Booking.com is the world‘s leading travel accommodation platform, with over 28 million listings across more than 200 countries. For anyone in the travel and hospitality industry, the data on Booking.com is a goldmine of valuable insights. By scraping and analyzing this data, you can:
- Track competitor prices and availability
- Optimize your own pricing strategy
- Monitor your hotel‘s online reputation
- Understand traveler preferences and demographics in your market
- Discover opportunities for new properties or amenities
However, scraping data from Booking.com presents some unique challenges. The site uses dynamic loading, meaning much of the content is populated via JavaScript after the initial page load. It also has anti-bot measures in place, like IP tracking and CAPTCHAs. A simple script that downloads the HTML won‘t get you very far.
In this guide, we‘ll walk through a robust process for scraping hotel data from Booking.com using Python. By the end, you‘ll be able to extract key details like:
- Hotel name and location
- Room types and prices
- Ratings and review counts
- Amenities and facilities
- Availability for given dates
We‘ll use popular libraries like Requests, BeautifulSoup, and Selenium to build a scraper that can handle the dynamic nature of Booking.com and scale to extract data on thousands of hotels. Let‘s dive in!
Setting Up Your Scraping Environment
Before we start writing any code, you‘ll need to set up your Python environment for web scraping. We recommend using Python 3.6+ and a virtual environment to keep your dependencies isolated.
First, create and activate a new virtual environment:
python -m venv env
source env/bin/activate # On Windows, use `envScriptsactivate`Then, install the libraries we‘ll be using:
pip install requests beautifulsoup4 selenium lxmlHere‘s a quick overview of what each library does:
- Requests is a simple HTTP library for downloading web pages
- BeautifulSoup is used for parsing and extracting data from HTML
- Selenium automates web browsers, which we‘ll use to handle dynamic pages
- lxml is a fast HTML parser that BeautifulSoup can use
With your environment ready, let‘s start building our Booking.com scraper!
Analyzing the Booking.com Website
Before writing any scraping code, it‘s crucial to familiarize yourself with the structure of the website you‘re targeting. Let‘s take a look at a typical hotel listing page on Booking.com:
[Insert screenshot of Booking.com hotel page]There‘s a lot of information here, but the key pieces we want to extract are:
- Hotel name
- Address
- Price
- Rating and review count
- Amenities
- Room types and availability
Inspecting the page source, we can see that most of this data is not present in the initial HTML. Instead, it‘s loaded dynamically via JavaScript. This means that tools like Requests and BeautifulSoup alone won‘t be sufficient – we‘ll need to use Selenium to fully render the page before parsing it.
Another consideration is the URL structure. A typical Booking.com URL for a hotel looks like:
https://www.booking.com/hotel/us/the-statler.htmlBreaking this down:
www.booking.comis the domain/hotel/specifies that we‘re looking at a hotel (as opposed to an apartment, resort, etc.)usis the country codethe-statleris a slug based on the hotel name.htmlis the file extension
To build a complete scraper, we‘ll need to generate these kinds of URLs to access hotel listing pages. We can do this by making an initial search on Booking.com and extracting hotel links from the results.
Scraping Hotel Links from Search Results
To get a list of hotels to scrape, we‘ll start by performing a search on Booking.com. Let‘s use New York City as an example location.
We can make this search request using Selenium. First, we need to set up a WebDriver:
from selenium import webdriver
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import WebDriverWait
from selenium.webdriver.support import expected_conditions as EC
driver = webdriver.Chrome() # Assumes you have chromedriver installedThen, we navigate to the Booking.com search page and fill in our query:
driver.get("https://www.booking.com")
search_box = driver.find_element_by_id("ss")
search_box.send_keys("New York")
search_button = driver.find_element_by_class_name("sb-searchbox__button")
search_button.click()
# Wait for search results to load
results = WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.ID, "search_results_table"))
)This code navigates to Booking.com, enters "New York" into the search box, clicks the search button, and waits for the results to load.
Once the results are loaded, we can parse the HTML using BeautifulSoup to extract hotel links:
from bs4 import BeautifulSoup
soup = BeautifulSoup(driver.page_source, "lxml")
hotel_links = []
for link in soup.select("#search_results_table .hotel_name_link"):
hotel_links.append(link.get("href"))
print(f"Found {len(hotel_links)} hotels in New York.")This uses a CSS selector to find all hotel name links in the search results, and extracts the href attribute which contains the relative URL for each hotel.
We now have a list of hotel URLs that we can use for more detailed scraping. Let‘s move on to extracting data from an individual hotel page.
Scraping Hotel Details from a Listing Page
With a list of hotel URLs in hand, we can now navigate to each one and extract relevant data points. Here‘s a function that takes a hotel URL and returns a dictionary of scraped data:
def scrape_hotel_page(url):
driver.get(url)
# Wait for data to be dynamically loaded
name = WebDriverWait(driver, 10).until(
EC.presence_of_element_located((By.ID, "hp_hotel_name"))
)
# Extract data using BeautifulSoup
soup = BeautifulSoup(driver.page_source, "lxml")
data = {
"name": soup.select_one("#hp_hotel_name").text.strip(),
"address": soup.select_one("#hp_address_subtitle").text.strip(),
"price": soup.select_one(".price").text.strip(),
"score": soup.select_one(".js--hp-scorecard-scoreword").text.strip(),
"reviews": soup.select_one(".score_from_number_of_reviews").text.strip(),
}
# Get amenities
data["amenities"] = []
for amenity in soup.select(".hp_desc_important_facilities .important_facility"):
data["amenities"].append(amenity.text.strip())
# Get room types
data["room_types"] = []
for room in soup.select("[data-room-id]"):
data["room_types"].append(
{
"name": room.select_one(".hprt-roomtype-icon-link").text.strip(),
"price": room.select_one(".prco-valign-middle-helper").text.strip(),
"occupancy": room.select(".hprt-occupancy-occupancy-info")[0].text.strip(),
}
)
return dataThis function:
- Navigates to the given hotel URL using Selenium
- Waits for the hotel name to be dynamically loaded
- Extracts the name, address, price, score, and number of reviews using BeautifulSoup selectors
- Extracts a list of amenities
- Extracts a list of room types, each with a name, price, and occupancy
By running this function on each hotel URL, we can compile a structured dataset of hotel details.
Scaling and Performance
Scraping a large number of hotels can be time-consuming, as each page requires rendering with Selenium. To speed things up, we can use parallel processing to scrape multiple pages at once.
Python‘s multiprocessing library makes this straightforward. We can create a pool of worker processes, each running its own Selenium instance:
import multiprocessing as mp
pool = mp.Pool(processes=4) # 4 parallel processes
results = pool.map(scrape_hotel_page, hotel_links)This divides the list of hotel links among 4 worker processes, which each scrape their assigned pages in parallel. The results variable will contain a list of dictionaries, one per hotel.
We can further optimize performance by using headless mode for Selenium, which avoids the overhead of rendering a visible browser window:
options = webdriver.ChromeOptions()
options.add_argument("--headless")
driver = webdriver.Chrome(options=options)With these optimizations, we can scrape hundreds or even thousands of hotel pages in a reasonable amount of time.
Storing and Using the Scraped Data
Once we‘ve scraped data on a set of hotels, we‘ll want to store it in a structured format for later analysis. A CSV file is a simple option:
import csv
keys = results[0].keys()
with open(‘hotels.csv‘, ‘w‘, newline=‘‘) as output_file:
dict_writer = csv.DictWriter(output_file, keys)
dict_writer.writeheader()
dict_writer.writerows(results)This code extracts the keys (column names) from the first result dictionary, then writes a header row and all data rows to a CSV file.
For more complex analyses, you may want to use a database like SQLite or PostgreSQL, or a data analysis library like Pandas.
Some examples of analyses you could perform on this hotel data:
- Calculate average prices and ratings by city or region
- Identify the most common amenities for high-rated hotels
- Compare prices for similar room types across competitor hotels
- Track price fluctuations over time for seasonal insights
- Combine with external datasets like weather or events for deeper insights
The possibilities are endless! Just remember to use your scraped data ethically and respect the terms of service of your data sources.
Legal and Ethical Considerations
While the data on Booking.com is publicly accessible, scraping it in large quantities may raise some legal and ethical concerns.
Before scraping any website, you should always check its robots.txt file, which specifies rules for what automated bots are allowed to access. You can find Booking.com‘s robots.txt at:
https://www.booking.com/robots.txtAs of 2024, this file does not explicitly disallow scraping. However, Booking.com‘s terms of service state:
The Website and Apps are protected by copyright as a collective work and/or compilation, pursuant to U.S. copyright laws, international conventions, and other intellectual property laws. The Content (including without limitation the Bookings.com Software) is the exclusive property of Booking.com and/or its licensors. You may not copy, reproduce, modify, create derivative works from, distribute or publicly display any Content without the prior written permission of Booking.com.
This suggests that, while scraping small amounts of data for personal use may be acceptable, large-scale commercial scraping could be seen as a violation of Booking.com‘s terms.
Additionally, any scraped data may be subject to privacy regulations like the GDPR. Be sure to anonymize personal data where appropriate and provide clear notices about how scraped data will be used.
As an ethical scraper, you should also ensure that your scraping does not place undue burden on Booking.com‘s servers. Limit your request rate, and consider caching results to avoid repeated requests for the same data.
Conclusion
Booking.com is an incredibly rich source of data for anyone in the travel industry. By carefully scraping and analyzing this data, you can gain valuable insights into market trends, competitor strategies, and consumer preferences.
In this guide, we‘ve covered the basics of scraping hotel data from Booking.com using Python, Selenium, and BeautifulSoup. With these tools, you can extract details like prices, ratings, amenities, and room types at scale.
Just remember to scrape responsibly and respect the website‘s terms of service. Happy scraping!