The header of the official Selenium page goes like this We will be using Selenium to scrape data from the famous movie site Rotten Tomatoes for this article. There are many web scrappers out there, namely Scrappy, BeautifulSoup, Pyppeteer, and Selenium. As I do not have much knowledge regarding tools from other languages, we will only be talking about Python libraries. There are a lot of open-source web scraping tools out there that are being used to scrape data from internet sites. Many of the data sets you encounter are scrapped from some sites. Scrapers are automated bots or scripts that crawl over the web pages and find the relevant data. This is where the web scrappers come into play. Safer to say, more difficult than finding a needle in a humongous haystack. It is humanly impossible to go through this enormous amount of data and find the relevant ones. But out of all those data, we need exact data that can aid our business decisions. Every day internet generates a trillion megabytes of data every day. Decision-making entirely relies on data the more satisfactory the data better the outcomes. To keep Scraping Rotten Tomatoes relevant in a world driven by cutting-edge technology, data becomes ever so important. ![]() The world moves faster than we think, and technology is more quickly. This article is aimed at providing a basic knowledge of Selenium scraping. We will use Selenium, a web automation tool, as a scraper to scrape the data of interest from the said website. In this article, we will explore how we can scrape movie data such as movie names, directors, box office collections, and so on from one of the most popular websites, Rotten Tomatoes. ![]() As the internet dominates the current age, there is no better source for data gathering than the internet itself. It does not matter how and where the data is coming from as long as it is good quality. ![]() It is just that currently, we have a complete arsenal of tools that can efficiently fetch us data from different sources. Businesses have always been using data to make decisions to drive their profits up, capture more markets, and stay ahead of the curve. The data consistently have been fuelling the growth of ind ustries from time immemorial. This article was published as a part of the Data Science Blogathon.
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