How to Scrape Yellow Pages US Business Listings in Python (Phone, Address, Website)

YellowPages.com covers nearly every US local service business. With no public API, scraping is the standard approach for extracting structured lead data. Here is how to do it at scale.

What each listing contains

• Business name and primary phone number
• Full address: street, city, state, and ZIP code
• Business categories
• Website URL (when listed)
• Years in business (when shown)

Common use cases

Sales prospecting. Build phone and email lists for plumbers, HVAC contractors, or dental practices in a target metro. Call or email directly from the export.

Franchise location mapping. Pull every listing for a brand across a state to map penetration and gaps.

Local market research. Count competitors in a category by ZIP code or city to inform location expansion decisions.

Directory building. Aggregate listings for a niche vertical (pet groomers, escape rooms, craft breweries) to power a specialized local directory.

Using the Apify actor

import apify_client

client = apify_client.ApifyClient('YOUR_APIFY_TOKEN')

run_input = {
    "searchTerm": "plumbers",
    "location": "Austin, TX",
    "maxResults": 100,
}

run = client.actor('themineworks/yellowpages-us').call(run_input=run_input)

for biz in client.dataset(run['defaultDatasetId']).iterate_items():
    print(f"{biz['name']}")
    print(f"  Phone: {biz.get('phone', 'N/A')}")
    print(f"  Address: {biz.get('address', 'N/A')}, {biz.get('city')}, {biz.get('state')} {biz.get('zip_code', '')}")
    print(f"  Website: {biz.get('website', 'N/A')}")
    print(f"  Years in business: {biz.get('years_in_business', 'N/A')}")

Building a phone outreach list

import csv

run_input = {
    "searchTerm": "HVAC contractors",
    "location": "Dallas, TX",
    "maxResults": 500,
}

run = client.actor('themineworks/yellowpages-us').call(run_input=run_input)
results = list(client.dataset(run['defaultDatasetId']).iterate_items())

with_phone = [b for b in results if b.get('phone')]

with open('hvac_dallas.csv', 'w', newline='') as f:
    writer = csv.DictWriter(f, fieldnames=['name', 'phone', 'address', 'city', 'state', 'zip_code', 'website'])
    writer.writeheader()
    for biz in with_phone:
        writer.writerow({k: biz.get(k, '') for k in writer.fieldnames})

print(f"{len(with_phone)} businesses with phone numbers exported")

Multi-city search with deduplication

CITIES = ['Houston, TX', 'Dallas, TX', 'Austin, TX', 'San Antonio, TX', 'Fort Worth, TX']
TERM = 'electricians'

seen = set()
all_results = []

for city in CITIES:
    run = client.actor('themineworks/yellowpages-us').call(
        run_input={"searchTerm": TERM, "location": city, "maxResults": 200}
    )
    for biz in client.dataset(run['defaultDatasetId']).iterate_items():
        key = (biz.get('name', '').lower(), biz.get('phone', ''))
        if key not in seen:
            seen.add(key)
            all_results.append(biz)
    print(f"  {city}: {len(list(client.dataset(run['defaultDatasetId']).iterate_items()))} total")

print(f"Unique businesses across Texas: {len(all_results)}")

Filtering by years in business

Established businesses (10+ years) are often better credit risks and more stable customers:

established = [
    b for b in results
    if b.get('years_in_business') and int(b.get('years_in_business', 0)) >= 10
]

print(f"Established businesses (10+ years): {len(established)}")
for b in sorted(established, key=lambda x: int(x.get('years_in_business', 0)), reverse=True)[:10]:
    print(f"  {b['name']}: {b['years_in_business']} years | {b.get('phone', 'no phone')}")

Pricing

$0.002 per business returned. No charge on empty searches or failed runs.