username: password)Where base64(username:password) is the Base64-encoded string of your username and password concatenated with a colon (:) separator.
,
‘verify_ssl’: False,
},
}
- Now, let us add the code to set up the Selenium Wire driver and proxy configurations:
service = Service(ChromeDriverManager().install())
driver = webdriver.Chrome(service=service, seleniumwire_options=seleniumwire_options)
The service object sets up the ChromeDriver required for browser automation with Selenium. Subsequently, the driver instantiation with seleniumwire_options applies the proxy settings of your web automation session, which enables the request to be sent via the proxies.
Step 3: Prepare the web browser automation and interaction
For effective scraping, you must understand that the dynamic nature of the website demands web browser automation and interaction code. The dynamic nature of Google Maps is because it relies on JavaScript to load content. Therefore, some information may appear dynamically depending on the user’s actions instead of being embedded in the initial HTML code. However, tools like Selenium, which we will be using in this guide, can mimic human user behavior, and this allows the scripts to trigger the display of data by interacting with the page.
For this step, we need to indicate the target URL followed by the command for the driver to open the web page, as shown below:
url = ” https://www.google.com/Maps/search/google+Maps+italian+food+in+new+york/
driver.get(url)
The next step is to ensure you avoid the prompt that Google uses to ask you to accept cookies. Therefore, we will use a try-except structure since there is a possibility that the browser instance won’t be required to accept cookies.
To do this, you need to inspect the HTML of the target page using the Accept cookies option. Next, we find the Accept All button’s XPath and CSS. Subsequently, this guide will indicate in the terminal whether you need to click the button:
Try:
button = driver.find_element(By.XPATH,”//button[@class=’VfPpkd-LgbsSe VfPpkd-LgbsSe-OWXEXe-k8QpJ VfPpkd-LgbsSe-OWXEXe-dgl2Hf nCP5yc AjY5Oe DuMIQc LQeN7 XWZjwc’]”)
button.click()
print(“Clicked consent to cookies.”)
except:
print(“No consent required.”)
The next step is to include a delay in our code. We recommend making the script wait for the Maps and places to load n( maximum of 30 minutes). Then, we can take a screenshot of the browser window to get visual information on how the page looks during scraping. Subsequently, this helps to identify page loading errors and anything that requires modification. You can change the destination where the screenshot will be saved as you prefer.
driver.implicitly_wait(30)
screenshot_path = ‘/path/to/your/destination/screenshot.png’
driver.save_screenshot(screenshot_path)
print(f”Screenshot saved to {screenshot_path}”)
At this stage, you need to implement browser scrolling so it loads more places and you can extract more data. Subsequently, this part of the code uses the xPath to locate the Google Maps panel on the left which holds our data, selects it to keep it in focus, and scrolls down using the Page Down keyboard button to load more results. However, you can modify the last line of the code if you need to adjust the number of presses and pause time between each press.
def scroll_panel_with_page_down(driver, panel_xpath, presses, pause_time):
“””
Scrolls within a specific panel by simulating Page Down key presses.
:param driver: The Selenium WebDriver instance.
:param panel_xpath: The XPath to the panel element.
:param presses: The number of times to press the Page Down key.
:param pause_time: Time to pause between key presses, in seconds.
“””
# Find the panel element
panel_element = driver.find_element(By.XPATH, panel_xpath)
# Ensure the panel is in focus by clicking on it
# Note: Some elements may not need or allow clicking to focus. Adjust as needed.
actions = ActionChains(driver)
actions.move_to_element(panel_element).click().perform()
# Send the Page Down key to the panel element
for _ in range(presses):
actions = ActionChains(driver)
actions.send_keys(Keys.PAGE_DOWN).perform()
time.sleep(pause_time)
panel_xpath = “//*[@id=’QA0Szd’]/div/div/div[1]/div[2]/div”
scroll_panel_with_page_down(driver, panel_xpath, presses=5, pause_time=1)
- To finalize our browser automation and interaction, we need to retrieve the target page HTML source code as shown below via the web driver:
page_source = driver.page_source
Step 4: Parse and Save the data to a CSV file
The last step in learning how to scrape Google Maps is to parse and save the data to a CSV file. At this stage, the Python script has retrieved all the data you need. Therefore, you need to sort and store the data in a CSV file so it can easily be accessible in a readable format.
BeautifulSoup is a powerful Python parsing library that we will initialize to parse the HTML content. Subsequently, the script goes through the parsed HTML to find and store elements based on their specific CSS class names.
soup = BeautifulSoup(page_source, “html.parser”)
titles = soup.find_all(class_=” ID”)
ratings = soup.find_all(class_=’ID’)
reviews = soup.find_all(class_=’ID’)
services = soup.find_all(class_=’ID’)
- Next, we can write a few lines of code to provide immediate feedback about the volume of data successfully retrieved. This is necessary to verify that the Python script works as intended by confirming the number of places identified during the scraping process, as shown below:
elements_count = len(elements)
print(f”Number of places found: {elements_count}”)
- Afterward, you need to specify a file path for saving scraped data into a CSV file that you have named. Subsequently, the script opens this file for writing and creates a header row with columns for “Place, Rating, Reviews, and Service options.” Moving on, it iterates over each title, rating, review, and service option and places the data into subsequent rows of the CSV file. Then, the terminal notifies us that the data has been successfully stored to the desired path.
csv_file_path = ‘/path/to/your/destination/places.csv’
with open(csv_file_path, ‘w’, newline=”, encoding=’utf-8′) as csv_file:
csv_writer = csv.writer(csv_file)
csv_writer.writerow([‘Place’, ‘Rating’, ‘Reviews’, ‘Service options’])
for i, title in enumerate(titles):
title = title.get(‘aria-label’)
rating = (ratings[i].text + “/5”) if i < len(ratings) else ‘N/A’
review_count = reviews[i].text if i < len(reviews) else ‘N/A’
service = services[i].text if i < len(services) else ‘N/A’
if title:
csv_writer.writerow([title, rating, review_count, service])
print(f”Data has been saved to ‘{csv_file_path}'”)
- Finally, we can end the web driver session and close the browser window. This is a crucial step in cleaning and optimizing resources used during web automation via Selenium. You can use this line of code to end the process:
driver.quit()
Challenges Associated With How To Scrape Google Maps
Getting data from Google Maps offers significant value to businesses. However, it comes with some challenges since the Python script is an automated program. The primary setback is that the algorithm cannot differentiate between good and malicious bots. Therefore, Google may mistake scrapers for malicious bots, which triggers IP bans blocking.
Here are some of the challenges related to how to scrape a Google Maps include:
CAPTCHAs
CAPTCHAs are a popular security measure on many websites, including Google. This test is designed to tell humans apart from computers. It includes identifying objects, positions, or colors in an image.
A simple bot will fail this test, causing the IP address to be blocked. However, if your Google Maps scraper is very advanced, it can bypass IP bans. One of the ways to handle CAPTCHAS is to integrate NetNut proxies that come with technology that can bypass these tests.
IP blocks
IP blocks are probably one of the biggest challenges when using a Google Maps scraper. When Google blocks your IP, you cannot access critical data, which may lead to tension and frustration.
Google can identify your IP address when you send a request to view a location on the Maps. Therefore, sending too many requests within a short period can trigger an IP block. You can avoid this by implementing a rate-limiting function in the code for scraping Google Maps.
Dynamic content
Another challenge to scraping Google Maps is a dynamic website. Google content is usually dynamically generated, so it relies on JavaScript. Therefore, your regular scraper may be unable to interact and extract the necessary HTML data for complete data retrieval. As a result, the final product of your web scraping produces incomplete data. Subsequently, you need to get familiar with the website so you can frequently update the scraping code as necessary. One of the ways to handle this challenge is to use headless browsers like Selenium or Puppeteer, which can handle dynamic content.
Rate limiting
Another challenge to scraping Google Maps is rate limiting, which describes the practice of limiting the number of requests per client within a period. Many websites, including Google, implement this technique to protect their servers from large requests that may cause lagging or a crash (in the worst cases).
Therefore, rate limiting slows down the process of web data extraction. As a result, the efficiency of your scraping operations will be reduced – which can be frustrating when you need a large amount of data in a short period.
You can bypass rate limits with proxy servers. These servers can randomize your request headers so that the website will not identify your requests as coming from a single source.
Best Practices for Scraping Google Maps Data
Read the web page robots.txt
Before you begin scraping Google Maps, ensure you read the robot.txt file. This helps you familiarize yourself with specific data that you can scrape and those you should avoid. Subsequently, this information helps guide you in writing the code for the web data extraction activity.
Terms and conditions/ web page policies
Another great tip for optimizing Google Maps scraping is reviewing the website policy or terms and conditions. Many people overlook the policy pages because they often align with the robot.txt file. However, there may be additional information that can be relevant to your web data extraction activities.
Avoid sending too many requests
There are two primary dangers of sending too many requests to a website. First, the site may become slow, malfunction, or even crash. Secondly, the website’s anti-scraping measures are triggered, blocking your IP address.
A common mistake when using a Google Maps scraper is sending many requests. Although the scrapers are meant to automate and streamline the process of data extraction, they can trigger anti-bot measures. Therefore, your IP will be flagged and most likely blocked if you attempt to scrape large quantities of data at once.
Use proxy servers
One of the primary challenges of Google Maps scrapers is blocked IP. However, you can avoid this limitation by using proxy servers. Proxies are intermediaries between your scraper and Google. It works by assigning you different IP addresses, which makes it difficult to identify and be blocked.
Therefore, choosing the right proxy solution for Google Maps scraping activities is a priority.
Rotate IP address
Rotating IP addresses is another strategy to help you avoid getting blocked when using a Google Maps scraper. Do not make the mistake of using the same IP address to collect data from Google for an extended period. Therefore, you need to choose a proxy provider with automated IP rotation to bounce your IP address around to avoid being banned.
Modify HTTP headers
Adjusting the HTTP headers is an excellent technique to avoid anti-bot detection when scraping Google Maps data. Although often overlooked, it can significantly reduce the chances of getting blocked by Google’s dynamic anti-scraping measures.
Choosing the Best Proxy Server For Scraping Google Maps- NetNut
The use of premium proxies is instrumental in optimizing your Google Maps scraping activities. NetNut is an industry-leading proxy server provider with an extensive network of over 85 million rotating residential proxies in 200 countries and over 250,000 mobile IPS in over 100 countries, which ensures seamless data extraction.
- NetNut offers various proxy solutions designed to overcome the challenges of web scraping. The rotating residential proxies are your automated solution that ensures you get access to real-time data from all over the world.
- Scalability is a crucial aspect of web scraping. NetNut proxies are highly scalable and provide high speed to ensure your data retrieval is completed in a few minutes. We also guarantee 99.9% uptime when you use our proxies for web scraping.
- The use of NetNut proxy allows you to hide your actual IP address to avoid IP blocks as well as maintaining security and anonymity.
- Alternatively, you can use our in-house solution- NetNut Scraper API, to access websites and collect data. The SERP Scraper API allows you to collect data from any location in the world. It allows you to bypass geographical restrictions, allowing you to access relevant data without any hindrances.
- CAPTCHAs and IP blocks are two of the most common challenges with search engine scrapers. However, with NetNut SERP Scraper API, you can bypass these anti-scraping measures with ease. Therefore, the API is ideal for large projects offering speedy data collection. Consequently, with no obstacles, your SERP scraping activities become streamlined, and you can make informed decisions quickly.
- Moreover, if you need customized web scraping solutions, you can use NetNut’s Mobile Proxy.
Conclusion
Google Maps is a feature that is becoming increasingly useful in the everyday lives of numerous individuals across the world. Therefore, this makes it an excellent source of web data extraction. This guide has examined how to scrape Google Maps with Python, the associated challenges, best practices, and why you need proxies.
Bear in mind that scraping Google Maps is not an easy task due to its dynamic nature and constant change in website structure. Therefore, before deploying your Google Maps scraper, you need to confirm that Google Maps has not updated its page structure. An update in the structure of the website makes it very challenging to collect all the data you require.
Proxies are crucial to the efficiency of your scraping activities. In addition, they mask your actual IP address to prevent browser fingerprinting which can lead to detection and IP Ban.
Do you have any questions? Feel free to contact us-+ as we have 24/7 live support team via email or live chat on the website.
Frequently Asked Questions
Is it legal to extract Google Maps data?
To determine the legal status of your scraping activities, you need to review Google Maps’ Terms of Use as well as the Robot.txt file. Although Google Maps scraping holds great significance for businesses, it is crucial to consider legal and ethical matters.
Extracting Google Maps data is legal if you are collecting only publicly available data. However, your scraping may be termed illegal if it involves extracting copyrighted data or private data such as usernames and passwords.
Can you get real-time data from Google Maps?
Yes, learning how to scrape Google Maps provides access to real-time data. Access to real-time data is crucial to businesses as it helps them make informed decisions based on the most current events. Subsequently, this is a valuable strategy for adapting to dynamic market changes, price changes, or customer sentiments.
What is a Google Maps scraper?
A Google Maps scraper is a program designed to extract data from the website. Most of the time, the scraper is built using a programming language like Python. Subsequently, it automates the process of retrieving data from Google Maps listings. This automated tool can be used for different purposes, including market research, local business analysis, lead generation, and more. Data that can be retrieved with the Google Maps scraper include business name, phone number, email, address, reviews, ratings, and more.
