We have an incredible amount of data available on the web, it is a rich resource for companies to conduct any type of research and get actionable insights. Web scraping or data mining comes in handy for businesses looking for efficient and effective ways to harvest web data. Either by honing Python skills or looking for a web scraping company, most of the data analytics work can be sorted out. Walking through this blog will allow you to understand the web scraping using Python and the tools required to get the best out of the world wide web. Let’s begin with understanding the behavior of dynamic web pages.
Scraping dynamic web pages
A dynamic website updates the content of the pages as and when the scroll button and load more options are used. This is used to reduce the load on the web page and for it to load faster by keeping the same layout each time a new page is accessed. These websites use Ajax to automatically load the content and HTML is used to wrap the text.
Dealing with Pagination
Pagination helps people consume large amounts of data into easily consumable information by splitting content into various pages for online surfers. There are multiple ways to create pagination, one way is by numbering the pages and providing infinite scrolling. Pagination definitely helps to improve the user experience but it makes web scraping even more difficult to conduct with native approaches.
If you are trying to get information from a paginated website for your business needs, approaching a web scraping provider like PromptCloud can be ideal. A DaaS company can help with covering dynamic to paginated web structures. However, if you are interested in doing this yourself, make sure you have the below pointers to keep in mind.
The approach to scan data for this particular instance requires creating a loop that can automatically click on the next button or the next arrow button by staying in the current page on the website. this method is most commonly used to traverse through pages. Using an Xpath syntax can help in identifying nodes and elements for locating the next page number immediately after scanning the current page.
Exploring infinite scrolling option
Load more is the specific button that comes up to trigger and render the content as you keep scrolling toward the bottom of the page. One way to scrape data in this space is by creating a pagination loop that clicks on the same button over and again. This loop keeps triggering until the load-more option disappears. This is set up using Ajax and Python takes over to scrape the website as a single page.
Approach for looping through pagination
Python is majorly used to scrape website data by importing all the required libraries like BeautifulSoup and Selenium. URL strings make connections for parsing available data from web pages. It then identifies and extracts data, tags, and nodes that carry valuable information. This process on one page gets documented and applied to all the other web pages simultaneously, saving manual effort and time.
Controlling the rate at which the data is crawled is most important for carrying a huge dataset requirement. As multiple IP triggers in a short span can blacklist the internet address. A web scraping company can help you with setting up random bursts of scraping at different times by taking little automated breaks in between crawls. This way, the website can avoid traffic and overbearing web performance.
Websites and public domains using Python and Selenium is the best tool for scraping dynamic webpages. However, you will still need to tell selenium what objects and elements to interact with, and for how long they need to interact. The page elements are identified using Xpath for finding a user element for extraction. Setting up crawlers accordingly can easily fix issues that dynamic websites bring out.