![]() To scrape YellowPages search we'll be forming search URL from given parameters and then iterating through multiple page URLs to collect all business listings. We can see that when we submit a search request YellowPages takes us to a new url containing pages of results. 1× once we click find we are redirected to results page with 427 results As for, parsel, another great alternative is beautifulsoup package or anything that supports CSS selectors which is what we'll be using in this tutorial. These packages can be easily installed via pip command: $ pip install httpx parsel loguruĪlternatively, feel free to swap httpx out with any other HTTP client package such as requests as we'll only need basic HTTP functions which are almost interchangeable in every library. Optionally we'll also use loguru - a pretty logging library that'll help us keep track of what's going on. In this tutorial, we'll stick with CSS selectors as YellowPages HTML's are quite simple. parsel - HTML parsing library which will help us to parse our web scraped HTML files.httpx - HTTP client library which will let us communicate with 's servers.So, if you're located outside of the US you'll need a US-based proxy, VPN to access .Īs for code, in this tutorial we'll be using Python and two major community packages: To begin we first should note that is only accessible to US-based IP addresses. For more on scraping use cases see our extensive web scraping use case article Project Setup ![]() Not only that but YellowPages also contains review data, business images and service menus which can further be used in market analysis. All of this data can be used in various market and business analytics to acquire competitive advantage or leads. contains millions of businesses and their details like phone numbers, websites and locations.
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