Scraping Zillow, Amazon, and Reddit with 3 Lines of Python (No Browser, No Blocks)

Zillow blocks scrapers. Amazon detects bots in milliseconds. Reddit rate-limits anything that isn't a human clicking slowly.

These are the three sites developers most want to scrape — and the three sites that are hardest to scrape reliably.

This post shows you how to scrape all three with 3 lines of Python each. No Playwright. No Selenium. No proxy rotation code. No CAPTCHA solving logic. Just clean data.

Why Scrapers Fail on These Sites

Zillow: JavaScript-rendered listings, Cloudflare protection, and aggressive bot fingerprinting. A raw requests.get() returns a 403 before it even loads the page.

Amazon: Product pages require residential IPs. Cloud IPs (AWS, GCP, Azure) are blacklisted. They also serve fake "bot detected" pages that look like real HTML but contain no product data.

Reddit: Rate-limits at the HTTP level, serves CAPTCHAs to anything that looks automated, and their official API now costs money for anything above toy usage.

The common thread: all three detect non-human traffic patterns and block them at the network level.

The Fix: Residential Proxies + Clean Output

ProxyClaw routes requests through real residential IPs (2M+ across 195 countries), handles CAPTCHA solving automatically, and returns clean Markdown instead of raw HTML.

pip install iploop-sdk

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Get a free API key at proxyclaw.ai — 0.5GB free, no credit card.

Scraping Zillow

3 lines:

```
from iploop import ProxyClawClient

client = ProxyClawClient(api_key="your_api_key")
result = client.fetch("https://www.zillow.com/homes/for_sale/New-York_rb/", format="markdown")
print(result.content)
```

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Output: Clean Markdown with listing addresses, prices, bed/bath counts, and square footage. No HTML parsing. No BeautifulSoup selectors that break every time Zillow redesigns their site.

Getting Structured Data

```
result = client.fetch(
"https://www.zillow.com/homes/for_sale/New-York_rb/",
format="json",
extract="listings"
)

for listing in result.data["listings"]:
print(f"{listing['address']} — ${listing['price']:,} — {listing['beds']}bd/{listing['baths']}ba")
```

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Scraping Amazon

Product pages, reviews, pricing — 3 lines:

```
from iploop import ProxyClawClient

client = ProxyClawClient(api_key="your_api_key")
result = client.fetch("https://www.amazon.com/dp/B09X7CRKRZ", format="markdown")
print(result.content)
```

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You get the product title, description, features, pricing, and top reviews in clean Markdown. No fake "Robot Check" pages. No blank HTML shells.

Price Monitoring Example

```
import json
from iploop import ProxyClawClient

client = ProxyClawClient(api_key="your_api_key")

def get_amazon_price(asin: str) -> dict:
result = client.fetch(
f"https://www.amazon.com/dp/{asin}",
format="json",
extract="product"
)
return {
"asin": asin,
"title": result.data["title"],
"price": result.data["price"],
"rating": result.data["rating"],
"review_count": result.data["review_count"]
}

asins = ["B09X7CRKRZ", "B08N5WRWNW", "B07ZPKN6YR"]
prices = [get_amazon_price(asin) for asin in asins]
print(json.dumps(prices, indent=2))
```

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Run this as a cron job, store to a database, and you have a price tracker with zero browser overhead.

Scraping Reddit

Threads, comments, upvotes — without the API costs:

```
from iploop import ProxyClawClient

client = ProxyClawClient(api_key="your_api_key")
result = client.fetch("https://www.reddit.com/r/MachineLearning/top/?t=week", format="markdown")
print(result.content)
```

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Pulling Thread Comments

```
result = client.fetch(
"https://www.reddit.com/r/python/comments/1abc123/some_thread/",
format="json",
extract="comments"
)

for comment in result.data["comments"][:10]:
print(f"[{comment['score']} pts] {comment['author']}: {comment['body'][:200]}")
```

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Useful for sentiment analysis, trend detection, or feeding community discussion into an LLM pipeline.

Combining All Three: A Market Research Agent

```
from iploop import ProxyClawClient
from langchain_openai import ChatOpenAI
from langchain.schema import HumanMessage

client = ProxyClawClient(api_key="your_api_key")
llm = ChatOpenAI(model="gpt-4o")

def research_market(product_query: str, amazon_asin: str):
amazon = client.fetch(f"https://www.amazon.com/dp/{amazon_asin}", format="markdown")
reddit = client.fetch(
f"https://www.reddit.com/search/?q={product_query.replace(' ', '+')}&sort=top",
format="markdown"
)
context = f"## Amazon\n{amazon.content[:2000]}\n\n## Reddit\n{reddit.content[:2000]}"
response = llm.invoke([HumanMessage(content=
f"Summarize the market for '{product_query}': {context}"
)])
return response.content

print(research_market("wireless earbuds", "B09X7CRKRZ"))
```

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Three data sources. One LLM call. Zero blocked requests.

Performance Notes

ProxyClaw adds \~1-3 seconds of latency per request. For price monitoring, market research, and data pipelines, this is completely acceptable.

For high-frequency scraping, use the batch API:

```
urls = [
"https://amazon.com/dp/B09X7CRKRZ",
"https://amazon.com/dp/B08N5WRWNW",
"https://zillow.com/homedetails/123-main-st/12345_zpid/"
]

results = client.fetch_batch(urls, format="markdown")
for r in results:
print(r.url, "—", len(r.content), "chars")
```

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Free Tier

0.5GB free per month. No credit card. Roughly 5,000–10,000 page fetches — enough to build and test a full scraping pipeline.

Paid plans: $1.50/GB vs BrightData at $8–15/GB.

What's Next

Wire it into a full agent pipeline — ProxyClaw + LangChain memory + structured output parsers \= agents that can autonomously research, monitor, and report on anything on the web.

GitHub: github.com/Iploop/proxyclaw
Docs: iploop.io/docs