FastPeopleSearch.com Data Extraction: Scrape Without Getting Blocked

⚠ No real data about a real person has been used in this article. Target URLs have been modified by hand to not reveal and personal information of a real person.

FastPeopleSearch.com lets you look up names, phone numbers, addresses, and public records (all in one place and completely free).

But scraping it is anything but simple.

The site is protected by Cloudflare, enforces strict anti-bot rules, and often won’t even load if you’re outside the US.

Even visiting the page is a challenge; scraping structured data requires serious tools and tactics.

In this short guide, we’ll show you how to bypass those restrictions and extract clean, structured information when web scraping with Python and Scrape.do.

Why Is Scraping FastPeopleSearch.com Difficult?

FastPeopleSearch is one of the most aggressive websites when it comes to blocking automated access.

Whether you’re running a basic Python script or a full-scale web scraping setup, you’ll likely hit a dead end for one of two reasons:

Georestricted to US IPs Only

The first and most immediate problem is geography.

FastPeopleSearch is completely locked down to US-based traffic.

If your IP isn’t coming from within the United States, the site won’t even let you see the homepage. It just drops a generic block page with no explanation.

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This makes it impossible to scrape the site from overseas without a reliable US proxy.

But even with the right IP address, you’re still far from done.

Protected by Cloudflare

FastPeopleSearch sits behind Cloudflare’s anti-bot firewall, which automatically analyzes every request before letting it through.

These protections go far beyond simple rate limits or blacklists.

Even if your request comes from a US IP, Cloudflare steps in.

It challenges your scraper with JavaScript puzzles, fingerprint checks, and most commonly, CAPTCHAs.

If you’re not rendering the page like a real browser (with all the required TLS fingerprints, header behavior, and timing), you’ll get stuck in a loop of unsolvable verification prompts.

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This means standard libraries like requests or basic proxy rotators are instantly flagged. You’ll need much more advanced tools just to get a 200 response let alone parse real data.

That’s why most scraping attempts on FastPeopleSearch fail before they even begin.

But there’s a solution:

How Scrape.do Bypasses These Blocks

Scrape.do solves both of FastPeopleSearch’s biggest challenges in a single request.

When you enable super=true add geoCode=us, your traffic is routed through clean residential IPs located inside the US removing the geoblock instantly. No need to manage proxy pools or IP rotation on your end. 🌐

Then, through its anti-bot and CAPTCHA bypass solutions, Scrape.do handles Cloudflare’s JavaScript challenges, bypasses CAPTCHA triggers, and mimics real user behavior with proper TLS fingerprints and dynamic headers.

The result?

You get a fully rendered, unblocked HTML page, ready to parse with BeautifulSoup.

Creating a Basic FastPeopleSearch.com Scraper

First things first; privacy.

This guide is for demonstration purposes, so we will not use real data from a real FastPeopleSearch result page. Instead, I’ve modified every detail on the target page below so it does not reveal any real information:

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When you’re giving it a go, don’t forget to do a random search and use the URL of that result page.

Prerequisites

We’ll be using Python for this guide, along with two libraries: requests for sending HTTP requests, and BeautifulSoup for parsing the returned HTML.

If you don’t have them installed yet, run:

pip install requests beautifulsoup4

You’ll also need an API key from Scrape.do, which you can get for free by signing up in <1min (no credit card required).

Sending a Request and Verifying Access

Once you have your token, it’s time to send your first request.

We’ll start by targeting a FastPeopleSearch profile page. The goal at this stage is simple: get a 200 OK response and confirm that the page is accessible and fully rendered.

Here’s how we do that using Scrape.do with geoCode=us and super=true:

import requests
import urllib.parse
from bs4 import BeautifulSoup

# Your Scrape.do API token
token = "<your_token>"

# Target URL
target_url = "https://www.fastpeoplesearch.com/john-doe"
encoded_url = urllib.parse.quote_plus(target_url)

# Scrape.do API endpoint (US-based residential proxies)
api_url = f"https://api.scrape.do/?token={token}&url={encoded_url}&super=true&geoCode=us"

# Send the request and parse HTML
response = requests.get(api_url)
soup = BeautifulSoup(response.text, "html.parser")
print(response)

If everything is working correctly, you should see this in your terminal:

<Response [200]>

This confirms we’ve successfully bypassed the location and Cloudflare protections. Now we’re ready to extract structured data from the page.

Extracting Name, City, and State

The profile page header includes the person’s full name followed by their location, usually formatted like this:

John Doe in Chicago, IL (Illinois)

This gives us everything we need for the name, city, and state in one string.

We locate this information using the element with id="details-header" and then split the text using " in " as a delimiter.

It’s a simple trick that avoids brittle CSS selectors and works reliably across profiles.

Here’s the code that will parse this section successfully:

# Extract name, city, state
header = soup.find("h1", id="details-header")
name, location = header.get_text(" ").strip().split(" in ", 1)
city, state = [part.strip() for part in location.split(",", 1)]

print("Name:", name)
print("City:", city)
print("State:", state)

Extracting Age

Just below the name and location, FastPeopleSearch displays the person’s age in a simple format like this:

Age 47

We locate it using the id="age-header" element, then strip the text and remove the "Age " prefix.

Here’s the code:

# Extract age
age = soup.find("h2", id="age-header").text.strip().replace("Age ", "")
print("Age:", age)

It’s straightforward, and it gives us a clean numeric string.

Extracting Address and Final Code

This one’s a bit more difficult:

The current address on FastPeopleSearch isn’t presented in a single clean line. It’s split across multiple tags inside a clickable <a> element, nested within the section identified by id="current_address_section".

If you try to extract it with .text.strip() or by targeting a specific tag, you’ll often get either missing parts or unwanted formatting.

That’s why we use stripped_strings which collects all visible text across nested elements and strips out any empty lines, whitespace, or junk markup.

By calling next() on it, we grab just the first meaningful line: the street address.

And with that, here’s the final version of the code that puts everything together:

import requests
import urllib.parse
from bs4 import BeautifulSoup

# Your Scrape.do API token
token = "<your_token>"

# Target URL
target_url = "https://www.fastpeoplesearch.com/john-doe"
encoded_url = urllib.parse.quote_plus(target_url)

# Scrape.do API endpoint - enabling "super=true" and "geoCode=us" for US-based residential proxies
api_url = f"https://api.scrape.do/?token={token}&url={encoded_url}&super=true&geoCode=us"

# Send the request and parse HTML
response = requests.get(api_url)
soup = BeautifulSoup(response.text, "html.parser")

# Extract name, city, state
header = soup.find("h1", id="details-header")
name, location = header.get_text(" ").strip().split(" in ", 1)
city, state = [part.strip() for part in location.split(",", 1)]

# Extract age
age = soup.find("h2", id="age-header").text.strip().replace("Age ", "")

# Extract address
addr = soup.find("div", id="current_address_section").find("a")
address = next(line for line in addr.stripped_strings)

# Print output
print("Name:", name)
print("Age:", age)
print("City:", city)
print("State:", state)
print("Address:", address)

And here’s all the data we’ve parsed in clean format:

Name: John Doe
Age: 47
City: Chicago
State: IL (Illinois)
Address: 756 E 91st St

Conclusion

FastPeopleSearch might be aggressive when it comes to anti-bot through very strict georestrictions and Cloudflare protection, but with Scrape.do, it’s another W for pro scrapers.

Need to scrape FastPeopleSearch?

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• Premium residential and mobile proxies world-wide 🌎
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