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In recent years, the travel industry has witnessed an enormous surge in the number of travelers choosing airlines as their preferred mode of communication. This has happened owing to the continuously growing middle class. This has led to the development of several online portals that offer users the privilege of searching for flights, choosing and comparing prices, and booking a flight. It also allows everyday travelers, agencies, and tour operators to keep an eye on pricing to decide the suitable time for travel for themselves and their clients. Flight prices constantly change on demand and availability. However, it interests the users if these prices are trackable and analyzed for future details.
Flight prices vary across several different routes differs accordingly. If you are a service provider, you must keep your customers updated. However, the web will never display such information related to ticket prices. Web scraping will give you a detailed insight into flight prices.
A travel booking business operating through a digital platform will require real-time airline updates to give you an edge over your competitors. In such an instance, your flight price data must compete in the industry.
Remember that customers browse the internet for the best agency offering the best deals.
On the other hand, your business might require frequent travel for you or your staff. The travel expenses can cause a dent in your profits. Hence, web scraping for flight prices becomes essential for better performance for your company.
Flight prices constantly change based on months, seasons, peak periods, etc. Collecting these data manually is undoubtedly a hectic task. It changes faster than it would give you time to collect, analyze, and utilize the information. Web scraping through software will keep your business informed about the sites.
To run a scraper script for flight prices, you will require the following details
It means that your bot will extract the information mentioned above.
Kayak is a well-known online travel agency operating in more than 30 countries and 18 languages. Assessing each query on their platform, Kayak is involved in searching more than hundreds of travel sites to display information to users related to flights, hotels, car rentals, and vacation deals.
If you are planning your next wonderful trip and looking for a flight to Madrid, you can opt for a Kayak agency. In the search bar, after entering the search criteria, the URL in the browser is modified accordingly, along with some additional filters like ‘Nonstop”.
The URL gets broken down into several parts. This includes origin, destination, end date, and suffix, allowing Kayak to search incredibly close connections and list the results by price.
Now, our objective is to extract the flight data we require. The website’s core HTML code process the web scraping Kayak flight prices. It comprises departure time, arrival time, and ad price. Here we rely on two packages to perform this. One is Selenium, which controls the browser and automatically opens the page. The second is BeautifulSoup. It changes the complicated HTML code into a simplified, structured, and readable format.
To accomplish this, the first step is to download a browser driver, such as ChromeDriver. After loading a few packages, notify Selenium to open the URL using the ChromeDriver. Please place it in the same folder as the Python code.
Once the website is loaded, our next step is to find out how we can extract the information that is useful for us. Let us take an example. In the below-given image, with the inspect feature of the browser for the flight departure time, we can see that the 8.55 pm departure time is in a span with the class called
After passing the website’s HTML code to BeautifulSoup, we can easily scrape the flight prices. Extraction is done using a simple loop. As for each search, we obtain a set of two departure times. Hence, we need to reshape it into a logical departure-arrival time pair.
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On inspecting the price element, we observed that Kayak used multiple classes for its price information. Hence, we require a regular expression to capture them all. To unwrap the price information, we will need a few additional steps.
After putting everything into a proper data frame, we get
The above data shows how everything has been transformed into a readable format after scraping in an HTML code.
To simplify this process, we will now wrap our code into a function and will address that function by using different destinations and starting day combinations. Our entire code will appear like this:
Once we have quantified all our combinations and scraped the detailed data, we can easily visualize our results.
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