Data Harvest: Unlocking Insights with Python Web Scraping
summary
Builds a working apartment scraper with Scrapy that crawls paginated listings, extracts name price size and rooms, computes price per square meter, and writes JSON (local or S3). Shows concrete fixes that make it work: set a realistic user‑agent, enable pipelines to clean and compute fields, handle pagination by incrementing page numbers, and tune concurrency and proxies for scale. Demonstrates 200 responses after the settings change and a saved dataset.
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chapters (10)
- 0:00Introduction
- 2:11Web scraping for data insights
- 3:59Advantages of Scrapy
- 7:16Scrapy project setup
- 11:33Creating a spider
- 15:54Pagination and user agents
- 17:44Data processing with pipelines
- 21:39Scrapy framework benefits
- 22:43Scaling high-volume scraping
- 26:18Q&A
