Scraping Booking.com with ScrapeGraphAI: The Ultimate Guide

In today's competitive travel market, having access to real-time hotel data is crucial. Whether you're a travel blogger, a data analyst, or a competitive intelligence expert, scraping Booking.com can provide you with valuable insights. This guide will walk you through how to scrape hotel names from Booking.com using ScrapeGraphAI, featuring both Python and JavaScript examples.

What is Web Scraping and Why Scrape Booking.com?

Web scraping is the process of programmatically extracting data from websites. Booking.com, one of the largest travel booking platforms, offers a vast array of hotel information. By scraping this data, you can:

• Monitor Competitor Pricing: Understand how hotels are priced.
• Enhance Content Marketing: Use real-time data to create compelling travel guides.
• Boost SEO: Generate unique, data-driven content that attracts organic traffic.
• Improve Market Analysis: Stay updated on the latest trends in the hospitality industry.

Example of a Booking.com search results page showing available properties in Como, Italy

Introducing ScrapeGraphAI

ScrapeGraphAI is a cutting-edge tool that simplifies the web scraping process, allowing you to extract targeted data with minimal code. Its smart scraping capabilities enable you to gather specific information by simply providing a URL and a custom prompt.

Scraping Booking.com with Python

Below is a Python example that demonstrates how to extract all hotel names from a Booking.com search results page:

python
from scrapegraph_py import Client
from scrapegraph_py.logger import sgai_logger

sgai_logger.set_logging(level="INFO")

# Initialize the client
sgai_client = Client(api_key="sgai-********************")
# SmartScraper request
response = sgai_client.smartscraper(
    website_url="https://www.booking.com/searchresults.it.html?ss=Como&ssne=Como&ssne_untouched=Como&label=gen173nr-1BCAEoggI46AdIM1gEaHGIAQGYARS4AQfIAQ3YAQHoAQGIAgGoAgO4ArfIrL4GwAIB0gIkN2JlNjliZDQtZWUwYy00YjYwLWI2MzctNWUyNjc5M2YyZjFh2AIF4AIB&sid=505115fd9d590b6452f8c5b6fcdb37ee&aid=304142&lang=it&sb=1&src_elem=sb&src=index&dest_id=-116052&dest_type=city&checkin=2025-03-07&checkout=2025-03-08&group_adults=3&no_rooms=3&group_children=0",
    user_prompt="extract me all the hotel's names"
)

# Print the response
print(f"Request ID: {response['request_id']}")
print(f"Result: {response['result']}")

sgai_client.close()

Expected Output

The response from the above code snippet is a JSON object containing a list of hotel names:

json
{
  "hotels": [
    "New Generation Hostel Como Lake",
    "Victoria Royal Garden",
    "Palazzo La Cortesella",
    "Residence Diaz",
    "Le Stanze del Lago Apartments",
    "Karina House - Como",
    "Avenue Boutique Hotel",
    "Piazza Cavour - Luxury Lake View Apartment",
    "Hilton Lake Como",
    "B&B Confine",
    "Hotel Metropole Suisse",
    "B&B HOTEL Como Baradello",
    "CALICANTUS Apartments ATENA",
    "AcquaTerraFuoco - City apartments",
    "Acquaterrafuoco City Apartments",
    "Casa Elena B&B Como Lake",
    "Como Cathedral View Apartment with Garage",
    "Plinius Lifestyle Hotel Lake Como",
    "Alessia's Place",
    "Borgo Vico Luxury Suites",
    "Hotel Marco's",
    "DBH – Boutique Hotel Lake Como",
    "Le Stanze del Lago Lake View",
    "i FAGGI ROSSI - RED BEECH TREES",
    "Villa Il Platano"
  ]
}

Scraping Booking.com with JavaScript

For those who prefer JavaScript, here's how you can achieve the same using the ScrapeGraphAI JavaScript client:

javascript
import { Client } from 'scrapegraph-js';
// Initialize the client
const sgai_client = new Client("sgai-********************");

try {
  const response = await sgai_client.smartscraper({
    websiteUrl: "https://www.booking.com/searchresults.it.html?ss=Como&ssne=Como&ssne_untouched=Como&label=gen173nr-1BCAEoggI46AdIM1gEaHGIAQGYARS4AQfIAQ3YAQHoAQGIAgGoAgO4ArfIrL4GwAIB0gIkN2JlNjliZDQtZWUwYy00YjYwLWI2MzctNWUyNjc5M2YyZjFh2AIF4AIB&sid=505115fd9d590b6452f8c5b6fcdb37ee&aid=304142&lang=it&sb=1&src_elem=sb&src=index&dest_id=-116052&dest_type=city&checkin=2025-03-07&checkout=2025-03-08&group_adults=3&no_rooms=3&group_children=0",
    userPrompt: "extract me all the hotel's names"
  });

  console.log('Request ID:', response.requestId);
  console.log('Result:', response.result);
} catch (error) {
  console.error(error);
} finally {
  sgai_client.close();
}

SEO Best Practices for Your Scraping Blog

To ensure your blog post reaches its maximum potential:

• Incorporate Relevant Keywords: Use terms like "scraping Booking.com", "web scraping tutorial", "extract hotel names", and "ScrapeGraphAI".
• Engaging Headings: Structure your content with clear and descriptive headings.
• Quality Content: Provide detailed explanations, code examples, and real-world use cases.
• Internal & External Links: Link to authoritative sources, including ScrapeGraphAI's website and Booking.com, to enhance credibility.
• Mobile Optimization: Ensure your blog is mobile-friendly for a better user experience.
• Regular Updates: Keep your content fresh by updating code snippets and SEO tactics as technology evolves.

Conclusion

Scraping Booking.com data with ScrapeGraphAI is an efficient way to gather real-time hotel information. Whether you're using Python or JavaScript, this guide has provided you with the essential steps and code examples to get started. By integrating SEO best practices into your blog, you can attract more traffic and provide valuable insights to your audience.

Happy scraping and optimizing your content!