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Sahani Rajapakshe
Sahani Rajapakshe

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Building an Automated Book Price Monitoring Scraper with Python

#webdev

In today’s data-driven world, automating data collection is an invaluable skill. Whether it’s monitoring competitor prices, aggregating market research, or just collecting information for analysis, web scraping plays a key role.

In this article, I’ll walk you through building a fully automated web scraper in Python that extracts book data — including titles, prices, stock availability, and ratings — from a demo e-commerce website. Along the way, I’ll cover how to handle pagination, category navigation, data cleaning, and even automate backups for historical data management.

Why Build a Web Scraper?

Manually checking hundreds or thousands of product pages is tedious and error-prone. Automating the process saves time, ensures accuracy, and enables data-driven decisions.

This project demonstrates practical skills in:

  • Web scraping with Python libraries
  • Handling multi-level navigation and pagination
  • Cleaning and structuring scraped data
  • Automating file management and backups
  • Preparing scripts for scheduled automation (e.g., weekly runs)

Tools Used

  • Python 3.x: The main programming language.
  • Requests: For sending HTTP requests to web pages.
  • BeautifulSoup: For parsing HTML and extracting data.
  • Pandas: For structuring data and saving it as Excel files.
  • shutil & os: For file and folder management.

Overview of the Scraper

The scraper navigates the site’s homepage to extract all book categories. It then loops through each category and its paginated pages, scraping details for every book:

  • Title
  • Price (in £)
  • Stock status
  • Rating

It handles real-world challenges like strange characters in price strings (which can cause errors), and automatically backs up old data files before saving the latest results.

Key Challenges and Solutions

Pagination & Category Navigation
Websites often organize products in categories and split listings into pages. The scraper uses loops and URL patterns to crawl through all pages dynamically, stopping when no more pages exist.

Data Cleaning
One tricky issue was an unwanted character (Â) in price strings, which broke the conversion to numbers. I fixed this by cleaning the strings before converting:

clean_price = price_text.replace('£', '').replace('Â', '').strip()
price = float(clean_price)
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Backup Automation
To keep data organized, the script moves previous Excel reports to a data_backup folder before saving the new file with the current date.

Scheduling the Scraper
Once your scraper runs flawlessly, automating it is the next step. Using Windows Task Scheduler (or cron on Linux/macOS), you can schedule weekly runs, ensuring your data is always fresh without manual intervention.

The Complete Script

Here’s a condensed version of the main scraper logic (full code available in my GitHub repo [link to your repo]):

What I Learned

  • Real-world scraping requires patience and problem-solving to handle edge cases and site structure.
  • Automation is crucial for maintaining up-to-date datasets.
  • Clean code and good file management help in scaling and maintaining projects.

Next Steps and Improvements

  • Adding logging and error handling for robustness.
  • Integrating email alerts for new or changed data.
  • Storing data in databases or cloud storage for easier analysis.
  • Building a simple dashboard to visualize price trends.

Conclusion

This project sharpened my skills in Python automation and web scraping while teaching me the importance of maintainability and automation in real projects. If you want to learn more or try it yourself, check out the full code on my GitHub: https://github.com/sahani94rajapakshe/book-price-scraper and Demo: https://youtu.be/cskb73iJlo4

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