How to Scrape Google Hotels with Python: A Complete Guide

Web scraping Google Hotels can provide valuable data for travel analysis, price comparison, and market research. This comprehensive guide demonstrates how to effectively extract hotel listings and detailed information using Python and the CrawlBase Crawling API.

Setting Up Your Environment

Before diving into the scraping process, ensure your development environment is properly configured:

• Verify Python installation (version 3.7 or above recommended)
• Install the required libraries using pip
• Create new files for your scraping code
• Sign up on CrawlBase to obtain your API token

Understanding Token Types

CrawlBase provides two types of authentication tokens:

• Normal Token: Suitable for static websites
• JS Token: Required for JavaScript-rendered sites like Google Hotels

Scraping Google Hotel Listings

The process begins by examining the HTML structure of Google Hotels pages to identify key CSS selectors for hotel listings. Using Beautiful Soup alongside the CrawlBase Python library simplifies the extraction of essential information such as:

• Hotel names
• Prices
• Ratings
• Location details

When using the JavaScript token, you can leverage the Ajax Weight parameter to ensure all dynamic content loads before processing the HTML response.

Extracting Individual Hotel Details

Beyond basic listings, this technique also allows for deeper extraction of specific hotel information:

1. Inspect the HTML for detailed hotel pages
2. Identify relevant CSS selectors (noting they may vary by location and layout)
3. Build a dedicated scraper for hotel details using Beautiful Soup
4. Collect comprehensive information including amenities, reviews, and availability

Data Storage and Processing

Once data is extracted, it can be organized and saved as a structured JSON file, making it ready for further analysis or integration with other systems. The complete process involves:

1. Authenticating with the CrawlBase JS token
2. Executing the scraping script
3. Collecting data into organized structures
4. Saving output to JSON format

Practical Considerations

When implementing this solution, keep in mind:

• CSS selectors may change based on Google’s updates
• Always verify selectors in your browser before implementing
• Rate limiting and ethical scraping practices are important
• Respect robots.txt directives and terms of service

With this approach, developers can efficiently extract valuable hotel data from Google Hotels, enabling a wide range of applications in the travel and hospitality industry.