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Booking.com Full-Year Price Tracker

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Booking.com Full-Year Price Tracker

Booking.com Full-Year Price Tracker

moving_beacon-owner1/my-actor-2
Try for free

Pay $2.00 for 1,000 results

The Yearly Data Scraper is a powerful, easy-to-use tool designed to automatically gather comprehensive data from Booking.com.

Hotel Data Scraper

This project is a robust web scraping solution built with Playwright and Python, designed to extract detailed hotel data from various hotel booking pages. The tool scrapes information such as hotel names, check-in/check-out dates, and prices, and exports the data into an Excel file. Additionally, it integrates seamlessly with Apify for storing the extracted data in a dataset for further processing.

Key Features

  • Accurate Data Extraction: Scrapes hotel names, check-in/check-out dates, and prices for any provided hotel URL.
  • Date Navigation: Automatically navigates between calendar months to extract data for the required period.
  • Excel Export: Saves the extracted data to an Excel file for easy sharing and analysis.
  • Apify Integration: Pushes the extracted data to an Apify dataset, enabling cloud-based storage and further processing.
  • Error Handling and Retries: The scraper automatically retries failed attempts to ensure consistent data extraction.
  • Price Formatting: Cleans and formats prices, removing unnecessary characters such as currency symbols.

Benefits for You

  • Easy Data Access: Automatically extracts the data you need from multiple hotel booking pages.
  • Time-Saving: Eliminates manual data collection, saving time and improving efficiency.
  • Scalable: Can handle multiple hotel URLs in one run, ideal for large-scale data collection.
  • Reliable: Includes retry mechanisms to handle network errors and page loading issues.
  • Flexible: Can be easily customized to scrape additional data or integrate with other systems.
  1. Configure Input Data:

    Provide a list of hotel URLs in the startUrls key of the input. Here's an example:

    1{
    2  "startUrls": [
    3    "https://www.example.com/hotel1",
    4    "https://www.example.com/hotel2"
    5  ]
    6}
  2. Run the Scraper:

    Once the setup is complete, run the scraper The script will:

    • Extract data from each provided hotel URL.
    • Save the data to an Excel file (named with a timestamp for easy identification).
    • Push the data to the Apify dataset for cloud-based storage.

Output

  • Excel File: The extracted data will be saved in an Excel file (hotel_data_<timestamp>.xlsx).
  • Apify Dataset: All the extracted data will be pushed to your Apify dataset for further processing or storage.

Customization

  • You can adjust the extract_dates_and_prices function to scrape additional information, such as amenities or room details.
  • Modify the startUrls list to include any hotel URLs you wish to scrape.
Developer
Maintained by Community

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  • 10 monthly users

  • 0 No stars yet

  • >99% runs succeeded

  • Created in Oct 2024

  • Modified 3 days ago