Парсинг google maps python

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A python Script to scrape data from google maps.

dhanraj6/Google-Maps-Scraper

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README.md

A python Script to scrape data from google maps.

Step by step guidence to run this file in my medium post click here.

1. Download the chromedriver from here if you dont have it.
2. Add the path of chromedriver in above .py file.
3. Add the link of google map place whose data you want to scrape in above .py file

Google Maps UI changes frequently if you get any errors on running just replace older id’s with new id’s for clickable items

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A python Script to scrape data from google maps.

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Google Maps reviews scraping

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gaspa93/googlemaps-scraper

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README.md

Scraper of Google Maps reviews. The code allows to extract the most recent reviews starting from the url of a specific Point Of Interest (POI) in Google Maps. An additional extension helps to monitor and incrementally store the reviews in a MongoDB instance.

Follow these steps to use the scraper:

• Download Chromedrive from here.
• Install Python packages from requirements file, either using pip, conda or virtualenv:

 conda create --name scraping python=3.6 --file requirements.txt 

Note: Python >= 3.6 is required.

The scraper.py script needs two main parameters as input:

• —i : input file name, containing a list of urls that point to Google Maps place reviews (default: urls.txt)
• —N : number of reviews to retrieve, starting from the most recent (default: 100)

generates a csv file containing last 50 reviews of places present in urls.txt

In current implementation, the CSV file is handled as an external function, so if you want to change path and/or name of output file, you need to modify that function.

Additionally, other parameters can be provided:

• —place : boolean value that allows to scrape POI metadata instead of reviews (default: false)
• —debug : boolean value that allows to run the browser using the graphical interface (default: false)
• —source : boolean value that allows to store source URL as additional field in CSV (default: false)
• —sort-by : string value among most_relevant, newest, highest_rating or lowest_rating (default: newest), developed by @quaesito and that allows to change sorting behavior of reviews

For a basic description of logic and approach about this software development, have a look at the Medium post

The monitor.py script can be used to have an incremental scraper and override the limitation about the number of reviews that can be retrieved. The only additional requirement is to install MongoDB on your laptop: you can find a detailed guide on the official site

The script takes two input:

• —i : same as monitor.py script
• —from-date : string date in the format YYYY-MM-DD, gives the minimum date that the scraper tries to obtain

The main idea is to periodically run the script to obtain latest reviews: the scraper stores them in MongoDB up to get either the latest review of previous run or the day indicated in the input parameter.

Take a look to this Medium post to have more details about the idea behind this feature.

Url must be provided as expected, you can check the example file urls.txt to have an idea of what is a correct url. If you want to generate the correct url:

1. Go to Google Maps and look for a specific place;
2. Click on the number of reviews in the parenthesis;
3. Save the url that is generated from previous interaction.

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Google Maps reviews scraping

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Scrape Google Maps Reviews Using Python

Python is a popular high-level, multi-purpose programming language. It is widely used for applications like desktop applications, web applications, artificial intelligence, etc. But one more beautiful task it can do is web scraping!

In this blog, we will scrape Google Maps Reviews using Python and its libraries — Beautiful Soup and Requests.

Why scrape Google Maps Reviews?

Scraping Google Maps Reviews can provide you with a variety of benefits:

Valuable Insights – Scraping Google Reviews can provide valuable insights from your customers, their opinions, and feedback which can help you to improve your product and revenue growth.

Competitive Intelligence – Reviews from Google Maps can help you identify your competitors’ strengths and weaknesses, and you leverage this data to stay ahead of your competitors.

Data Analysis – The review data can be used for various research purposes such as sentimental analysis, consumer behavior, etc.

Reputation Management – Monitoring or analyzing the negative reviews left by your customers helps you identify the weakness in your product and allows you to solve problems your customers face.

Let’s Start Scraping Google Maps Reviews Using Python

In this blog, we will design a Python script to scrape the top 10 Google Reviews, including location information, user details, and much more. Additionally, I will also demonstrate a method to easily extract reviews beyond the top 10 results.

The Google Maps Reviews scraping is divided into two parts:

1. Getting the raw HTML from the target URL.
2. Extracting the required review information from the HTML.

Set-Up:

Those who have not installed Python on their device can watch these videos:

If you don’t want to watch these videos, you can directly download Python from their official website.

Requirements:

To scrape Google Maps Reviews, we will be using these two Python libraries:

To install these libraries, you can run the below commands in your terminal:

pip install requests pip install beautifulsoup4

Process:

After completing the setup, open the project file in your respective code editor and import the libraries we have installed above.

import requests from bs4 import BeautifulSoup

Then, we will create our function to scrape the reviews from Google Maps Of Burj Khalifa.

def get_reviews_data(): headers = < "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.54 Safari/537.36" >response = requests.get("https://www.google.com/async/reviewDialog?hl=en_us&async=feature_id:0x3e5f43348a67e24b:0xff45e502e1ceb7e2,next_page_token:,sort_by:qualityScore,start_index:,associated_topic:,_fmt:pc", headers=headers) soup = BeautifulSoup(response.content, 'html.parser') user = [] location_info = <> data_id = '' token = ''

In the above code, first, we set headers to the user agent so that our bot can mimic an organic user. Then, we made an HTTP request on our target URL.
Let us decode this URL first:

https://www.google.com/async/reviewDialog?hl=en_us&async=feature_id:0x3e5f43348a67e24b:0xff45e502e1ceb7e2,next_page_token:,sort_by:qualityScore,start_index:,associated_topic:,_fmt:pc

feature_id – It is also known as a data ID. It is a unique Id for a particular location on Google Maps.
next_page_token – It is used to get the following page results.
sort_by – It is used for filtering the results.
You can get the data ID of any place by searching it on Google Maps.

Let us search for Burj Khalifa on Google Maps.

If you take a look at the URL, you will get to know the data ID is between !4m7!3m6!1s and !8m2! , which in this case is 0x3e5f43348a67e24b:0xff45e502e1ceb7e2 .

Now, open the URL in your browser, and a text will be downloaded to your computer. Open this text file in your code editor, and convert it into an HTML file.

We will now search for the tags of the elements we want in our response.

Let us extract the information about the location from the HTML.

Look at the above image, you will find the tag for the title as P5Bobd , for the address as T6pBCe , for the average rating, is span.Aq14fc , and then for the total reviews is span.z5jxId .

for el in soup.select(‘.lcorif’): location_info =

Now, we will extract the data ID and the next page token.

Search for the tag loris in the HTML. You will find the data ID in the attribute data-fid . Then search for the tag gws-localreviews__general-reviews-block , and you will find the next page token in its attribute data-next-page-token .

for el in soup.select(‘.lcorif’): data_id = soup.select_one(‘.loris’)[‘data-fid’] token = soup.select_one(‘.gws-localreviews__general-reviews-block’)[‘data-next-page-token’] location_info =

Similarly, we can extract the user’s details and other information like images posted by the user, his rating, the number of reviews, and the feedback about the location written by the user.

This makes our code looks like this:

import requests from bs4 import BeautifulSoup def get_reviews_data(): headers = < "User-Agent": "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/101.0.4951.54 Safari/537.36" >response = requests.get("https://www.google.com/async/reviewDialog?hl=en_us&async=feature_id:0x3e5f43348a67e24b:0xff45e502e1ceb7e2,next_page_token:,sort_by:qualityScore,start_index:,associated_topic:,_fmt:pc", headers=headers) soup = BeautifulSoup(response.content, 'html.parser') user = [] location_info = <> data_id = '' token = '' for el in soup.select('.lcorif'): data_id = soup.select_one('.loris')['data-fid'] token = soup.select_one('.gws-localreviews__general-reviews-block')['data-next-page-token'] location_info = < 'title': soup.select_one('.P5Bobd').text.strip(), 'address': soup.select_one('.T6pBCe').text.strip(), 'avgRating': soup.select_one('span.Aq14fc').text.strip(), 'totalReviews': soup.select_one('span.z5jxId').text.strip() >for el in soup.select('.gws-localreviews__google-review'): user.append(< 'name': el.select_one('.TSUbDb').text.strip(), 'link': el.select_one('.TSUbDb a')['href'], 'thumbnail': el.select_one('.lDY1rd')['src'], 'numOfreviews': el.select_one('.Msppse').text.strip(), 'rating': el.select_one('.BgXiYe .lTi8oc')['aria-label'], 'review': el.select_one('.Jtu6Td').text.strip(), 'images': [d['style'][21:d['style'].rindex(')')] for d in el.select('.EDblX .JrO5Xe')] >) print("LOCATION INFO: ") print(location_info) print("DATA ID:") print(data_id) print("TOKEN:") print(token) print("USER:") for user_data in user: print(user_data) print("--------------") get_reviews_data()

Run this code in your terminal, and your results should look like this:

The tutorial is not over yet. I will also teach you about the extraction of the next-page reviews.

In the output of the above code, we have got the token — CAESBkVnSUlDZw==

Let us embed this in our URL:

https://www.google.com/async/reviewDialog?hl=en_us&async=feature_id:0x3e5f43348a67e24b:0xff45e502e1ceb7e2,next_page_token:CAESBkVnSUlDZw==,sort_by:qualityScore,start_index:,associated_topic:,_fmt:pc

Make an HTTP request with this URL in your code. You will get the following page reviews successfully.

Using Google Maps Reviews API To Scrape Reviews

Scraping Google is difficult. Many developers can’t deal with the frequent proxy bans and CAPTCHAs. But our Google Maps Reviews API, a fully user-friendly and streamlined solution can help you scrape reviews from Google Maps.

To use our API, you need to sign up on our website. It will only take a bit.

Once you are registered, you will be redirected to a dashboard. There you will get your API Key.

Use this API in the below code to scrape the reviews from Google Maps:

import requests payload = resp = requests.get('https://api.serpdog.io/reviews', params=payload) print (resp.text)

With this short script, you can scrape Google Maps Reviews at a blazingly fast speed without any problem.

Conclusion

In this tutorial, we learned to Scrape Google Maps Reviews Using Python. Please do not hesitate to message me if I missed something. If you think we can complete your custom scraping projects, you can contact us.

Follow me on Twitter. Thanks for reading!

Additional Resources

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