3 Python Web Scraping Projects You Can Do In 1 Weekend

You will rarely work with CSV data as a data engineer.

That’s why gaining exposure to and building comfort with dynamic datasets sourced through remote, “messy” sources like APIs and web pages is key to your professional development.

If you don’t want to go to the trouble of obtaining a web token or reading pages of documentation, web scraping is the next best thing.

By its nature, web data, nested in HTML tags and JavaScript snippets, is messy and insights-rich. Just think, for a second, about how many websites feature tables of data.

Or, how useful it would be to obtain and analyze unstructured data like the prices of items in your Amazon cart over time or real estate prices for a particular zip code.

This guide aims to present you with 3 common web scraping scenarios

• A single scrape of unstructured data
• A Single read of a table element
• Ingestion of multiple embedded tables

The sources you will examine are both accessible by any Internet user: Zillow (unstructured data) and Wikipedia (tabular data). The subject matter spans real estate, historic data and sport data.

We’ll examine hands-on examples so, by the guide’s conclusion, you’re as comfortable inspecting page contents as you are inspecting a data frame.

Build Your Pipeline To A Data Engineering Career

You’ve reached the limit of the public preview. The full version of this post includes the implementation details: The code, the edge cases, and the "why" behind the architecture.

When you join PipelineToDE, you get:

• The DA → DE Pathway Course: A structured roadmap to bridge the gap between analysis and engineering.
• Weekly Senior Deep Dives: Fresh, tactical insights on Python, Cloud (GCP/AWS), and modern orchestration delivered every week.
• Production-Ready Blueprints: Access to 80+ protected stories and code repos from my time in the trenches as a Senior DE
• The DE Job Board (Coming Soon): Exclusive access to a curated board of high-agency Data Engineering roles.