10 Introduction to Web Scraping
In the previous chapters, you worked with data from APIs, databases, and CSV files—sources that deliver clean, structured payloads you can immediately load into a DataFrame or insert into a table. But not all data is that cooperative. Sometimes the information you need lives on a web page, embedded in HTML designed for humans to read, not for pipelines to process. In this chapter, you will learn how to extract data from web pages, a technique called web scraping, and build the foundation for the AI-powered data generation workflows you will construct in Chapters 11 through 13.
To ground this work in something practical, we introduce a use case that will carry through the rest of Part 3: building a product database for RuckZone, a company that helps people who go rucking. If you are not familiar with the term, rucking is the practice of walking or hiking with a weighted backpack, popular among military personnel, fitness enthusiasts, and outdoor adventurers. RuckZone wants to build a comprehensive database of every piece of apparel, tool, and equipment someone might need on their outdoor excursions. They have a general idea of what categories to include, but they need specifics: weight, color, size, cost, images, and detailed descriptions that match a strict data contract.