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How Can An Airbnb Scraper Assist In Extracting Publicly Available Data?

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How Can an Airbnb Scraper
Assist in Extracting Publicly
Available Data?

@© nfo@webscrapingcom | @) iwebscraping com

If you are in search of exploring a perfect destination or vacation spot, then let data web scraping do its job!

A web data scraper is a software that assists you in automating the difficult process of fetching unnecessary information from third-party websites. Many online services provide developers with the right API in reference to read a piece of information from several websites. But, Airbnb is not that kind of website. This is the time when web scraping services comes into action.

Reasons Behind Scraping Airbnb listings data

Airbnb gives you an option to put properties on rent. It is successfully invented in 2008 by Joe Gabbian, Brian Chesky, and Nathan Blencharczyk.

Everyone can search for the listing on website, just by accessing Airbnb and browsing for a destination.

It is obvious that there are several personal reasons for extracting few pieces of information and we hope this blog will help you thoroughly.

Process of Scraping Airbnb Data Using Web Data Scraping API

To fetch the essential data, you need to follow below steps:

1. Monitoring a Source Code


 

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List out the basic things that you want to scrape from Airbnb’s website. You just need to right-click at any point on the website and select “Inspect” option and you will see the developer’s tool. For example, if you want to scrape the price, destination ratings, image, and type.

2. Selecting a Web Scraper Tool


 

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Stays in Bangkok

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For obtaining the effective results, we suggest taking our service, of web scraping API. Once you log in to the portal, you need to open a dashboard page. Then search the private API playground where you can examine your product and its documents.

3. Installing the Project

The selling method of other sellers on Shpock will help you influence the way that you sell your products. A shock product scraper will harvest the listings in the categories as yours, descriptions of the products, and photos of the products.

Once the folder gets created, run the below code:

npm init -y npm install got jsdom

For request, we need to install the got module, and for HTML parsing, we will run jsdom package.

Also, create a new folder named index.js and open it.

4. Make the Request

Set the factors and then create a request for parsing the HTML. Also, write the below lines in previous file.

const {JSDOM} = require("jsdom") const got = require("got") (async () => { const params = {       api_key: "YOUR_API_KEY",       url: ""   }   const response = await got('', {searchParams: params}) const {document} = new JSDOM(response.body).window   const places = document.querySelectorAll('._gig1e7') })()

As mentioned earlier, all the related information is found under _gigle7 element, hence we will extract all the fundamentals that are allotted to _gigle7 class. One can then login the screen using a code console.log() action just write the code shown below where we mention the fixed places.

console.log(places)

5. Receiving JSON Format Data

From here, we need to search more for fetching particular elements inclusive of the price, rating information, and image type.

Then, copy the below code.

const results = [] places.forEach(place => {    if (place) {        const price = place.querySelector('._ls0e43')        if (price) place.price = price.querySelector('._krjbj').innerHTML        const image = place.querySelector('._91slf2a')        if (image) place.image = image.src        const type = place.querySelector('._b14dlit')        if (type) place.type = type.innerHTML        const rating = place.querySelector('._10fy1f8')        if (rating) place.rating = rating.innerHTML        results.push(place)    } }) console.log(results)

As shown above, for every listing on the initial page, we extract the price tag element, reviews, kind of listing and image source location, and ratings. As a result, we will possess a collection of objects, and also every object will include element in the list.

Now, when the necessary code for scraping Airbnb information is written, the page index.js will look like:

const {JSDOM} = require("jsdom"); const got = require("got"); (async () => {   const params = {       api_key: "YOUR_API_KEY",       url: ""   }   const response = await got('', {searchParams: params})   const {document} = new JSDOM(response.body).window   const places = document.querySelectorAll('._gig1e7')   const results = []   places.forEach(place => {       if (place) {           const price = place.querySelector('._ls0e43')           if (price) place.price = price.querySelector('._krjbj').innerHTML           const image = place.querySelector('._91slf2a')           if (image) place.image = image.src           const type = place.querySelector('._b14dlit')           if (type) place.type = type.innerHTML           const rating = place.querySelector('._10fy1f8')           if (rating) place.rating = rating.innerHTML           results.push(place)       }   })   console.log(results) })()

It is very easy to scrape Airbnb data using web scraping API.

  1. Place A Request To Web Scraping API Using Two Parameters: API Key And URL Needed To Scrape Data From.
  2. Use JSDOM To Load DOM
  3. Choosing All The Data Listings By Searching The Particular Class.
  4. For Every Listing, Obtain Price Tag, Listing Type, Rating, And Image.
  5. Add Each Point To A New Collection Known As Results.
  6. Log The New Result Collection To The Display.

The response will be as following:

[  HTMLDivElement {    price: '$47 per night, originally $67',    image: '',    type: 'Room in serviced apartment in Friedrichshain',    rating: '4.73'  },  HTMLDivElement {    price: '$82 per night, originally $109',    image: '',    type: 'Entire serviced apartment in Mitte',    rating: '4.80'  },  HTMLDivElement {    price: '$97 per night, originally $113',    image: '',    type: 'Entire apartment in Mitte',    rating: '4.92'  },  HTMLDivElement {    price: '$99 per night, originally $131',    image: '',    type: 'Entire apartment in Prenzlauer Berg',    rating: '4.90'  },  HTMLDivElement {    price: '$56 per night, originally $61',    image: '',    type: 'Entire apartment in Tiergarten',    rating: '4.67'  }, ... ] 
 

Web Scraping and Airbnb

A web data extractor allows you to select the particular data you wish to have from Airbnb listing and scraping them to build a listing database.

For instance, iWeb Scraping service uses Python for scraping Airbnb data and easily scrapes dynamic sites like Airbnb.

Final Words

By using data web scraping for Airbnb, we can extract all the possible data from Airbnb within your reach. You can repeat this process to search for the best vacation spot or manage your new listing.

If you find any queries related to how to scrape the Airbnb data or any other website, feel free to contact us.


 

3) obo pa ats mamta ssses @)

re tn Coetimn beatin | ates mea om wh Arcodmng

ee senid
Stays in Bangkok

PR
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