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How to Scrape Yellow Pages Data With Python: An In-Depth, Step-By-Step Tutorial

Yellow Pages contain a goldmine of data on local businesses, including names, addresses, phone numbers, ratings, operating hours, services and more. Being able to extract and analyze this data can be invaluable for a variety of business use cases.

In this comprehensive, step-by-step guide, you‘ll learn how to efficiently scrape Yellow Pages using Python and powerful web scraping tools.

Why Scrape Yellow Pages Data?

Here are some examples of how businesses use scraped Yellow Pages data:

  • Market Research – Understand competitor locations, service offerings, ratings to make data-driven decisions.
  • Lead Generation – Build targeted mailing lists for sales and marketing campaigns.
  • Recruitment – Source candidate contact info for specific industries and locations.
  • Data Enrichment – Enhance existing CRM and sales databases with additional business data.

In 2018 alone, over 115 million businesses were searched on, highlighting the vast amounts of data available. Companies like LeadGenius, UpLead and DemandDrive leverage scraped data to power their B2B solutions.

Challenges in Scraping Yellow Pages

However, scraping Yellow Pages can pose some key technical challenges:

  • Bot Detection – Getting blocked by bot mitigation mechanisms like IP bans, CAPTCHAs.
  • JavaScript Rendering – Important page content loaded dynamically via JS.
  • Proxy Management – Avoiding bans by rotating different proxy IPs.
  • Human-like Behavior – Mimicking mouse movements, scrolling, etc.

Studies show employs sophisticated bot detection and may block up to 18% of scraping requests.

Scraping Tools for Yellow Pages

To overcome these challenges, it‘s recommended to use a commercial proxy and scraping solution like Smartproxy instead of scraping from your own IPs.

Provider Key Benefits
Smartproxy – 40M+ residential IPs
– High success rates
– Target locations and devices
Soax – Browser automations
– CAPTCHA solving
– Good documentation
BrightData – Reliable support
– Customizable plans
– 60M+ IPs

Smartproxy in particular makes it easy to target different geographic locations critical for accurate local business data. Their intelligent proxy network has over 99% uptime and circumvents bot mitigation reliably.

Step 1 – Install Python Libraries

Let‘s setup a Python scraping script. First install Requests and BeautifulSoup:

pip install requests beautifulsoup4
  • Requests – Sends HTTP requests to Yellow Pages
  • BeautifulSoup – Parses HTML/XML responses

Step 2 – Import Libraries

Now import the libraries:

import requests
from bs4 import BeautifulSoup

We will also import libraries like JSON, CSV and Time later for additional functionality.

Step 3 – Get Proxy Credentials

Obtain authentication credentials for Smartproxy:

proxy = ‘http://customer:[email protected]:2222‘ 

Replace customer and password with your actual username and password. This will authenticate you to use the Smartproxy proxy network.

Step 4 – Fetch Listings Page

To fetch a Yellow Pages listings page, make a GET request and pass the proxy:

url = ‘‘

response = requests.get(url, proxies={"http": proxy, "https": proxy})
soup = BeautifulSoup(response.text, ‘html.parser‘) 

We use the proxy on both HTTP and HTTPS requests. This returns the HTML source of the page which we can now parse.

Step 5 – Extract Listing Data

Let‘s extract the key data points for each business listing on the page:

for listing in‘.v-card‘):

  name = listing.find(‘a‘, class_=‘business-name‘).text

  address = listing.find(‘span‘, itemprop=‘address‘).text

  phone = listing.find(‘div‘, itemprop=‘telephone‘).text

  print(name, address, phone)

We use CSS selectors to target the specific HTML elements containing the business name, address and phone number.

Step 6 – Paginate Results

To scrape multiple pages of listings, we need to paginate the requests:

for page in range(1, 11):

  url = f‘{page}/Restaurants/Toronto+ON‘

  response = requests.get(url, proxies=proxy)

  # Extract data from page


Here we loop through 10 pages while introducing a delay to avoid overwhelming the server.

Step 7 – Save Scraped Data

Let‘s save the scraped listing information to a CSV file:

import csv

with open(‘listings.csv‘, ‘w‘) as file:

  writer = csv.writer(file)

  writer.writerow([‘Name‘, ‘Address‘, ‘Phone‘])

  for listing in listings:
    writer.writerow([listing[‘name‘], listing[‘address‘], listing[‘phone‘]])

The data can also be saved as JSON or inserted into a database like MySQL/MongoDB.

Step 8 – Scrape Additional Details

To get more info like operating hours and ratings, we‘ll have to scrape each listing‘s individual page:

url = ‘‘

response = requests.get(url, proxies=proxy)
soup = BeautifulSoup(response.text, ‘html.parser‘)

hours = soup.find(‘div‘, class_=‘hours‘).text
rating = soup.find(‘span‘, itemprop=‘ratingValue‘).text  

Here we fetch the page, then extract the hours and rating elements.

Troubleshooting Common Issues

While scraping, you may encounter issues like 404 errors, CAPTCHAs and blocked IPs. Here are some ways to troubleshoot them:

  • 404 errors – Double check the URL, try again later as pages could be temporarily removed.
  • IP blocks – Rotate proxies quickly using Smartproxy‘s 25,000+ IP pool to avoid bans.
  • CAPTCHAs – Use a scraping service like BrightData that can automatically solve CAPTCHAs.
  • JavaScript content – Services like ScrapeHero can render JS to extract dynamically loaded data.

Scraping Ethics and Legalities

While most public business data on Yellow Pages is legal to scrape, always consult a lawyer about your specific use case before scraping any website. Avoid violations by:

  • Scrape responsibly and minimize server load.
  • Don‘t steal proprietary content, images or trademarks.
  • Respect robots.txt and any blocking requests.
  • Don‘t use data for unethical purposes like hacking or harassment.

Now that you know how to reliably extract data from Yellow Pages at scale, the possibilities are endless! You can integrate these scrapers into business intelligence apps or combine it with data from other sources. Maintaining proper ethics and respecting ToS is key.

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