{"id":6431,"date":"2023-10-18T14:47:43","date_gmt":"2023-10-18T14:47:43","guid":{"rendered":"https:\/\/royadata.io\/blog\/?p=6431"},"modified":"2023-10-18T14:47:43","modified_gmt":"2023-10-18T14:47:43","slug":"yellow-page-scraper","status":"publish","type":"post","link":"http:\/\/royadata.io\/blog\/yellow-page-scraper\/","title":{"rendered":"Yellow Page Scraper 101: How to Scrape Yellow Pages Data with Python"},"content":{"rendered":"
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Scraping Yellow Pages is incredibly easy. To learn how to scrape contact details by coding a Yellow Page scraper or using an already-made tool, keep reading the below article.<\/p>\n<\/blockquote>\n

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<\/span>Yellow Pages Scraper and How to Scrape Yellow Pages<\/strong><\/span><\/h2>\n

Cold marketing<\/a> is still one of the major ways businesses search for prospective customers and clients. With the right procedure, you could convert a total stranger into a loyal customer and make money from him. But how do you know who to contact and for what? Traditionally, marketers look through business directories in papers or specialized books such as Yellow Pages in search of contact details of prospective businesses.<\/p>\n

Usually, the businesses listed didn\u2019t get listed to get sold to but listed to gain more exposure and customers. However, the world has gone digital, and business directories in paper format are fading away, giving way to e-business directories.<\/p>\n

The introduction of e-business directories such as Yellow Pages and Yelp has made searching for businesses easy and swift. Interestingly, it also opens up opportunities for marketers to quickly collect contact details and other business information from these directories.<\/p>\n

Unfortunately, it does not come easy. Business directory websites wouldn\u2019t willingly hand over their contact with any random person on the Internet. You will have to extract it yourself, and when you need to extract many listings, doing so manually won\u2019t be effective and efficient. You will need to do so via web scraping.<\/p>\n\n

<\/span>Yellow Pages Scraping \u2013 an Overview<\/strong><\/span><\/h2>\n
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How do Yellow Pages scraping works?<\/p>\n<\/blockquote>\n

A computer program known as a web scraper is used to visit the pages with the business listing and then extract listing data of interest in an automated manner. The web scraper<\/a> downloads the HTML of the page, parse out required data<\/a>, and save it in an accessible format. That\u2019s how simple it is, scraping Yellow Pages fundamentally. Unfortunately, the process is not as easy and straightforward as mentioned. Yellow Pages do not like being scraped, and as such, they put up a defense in the form of anti-scraping systems to discourage scraping.<\/p>\n

However, the defense is only effective for the non-techies that do not know how to bypass them. It turns out that you could easily scrape Yellow Pages even with the anti-scraping systems. All you need to do is use rotating proxies to make IP tracking and blocking<\/a> useless in other to exceed the request limit and use Captcha solver in case you are forced to solve Captchas.<\/p>\n

When it comes to the choice of web scraper for scraping Yellow pages, you can either develop one for yourself or use an already-made one in the market. Generally, only coders can develop a scraper as developing one requires coding skills since they are written in computer programming languages.<\/p>\n

If you do not know how to code, then the best option for you is to make use of an existing scraper. In this article, we will be showing you how to code your own Yellow Pages scraper and all you need to know. We will also be recommending some of the best Yellow Pages scrapers already in the market for you to use.<\/p>\n\n

<\/span>How to Scrape Yellow Pages Using Python, Requests, and Beautifulsoup <\/strong><\/span><\/h2>\n

As a coder, you will want to build your own Yellow Pages scraper and incorporate features you like. Yellow Pages isn\u2019t a difficult website to scrape. While it utilizes JavaScript, you can actually scrape data even without JavaScript enabled.<\/p>\n

And for this reason, you will not require any tool that will help you execute JavaScript and introduce complexity. You can use any programming language of your choice to code a Yellow Page scraper. In this example, we will be making use of Python<\/a> as it is arguably the most popular language used in coding web scraping bots.<\/p>\n

With language chosen, we move to the next step of choosing libraries to use. Our Yellow Pages Scraper is going to be simple and straightforward \u2013 a minimum viable product and a proof of concept, to be precise. We will be using the Requests library<\/a> for sending HTTP requests downloading HTML for the Yellow Pages.<\/p>\n

Beautifulsoup<\/a> will be used for parsing. For tutorials, proxies are not required as we will only be sending a few requests. However, I am surfing from a country that does not have access right to the Yp.com service, and as such, I can\u2019t access Yellow Pages from my location without a proxy server<\/a>. The reason I stated this is because you might be in the same situation as mine. However, I will be taken the proxy setup part from the code.<\/p>\n

Below is the code for a very simple Yellow Pages Scraper that extracts business details from a specific URL. Take note; we did not incorporate any anti-bot bypass technique<\/a> for simplicity.<\/p>\n

from bs4 import BeautifulSoup\n\nimport requests\n\nheaders = {'User-Agent':'Mozilla\/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit\/601.3.9 (KHTML, like Gecko) Version\/9.0.2 Safari\/601.3.9'}\n\nurl = 'https:\/\/www.yellowpages.com\/los-angeles-ca\/dentists'\n\nresponse= requests.get(url, headers=headers)\n\nsoup= BeautifulSoup(response.content,'lxml')\n\nfor item in soup.select('.v-card'):\n\nprint('----------------------------------------')\n\nprint(item.select('.business-name')[0].get_text())\n\nprint(item.select('.rating div')[0]['class'])\n\nprint(item.select('.rating div span')[0].get_text())\n\nprint(item.select('.phone')[0].get_text())\n\nprint(item.select('.adr')[0].get_text())\n\nprint('----------------------------------------')\n\nprint('')<\/pre>\n