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In this script, I'm using Scrapy, a powerful web scraping framework, to extract real estate data from Zillow. By employing custom headers, I bypass the 403 error, ensuring smooth access to the website.
Here's a breakdown of the code:
1. Spider Setup: 🕷️
- Defined a Spider named `zilspider`.
- Set the starting URL to scrape data from the New York, NY area on Zillow.
2. Initiating Requests: 🚀
- The `start_requests()` function sends an initial request to the specified URL.
3. Parsing Data: 📜
- In the `parse()` method, the script locates a specific `Script` tag containing JSON data (`__NEXT_DATA__`).
- This JSON data holds information about the homes listed on the page.
4. Extracting Home Information: 🏡🔍
- For each home, various details are extracted, including:
Type of home 🏘️
Days on Zillow 🗓️
URL to the home’s page 🌐
Main Image 📸
Status of the listing 📊
Price 💰
Address 🏠
Zip Code 📍
Number of Beds & Baths 🛏️🛁
Area in Square Feet 📏
- This data is then yielded as output for further processing or storage.
5. Handling Pagination: 📄➡️
- The script checks if there's a next page of listings available.
- If so, it constructs the full URL for the next page and makes another request to continue scraping.
This script is a practical example of how to use Scrapy for web scraping, particularly for extracting structured data from complex web pages. Whether you're interested in real estate trends or just curious about property listings, this tool can help you gather the data you need! 🏡💼
🌟 Important: Always consider the ethical implications of web scraping. 🌟
💬 Feel free to contact me if you have any questions about web scraping! 💬
00:00 - Intro and explanations
02:16 - Setting our Scrapy project
04:34 - Checking the Zillow website to find data and show the pagination
19:55 - Handling 403 error by adding custom headers
26:10 - Scrapy shell and take NEXT_DATA Script and take first page data
39:46 - Implementing pagination
47:36 - What's the advantages of web scraping, maybe Outro!