The most common use of web scraping booking.com is to collect hotel listings from multiple websites. This can involve keeping an eye on rates, developing an aggregator, or improving the user experience of already-existing hotel booking services.
This little script accomplishes that. We will gather hotel information from Booking.com with the assistance of BeautifulSoup.
Since the end of the previous recession, the hotel business has been expanding steadily over the past 15 years. The industry has grown more competitive as a result of growth.
The market welcomes new sellers every other day, and as a result, the previous vendors' profit margins become smaller. Therefore, it has become difficult for OTAs to maintain the booking income.
However, OTAs may get around this issue by watching what their rivals charge. But how can you find them, exactly? Web scraping will increase your revenue in addition to helping you keep track of your competitors.
Why Scrape Booking.com with Python?
Python is the universal language, and web scraping extensively uses it. Additionally, it provides specific libraries for web scraping.
If there is a vast community, you can address your problems whenever they arise. I suggest reading this detailed guide on web scraping booking.com with Python if you are new to the practice.
Booking.com scraping requirements
For this lesson, we'll require Python 3. x, which you've previously set up on your computer. Two other libraries that will be needed later in this guide for web scraping booking.com data which also must be installed.
- We will use requests to establish an HTTP link with Bing.
- We may design an HTML tree with the aid of BeautifulSoup for easy data extraction.
Setup
Install the libraries above after creating a folder first.
Write a Python file in this folder to house the code. We are going to scrape the following information from the target page.
- Address
- Name
- Pricing
- Ratings
- Room Design
- Facilities
Scrape Booking.com Data
Now that everything is ready let's send a GET request to the intended website to see whether it works.
The code is simple and doesn't require much explanation, but allow me to review it. We specified headers and target URLs after importing two libraries that we had obtained previously in this lesson.
Finally, we initiated a GET request to the target URL. If the code shown after printing is not 200, it indicates an error in your code.
What are the ways to Scrape Data Points?
Since we know the data points we want to scrape, let's examine Chrome to determine where their HTML is.
We will locate the target items for this lesson using BeautifulSoup's find() and find all() methods. The DOM structure will determine which approach is best for each element.
Obtaining the name and address of a hotel
Let's examine Chrome to determine the names and address's DOM locations.
The hotel name is under the h2 tag with the class pp-header_title, as you can see. To simplify things, we'll use the BeautifulSoup constructor to create a soup variable. This will allow us to extract all the data points.
Using an HTML parser, BS4 will transform a complicated HTML page into a thorny tree of objects created in Python. We can get the name and address by using the "soup" variable.
Similarly, we can fetch the address.
The property address is stored in the "hp_address_subtitle" class, which is located under the "span" tag.
Scraping Booking.com Ratings and Facilities
We will again check and locate the score and facilities element's DOM position.
The class d10a6220b4 div element contains the storage for the rating. To extract this component, we'll utilize the same soup variable. To extract the rating information, use the code below.
Facilities extraction is challenging. We'll make a list and put all the required HTML components in it. Then, we will save the individual texts in the primary array using a for loop to traverse over all the items.
Let's look at how to accomplish that in just two easy steps.
The facilities items will all be stored in the fac variable. Let's now remove them one at a time.
The fac_arr array will have all of the text values for the elements. We were able to extract the critical facilities effectively.
The most challenging aspect of the entire tutorial is this section. Before extracting information about prices and room types, one must carefully understand the booking.com DOM structure.
All the information is in the body tag here—the tr tag, which appears immediately below the tbody and contains all the data from the initial column.
Then, if you move down a step, you will see several td tags containing data like Room Type, price, etc.
Initially, search every tr tag.
You'll see that each tr tag includes a data-block-id attribute, for example. Let's compile every single ID into a list.
With all the IDs in your possession, the rest of the work becomes more straightforward. We will repeatedly loop over each data-block-id to separate the different tr blocks' specific room kinds and prices.
The all Data variable will contain all of the HTML information for a certain data-block-id.
We may now go to the td tags inside this tr tag. First, let's remove the rooms.
Interestingly, you can use the same room for the following pricing round, even when multiple options exist for a particular room type. Allow me to illustrate with a visual for you.
For this accommodation type, there are three prices. As the for loop iterates, the rooms variable will have a value of None. By printing it, you can see it. So, until we obtain a new value, we will continue to utilize the old value for rooms.
Until you receive a new value, last_room will keep track of the previous value of the rooms.
Now let's get the pricing.
The div tag with the class "bus-price-display__value prco-text-nowrap-helper prco-inline-block-maker-helper prco-f-font-heading" stores the price. We can locate and extract the text using the allData variable.
Finally, we could scrape every piece of data we were looking for.
Final Coding
Information, such as facilities and feedback, is also available. Just a few more adjustments will allow you to extract them as well. You may also extract other hotel information by modifying the hotel's distinctive name in the URL.
The code will look like this.
The output will look like this:
What are the Benefits of Scraping Booking.com Data?
Many travel agents get a ton of information. They know that access to competitor companies' pricing tactics is necessary if they hope to obtain a competitive edge.
One must scrape many websites and combine the data to gain an edge over the specialized opponent. After comparing your prices to your competitors, adjust your prices accordingly. You can offer discounts or showcase on the platform how affordable your prices are compared to your rivals.
With over 200 online travel agencies available, scraping booking.com and comparing with other websites information can take time.
Conclusion
As shown in this example, Python can scrape Booking.com to compare prices. However, hotel data scraping has numerous other applications. Python may be used to scrape other websites, like Expedia, Hotels.com, etc.
