Organized by Group, Density and Density in Group.

r/dataisbeautiful banned me for posting about this

honestly I just spent the last 3 weeks trying to compile a clean historical pricing dataset for a machine learning project, and I swear every major real estate site is locked down like fort knox now.

I used to just run basic python scrapers overnight, but now you hit aggressive rate limits or those endless cloudflare loops after like 50 requests. It's exhausting. I literally had to route my whole pipeline through residential proxies just to keep the sessions alive long enough to pull the raw tables without my ip getting permabanned. Kinda insane how many hoops we have to jump through just to aggregate public info nowadays.

Anyway I finally managed to scrape about 800k rows across the east coast. Still cleaning up some messy geocoding stuff but I should be able to drop a mega link or github repo here by the weekend if anyone wants to play around with it. anyone else noticing how brutal scraping has gotten this year?

Free sample of Primark's product data: 1,000 products across 17 storefronts (UK, Ireland, US and 14 EU countries), taken from a full snapshot of 150k+ listings on 27 October 2026. A listing is one product in one country, and the same product ID is used everywhere, so you can compare what one item costs in each country.

HuggingFace: https://huggingface.co/datasets/votiakov/primark-products-sample

GitHub: https://github.com/votiakov/primark-dataset

Fields: local price and currency, crossed-out price, local title and description in each storefront's language, category, colour, materials, care instructions, image URLs, product URL, plus a separate sizes file with Click & Collect stock per size. CSV and Parquet, with a data dictionary.

Happy to answer questions about the data.