Whitepages Scraper

Scrape Whitepages.com people search into structured data. Search a name (optionally in a state or city) and get every person on the result pages with their current address, phone numbers, age range, neighborhood, previous locations and relatives, then open person pages for associates and the rest. JSON or CSV.

pip install whitepages-scraper
export SCRAPEDO_TOKEN=your_token
whitepages-scraper --name "John Smith" --city Seattle --state WA --pages 2 -f csv -o people.csv

What is in the way

import requests

requests.get("https://www.whitepages.com/name/John-Smith/Seattle-WA")
# outside the US: the connection is dropped; from a datacenter IP: Cloudflare challenge

1. US only. Visitors outside the United States, datacenter IPs and most VPNs are dropped without an error page.
2. Cloudflare. Every page sits behind Cloudflare's bot checks, so a plain HTTP client gets a challenge instead of HTML.

This package fetches every page through Scrape.do with super=true&geoCode=us (a US residential IP): about 3 seconds and 10 credits per page.

Where the data is

Whitepages renders on the server and publishes the people on each page as schema.org JSON-LD:

• Search pages carry an ItemList with up to 25 Person items: full name, the current street address, phone numbers, Age Range, Neighborhood and Previous Location properties, relatives (relatedTo) and the person page URL. The visible cards mask phones ("(206) *** ****"); the JSON-LD has the full number. In a live test, one Seattle search page had 25 people, 25 addresses and 15 phone numbers in one request.
• Person pages carry one Person with phones under contactPoint, relatives and associates (knows). The address is often only in the HTML (.address-line1, .address-line2) and the age range in the text ("is in their 60s"); the parser reads both.
• Pagination is ?page=2, ?page=3, 25 people each.

Ages are ranges ("40s", "80+"), never exact. Free pages show the current phone at most; phone history, emails and background reports are behind Whitepages' paid tier and are not scraped.

Python

from whitepages_scraper import WhitepagesScraper

wp = WhitepagesScraper(token="YOUR_SCRAPEDO_TOKEN")  # or set SCRAPEDO_TOKEN

people = wp.search("John Smith", city="Seattle", state="WA", pages=2)
for p in people[:3]:
    print(p.name, p.age_range, p.address, p.city, p.phone, p.relatives)

person = wp.get_profile(people[0].url)
print(person.associates, person.phones)
print(wp.credits_used)

Command line

# Name search: 25 people per page, no extra requests
whitepages-scraper --name "Mary Johnson" --state TX --pages 2 -f csv -o mary.csv

# Open every person page found (one more request each)
whitepages-scraper --name "John Smith" --city Seattle --state WA --profiles --limit 10

# Person page URLs
whitepages-scraper "https://www.whitepages.com/name/<Name>/<City-ST>/<id>"

Fields

name, first_name, middle_name, last_name, age_range, address, city, state, zip_code, neighborhood, phones, aliases, relatives, associates, previous_locations, url.

Use it responsibly

This is personal data about real people. Whitepages is not a consumer reporting agency: don't use this data for decisions about employment, housing, credit or insurance (the FCRA regulates those in the US). Honor opt-out requests, follow the privacy laws that apply to you (CCPA, GDPR) and Whitepages' terms, and keep request rates modest.

Also available

• No-code: Whitepages Scraper on Apify
• Browser demo: Hugging Face Space
• Full guide: Whitepages.com Scraping: Bypass Cloudflare and Extract People Data

Tests

python -m unittest discover -s tests

The fixtures are fictional people (Jane Doe, 555 numbers); the tests never hit the network.

License

MIT