Putting it all together, we have: def url_parse(): data = requests. soup = bs(data, parser)Īnd finally our function will return this soup object. Then, we will create a soup object that will parse this HTML content into readable text format.
We will use the requests library to fetch the HTML content from the URL and store it in a variable called data. Let’s define the function for fetching the HTML page. class StaticSiteScraper: def _init_(self): pass # A method for parsing the url def url_parse(self): pass # A method for fetching all links from the webpage def get_all_links(self): pass # A method for fetching all image links from the webpage def get_all_image_links(self): pass # A method for downloading images from the image links def download_images(self): pass 1. We will call this scraper by the name “StaticSiteScraper”. Let’s create the scraper class and define our methods. Now that we have listed the core functionalities of this scraper, let’s build the scraper step by step. A method for downloading images from the image links to your current directory. A method for fetching all image links from the webpageĤ. A method for fetching all links from the webpageģ. This method should create a custom object that could be used to access any web element on the given page.Ģ. Let’s list out the functions we require to build this scraper We will make use of OOP with python to create this scraper from scratch. And of course, we will try to scrape a static webpage as well. In this article, we will focus on creating a simple web scraper class using Beautifulsoup and Requests library that can scrape - by default, links, and images - any static webpage. Every web scraper should be made for a particular situation and should be modified according to the changes in the page to be scraped. Though there are multiple ways to collect data, in this article we will focus on web scraping techniques to collect data for our data science projects.īuilding a universal web scraper is neither feasible nor desirable. Photo by Ilya Pavlov on Unsplash Introductionĭata collection is one of the major steps that come after defining and analyzing a problem in a data science project.
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