Build a Web Scraper (Python)

This weekend you and I are going to build a web scraper that actually pulls data off real pages and saves it to a file you can open in a spreadsheet. Not a toy that prints "hello" - a small program you'd be comfortable pointing at a catalog of books, a list of quotes, or a directory of listings, and walking away while it does the boring work.

You build this on your own machine. The code here is meant to be saved to files and run from your terminal with python script.py. There's no browser sandbox doing the work for you, because real scraping needs the network, the filesystem, and a couple of libraries that don't live in your browser. That's the honest version of the skill, and it's the version that's useful on Monday.

What you'll build

A command-line scraper, built up one piece at a time, that:

• fetches a web page over HTTP and checks it actually came back OK,
• parses the HTML into something you can search,
• pulls named fields (title, price, rating) into clean Python dictionaries,
• walks from one page to the next, with delays so you're a good guest,
• and writes everything to CSV and JSON.

By the last phase it's one working program. Each phase before that leaves you with a real, runnable piece of it.

The stack

We use a practice site built for exactly this - https://books.toscrape.com and https://quotes.toscrape.com. They exist so people can learn scraping without bothering anyone's production servers. We'll talk about why that matters in Phase 4.

Rough time

A focused weekend. Call it three to four hours if you type along. Phases 1–3 are the core and move quickly; Phase 4 is where the judgment lives; Phase 5 is the satisfying part where data lands in a file.

What you'll learn

• How an HTTP request and response actually work, in code you can read.
• How to find the elements you want using both BeautifulSoup's find family and CSS selectors - and when to reach for which.
• How to write extraction that doesn't crash the moment a field is missing.
• The etiquette and the law of scraping: robots.txt, rate limits, terms of service, and what "be polite" means in practice.
• How to persist structured data and where you'd take this next - a database, a schedule, or a headless browser for sites that build themselves with JavaScript.

The shape of it

graph LR
  A[Fetch page] --> B[Parse HTML]
  B --> C[Extract fields]
  C --> D[Next page?]
  D -->|yes| A
  D -->|no| E[Save CSV / JSON]

That loop is the whole game. Everything we build hangs off those five boxes. Let's get a machine ready and pull down our first page.