In a hurry?
The best Google Maps scraper in 2023, as found in our independent testing, is Phantombuster!
Over time, Google Maps has developed drastically from a simple navigation app to a platform with various opportunities for businesses to promote themselves.
In today’s age, it’s imperative for all physical businesses to be present and updated on Google Maps in order to grow their customer base.
Every business also wants its name featured at the top, and to achieve this, they publish tons of attractive data to Google Maps.
This data can be invaluable to individuals- whether they’re studying statistics, doing market research, gathering contact details to embed Google maps, or something else.
If you want access to all of this information in one place, or want to draw comparisons, you need to copy-paste this info into a document.
Of course, plain copy-pasting done manually can take forever when it comes to larger projects.
This is where web scraping comes in- this technique collects all the info and presents it in a spreadsheet accurately.
Scraping data from Google Maps can be done with various automated Google Maps scrapers that make the task significantly easier and quicker, although you can perform a more complicated manual extraction by writing Python code.
As such, Google Maps scraper tools are generally better, though they’re not free most of the time.
To help you get started with scraping Google Maps, we’ve gathered a list of the best Google Maps scrapers out there, along with how to use them. Let’s dive in!
Best Google Maps Scrapers 2023
A super popular Google Maps scraper is Phantombuster.
It’s a robust program that almost fully automates the data extraction process.
Because of this, there are no prerequisites to using this app and you certainly don’t need sharp developing or coding skills.
Phantombuster extends its data scraping support to Google Maps, but it can run on a number of other websites.
Phantombuster presents extracted data in an easy-to-use interface with point and click mechanisms.
The data is extracted and displayed in a structured manner. You can export the information in multiple formats, including Excel, HTML, CSV, and TXT.
You can upload extracted data to databases including Oracle, MySQL, and SQL Server.
The app also has a great support network- you can contact customer support at all times, and there’s an active blog and FAQ section on Phantombuster’s official website where you can find answers to the most common problems users run into.
Phantombuster works well in the cloud.
Since Phantombuster mimics typical human behavior on the internet, there are little to no chances of you being blocked from any site, let alone Google Maps.
You can scrape several pages at once and either run the app on the device or on the local cloud network.
Phantombuster follows a freemium pricing strategy. There’s a two-week free trial, and afterward, you need a subscription that starts at just $59/month.
There are several other pros to using Phantombuster and some unique features are present.
These include anonymous scraping, where your IP address won’t be given to the targeted website. Because of this, you don’t need a VPN while scraping data anymore.
So once you’ve scraped Google Maps for data and want to scrape a particular business’ social media, you can perform the task just as easily.
Phantombuster also has auto-detect, so the app’s AI learns when a webpage uses tables, load buttons, listings, etc.
The auto-detect mode can be found in the advanced modes section.
Other than that, fast cloud extraction from many web pages, and 24/7 access to data stored in the cloud are key features that make Phantombuster a go-to scraping platform.
One more highlight of Phantombuster automation is the scheduled extraction- that’s right, you don’t need to present to initiate scraping in real time, and can simply set up commands for a scheduled crawling.
You can also set auto extraction tasks that will be performed at regular intervals daily, weekly, or on a monthly basis.
The software can run APIs too, communicating your scraped data to another system in real-time. You can create an API task ID inside Phantombuster related to each scraping task.
You can also set incremental extraction where your extracted data is automatically updated without wasting time on having to set up new configuration fields.
Lastly, as for user-friendliness, the app itself has a clean and intuitive interface. There’s a lot of support related to using Phantombuster on the official website too.
The software also comes with many pre-built tasks called phantoms, which can be selected to verify and extract data without having to configure or create fields yourself.
If you’re unsure of how to start using Phantombuster for scraping Google Maps, you can follow the simple 6-step guide below.
Finding Maps URL
First things first, go to your Google Maps and visit the area where the businesses whose information you want to scrape are present to copy the web address.
Forming A Task
Log in to your Phantombuster account and go to the main web page. Click on Add Task and select Advanced Mode when creating the task.
Since Google Maps is a complicated website with complex data structures, using a flexible and powerful scraping mode like this one is necessary.
Afterward, paste the URL you copied in the Extraction URL box and hit the save button.
Scrape Multiple Pages
Next, you need to make a pagination loop, which will scrape multiple pages for all the results. Pagination is important to separate content into discrete pages.
Automated pagination can also add consecutive numbers to each field, making it easier to identify and sort.
To create this loop, start by clicking the Workflow button on the upper-right corner of your Phantombuster page.
Enter the Workflow Mode so you can correct any mistakes you make along the way.
As Google Maps uses AJAX, click Set Up AJAX Load before performing the Click To Paginate action.
Uncheck the Retry box and tick the Load With AJAX option. Set a 15-second timeout. Hit Ok and Save.
Scrape Item Details
Now go to the first page again and select the first two sections containing the information for your business (e.g restaurant information).
On the Action Tips panel, click on Extra Data In Loop.
It’s time to save the fields that appear. If you want to delete any unnecessary data field, right-click and choose Delete. You can rename the fields if you’d like.
On the top-left corner, you’ll see a Start Extraction option. Click it, then pick between whether you want the data extracted locally or on the cloud.
You’ll get your output after a while. Remember, some fields may be left empty if a place doesn’t have a description or data about it online.
This Google Maps scraper is arguably the best all-around web-scraping software out there.
Oxylabs is equipped with features that automate all data extraction, so you don’t need coding knowledge.
Oxylabs has a simple and intuitive interface.
There may be a small learning curve when you begin using it, but there are tons of resources on the Oxylabs website for you to learn everything about the tools.
If you’re a business, you can also order a turnkey scraping solution that is created for you by certified Oxylabs developers.
Oxylabs has a free trial that you can use to get a hang of the service, though we recommend going for a paid plan (starts at $99/mo).
Oxylabs has ready-to-use tools that help get accurate results fast. The API can extract data including stars, reviews, timings, photos, reviewer ID, geolocation, etc.
Extracting data from Google My Business is simpler than ever with Oxylabs.
You can even get info about similar suggestions (People also search for).
You can download the extracted data as JSON, HTML table, EXCEL, XML, CSV, and RSS feed.
Furthermore, you don’t have to worry about getting blocked for scraping because of smart proxy rotation and VPN options.
The bots mimic human activity and are essentially indistinguishable.
And since Oxylabs uses open-source tools, there are no worries about vendor lock-in.
Oxylabs’s whole platform can be used as an API that can be connected to your software.
3. Bright Data
Bright Data is easily one of the best in the business when it comes to being a Google map scraper, and they say that they can help their clients with ready-made data sets, so that you don’t have to worry about being an expert in data collection from the beginning.
They take care of most of the process for you, which means that all you need to do is set up an account with them, and then you can sit back, relax, and know that your data is being scraped professionally, and into a format that is easily readable, and easy to understand.
They also say that they help their clients with data collection infrastructure as well, so if you do know a little bit about data collection, and you want to customize the way that they collect your data then they can assist you with this side of things as well.
If you are trying to collect all the necessary public web data that you care about, then these guys are going to be a really good option.
Bright Data has many benefits, which is why they are number one on our list. One of the first is that you can request a demo, so that you can try them out for free before you sign up for anything.
This way, you can get to the bottom of whether they’re going to be the best Google Maps web scraper or not.
As we have talked about already, these guys offer data sets, where you can benefit from large scale, pre-collected data sets, so you are going to get a preview of an entire website, including Google Maps.
The best part is that they update these data sets regularly, so you’re always going to be able to get your hands on the latest data.
They also have a data collector, where you’re going to be able to automate and streamline your data collection, without having to know anything about coding, and being able to make the most of maximum flexibility.
Another key feature that’s really important about these guys is the fact that they have proxies which means that you aren’t going to have to go anywhere else in order to make the most of your Google Maps scraper.
It is always recommended that you combine a proxy with a scraper so that you can keep your identity anonymous.
Another easy-to-use Google Maps scraper tool is ScraperAPI.
This software is AI-powered and can be used to extract all the data from a Google Maps location and clean it by filtering the unnecessary content as efficiently as Phantombuster.
ScraperAPI offers support for macOS, Windows, and Linux.
All three platforms use the same version, so you can switch between them easily if you run more than one PC.
A massive perk of using ScraperAPI is that it’s totally free.
The starter plan, although it has certain limitations (e.g you can’t export more than 100 rows of data in a day), is available free of cost without any credit card charges.
If you want more functionality, you can opt for paid plans. They start at an affordable $49/mo.
All plans give a cloud account that can be used as a centralized hub for all your scraping tasks.
As for exporting data, you can download the streamlined info in various formats including TXT, HTML, CSV, EXCEL, etc. You can run the files on JSON, Sheets, and MySQL.
ScraperAPI is a visual Google Maps scraper and boasts a point and click interface, so you can select any place of interest on the map.
The software will take care of all the workflow and pagination.
A major pro of using ScraperAPI is that it was built by experienced scrapers who understand how AJAX websites work.
Because of this, they have integrated features that prevent you from being blocked by Google Maps.
On top of that, if you go for paid plans, you’ll get access to ScraperAPI IP. You can make custom IP addresses as well.
Other key benefits include the ability to scrape from locked URLs that need a login, and AI that catches duplicated data and prompts you to either skip the data or stop the task completely.
Filter fields are also present that enable you to block any unnecessary data. Merge Fields, Add and Delete, Text Replacement, Regular Expression, etc are also present.
You can extract contact numbers and emails too and have them automatically sorted out.
With that out of the way, let’s talk about the most attractive feature of ScraperAPI- there are two scraping modes.
The first is Smart Mode, where you simply need to input the Maps URL. There’s no need to set configuration rules, though you can filter and edit info as needed.
The other mode is called Flowchart Mode, and it’s used for manually browsing the webpages.
It streamlines complex scraping methods and is better suited for people who have mastered the Smart Mode and want to learn more.
Once you put in the URL (make sure it’s the first page), ScraperAPI automatically finds relevant data on succeeding pages and extracts it.
The powerful AI identifies lists, tables, paging buttons, etc as well. Plus, you don’t have to scroll infinitely until you find all the reviews, unlike manual coding.
Like Phantombuster, you don’t need prior knowledge of coding to use either mode.
You can simply follow video tutorials on the official ScraperAPI website or read the Flowchart guides.
You can schedule extractions, run multiple tasks, and load URLs and images. Guides to modify data are also present.
Last but certainly not least is WebHarvy, yet another popular Google Maps scraper tool that works effectively with Google Maps.
This automated app has a very simple interface and only requires you to paste the URL and let it work its magic.
It requires no programming knowledge and can be used to store data in various formats (XLM, EXCEL, CSV, etc). You can upload these to an SQL database.
Where WebHarvy stands out from the rest is customer service- their support is amazing and is always present to help users with any problem or bug they’re facing.
WebHarvy is comparatively very cheap too, starting at $139 for a single-user license.
Starting with support, WebHarvy not only has online blogs and tutorials on its site for customers to learn from but there’s an interactive tour you’ll find on the webpage, which can help you decide whether or not to purchase the license.
As for functionality, you just need to fire up the app and paste the first page URL.
The software handles pagination itself, and it will scrape data from all consecutive web pages.
Since the app uses AI, it recognizes patterns and can form tables of contact data, address, email, name, etc without needing configuration.
You can use Regular Expressions to scrape matching parts.
Overall, it’s an excellent automated scraper for extracting Google Maps information.
How to Scrape Google Maps Data with Python & Selenium
If you’re a coder, you can follow the more complicated route and build a custom scraper to extract and scrape Google Maps data.
Following this method is highly complex and requires a great deal of knowledge of coding, as you’ll be writing Python codes and making adjustments along the way.
For reference, we’re going to go through the basic steps of a coding method that uses Selenium and runs the BeautifulSoup parser, although other parsers like Parsel can work just as efficiently.
We’ve mentioned below what you need to perform in which order so you can execute your code perfectly.
To begin, let’s discuss the tools you’ll need.
First up, you need a virtual library to maintain your Python dependencies in the project.
The best software for this purpose is Anaconda, but apps like VirtualENV can work too.
Basically what these tools do is that they maintain a library dependency for every project you add, creating a virtual environment.
You install the code library inside each environment and activate or deactivate it when needed.
This doesn’t interfere with the whole project.
We recommend using Anaconda as it can be beneficial later on to add extra steps like data analysis, data cleansing, machine learning, etc at the end.
Most web scraping, whether on Google Maps or elsewhere, is done through Selenium.
This software is built for web app testing and the framework features an API that simulates clicks, scrolls, and other kinds of regular human interactions that happen on websites.
Because of this, simple clicks and scrolls can help load a ton of data which you can then scrape.
Selenium library can implement different languages, from C# to Java to Python and many more. Python is the best language for web scraping.
It swiftly loads web pages and generates the required interactions, so you can retrieve extra info about the business you’re interested in.
Lastly, you need a parser tool. Our pick is BeautifulSoup, but other options should work.
BeautifulSoup is native to Python and parses all XML and HTML files. You can access attributes easily later on.
The main function of a parser is to separate the string of commands on the HTML page being processed by Selenium and extract relevant information as raw text.
The info is then sent for further processing. Install your BeautifulSoup library in the Anaconda environment.
Along with these 3, you also need to install a webdriver for the browser of your choice. A webdriver is software that runs a browser instance for Selenium to run over.
While any driver should work, the manual tests you perform later must be run with the same browser whose driver you picked.
Since most people prefer Google Chrome, our guide uses the Chromedriver.
Steps For Defining Your Google Maps Scraper
Let’s start collecting the Google Maps reviews for a place of interest, like a restaurant. This 5-step guide is for defining a Google Maps data scraper on Selenium.
- The first step is to initialize the webdriver. You can do this by adding a code to the Selenium environment with English set as the default browser language.
- Using your webdriver, simply visit the place of interest on Google Maps and open the page. Because Maps uses complicated URLs, it’s best to manually copy it from your browser as a variable and paste it to the driver.
- The third step is to sort the category. Here’s an example of retrieving reviews- by default, they are sorted as most to least relevant. If you want to sort newest to oldest, find the code for the Sort button using CSS or XPath search, highlight it, then look for the Latest code. Implement a wait function as Google Maps runs on AJAX.
- Send the sorted page to the parser. Using the find_all method, a list of div fields or elements can be created for specific properties.