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The outburst of information on the internet is a boon for data enthusiasts. This available data is rich in variety and quantity, much like a secret box of information, waiting to be discovered and put to use. Say for example you need to take a vacation, you can scrap a few travel websites, imagine the possibilities, you can pick up travel recommendations of places to visit, popular places to eat, and read all positive feedback from previous visitors. These are a few options, but the list is endless.
How do you extract this information from the internet, is there a fixed way to get information, concrete steps to follow? Not really, there is no fixed methodology. The internet has a lot of unstructured and noisy data, to make sense of this overload of information, you need to use Web Scrapping. It is believed that almost any form of data on the internet can be scrapped, and there are different kinds of web scrapping techniques, each available to tackle different scenarios.
web_scraping
Why Python? as is common knowledge to all, Python is an open source programming language, hence you will find many libraries to perform one function. It does not mean that you will need to learn every library, but you will need to know how to put in a request, to communicate effectively with the website.

Here are 5 Python Web Scrapping Libraries you can use

  1. Requests – It is a simple and powerful HTTP library; you can use it to access web pages. It can access API, post to forms and much more.
  2. Beautiful Soup 4 (BS4)– It is a library that can use different parsers. A Parser is essentially a program that is used to extract information from XML or HTML documents. It can automatically detect encodings, which means you can manage HTML documents with special characters. It has the ability to navigate a parsed document easily, thus making it quick and easy to build a common application.
  3. Lxml – It has great performance and production quality. Initially, it was believed that if you need speed then you should use lxml, and for managing messy documents you should use BeautifulSoup, however that is no longer true, it's vice-versa now, BeautifulSoup can also support lxml parser. Therefore, it is recommended that you try both and settle on the one convenient to you.
  4. Selenium – So Requests is generally used to scarp a website, however, there are some sites that us JavaScript for its content. These sites need something more powerful, Selenium is a tool which can automate browsers, and it also has Python bindings for controlling it right from your application. All this makes it ideal to integrate it with your chosen parsing library.
  5. Scrapy –it can be considered as a complete web scraping framework. It can be used to manage requests, store user sessions, follow redirects and manage output pipelines. What is phenomenal is that you can reuse your crawler, scale it by swapping Python web scraping libraries, like for example, use Selenium to scrap dynamic web pages, all while managing complex data pipelines.

To recap, you can choose from, Requests and Selenium to scrap HTML and XML from web pages, and you can use BeautifulSoup and lxml to parse into meaningful data and Scrappy to manage huge requirements and if you need to, build a web crawler.

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