Web scraping is the art of extracting data from a website in an automated and well-structured form. There could be different formats for scraping data like excel, CSV, and many more. Some practical use cases of web scraping are market research, price monitoring, price intelligence, market research, and lead generation. Web scraping is an instrumental technique to make the best use of publicly available data and make smarter decisions. So it’s great for everyone to know at least the basics of web scraping to benefit from it.
Now we have seen how the process of web scraping works. Let’s get started with coding,
In most cases, Colab comes with already installed third-party packages. But still, if your import statements are not working, you can get this issue resolved by installing few packages by the following commands,
It will display the output of the form,
Let’s try to understand this piece of code,
It gives a very long output; some of the screenshots are attached below.
One of the greatest things about Beautiful Soup is that it is built on the HTML parsing libraries like html5lib, html.parse, lxml etc that allows Beautiful Soap’s object and specify the parser library to be created simultaneously.
In the code above, we have created the Beautiful Soup object by passing two arguments:
Finally, soup.prettify() is printed, giving the parse tree visual representation from the raw HTML content.
Now it’s time to extract some of the useful data from the HTML content. The soup objects contain the data in the form of the nested structure, which could be further programmatically extracted. In our case, we are scraping a webpage consisting of some quotes. So we will create a program that solves these quotes. The code is given below,
Before moving further, it is recommended to go through the HTML content of the webpage, which we printed using soup.prettify() method and try to find a pattern to navigate to the quotes.
Now I will explain how we get this done in the above code,
If we navigate through the quotes, we will find that all the quotes are inside a div container whose id is ‘all_quotes.’ So we find that div element (termed as table in the code) using find() method:
The first argument in this function is that the HTML tag needed to be searched. The second argument is a dictionary type element to specify the additional attributes associated with that tag. find() method returns the first matching element. One may try table.prettify() to get a better feeling of what this piece of code does.
If we focus on the table element, the div container contains each quote whose class is quote. So we will loop through each div container whose class is quote.
Here the findAll() method is very useful that is similar to find() method as far as arguments are concerned, but the major difference is that it returns a list of all matching elements.
We are iterating through each quote using a variable called row.
Let’s analyze one sample of HTML row content for better understanding:
Now consider the following piece of code:
Further, we can also add, remove, modify and access tag’s attributes. We have done this by treating the tag as a dictionary:
Finally we will generate a CSV file, which will be used to save our data.
We have named our file inspirational_qoutes.csv and saved all the quotes in it to be used in the future also. Here is how our inspirational_quotes.csv file looks like,
In the output above, we have only shown three rows, but there are 33 rows in reality. So this means that we have extracted a considerable amount of data from the webpage by just giving a simple try.
Some of the real-world scenarios in which web scraping could be of massive use are,
Doing the right market research is the most important element of every running business, and therefore it requires highly accurate information. Market analysis is being fueled by high volume, high quality, and highly insightful web scraping, which can be of different sizes and shapes. This data can be a very useful tool for performing business intelligence. The main focus of the market research is on the following business aspects:
Web scraping can be a very handy and fruitful technique for creating the listings according to the business types, for example, real estates and eCommerce stores. A web scraping tool can help the business browse thousands of listings of the competitor’s products on their store and gather all the necessary information like pricing, product details, variants, and reviews. It can be done in just a few hours, which can further help create one’s own listings, thus focusing more on customer demands.
Web scraping helps various businesses gather and compare information and provide that data in a meaningful way. Let’s consider price comparison websites that extract reviews, features, and all the essential details from various other websites. These details can be compiled and tailored for easy access. So a list can be generated from different retailers when the buyer searches for a particular product. Hence the web scraping will make the decision-making process a lot easier for the consumer by showing various product analytics according to consumer demand.
Web scraping can help aggregate the information and display it in an organized form to the user. Let’s consider the case of news aggregators. Web scraping will be used in the following ways,
So in this article, we had an in-depth analysis of how web scraping works considering a practical use case. We have also done a very simple exercise on creating a simple web scraper in Python. Now you can scrape any other websites of your choice. Furthermore, we have also seen some real-world scenarios in which web scraping can play a significant role. We hope that you enjoyed the article and everything was clear, interesting and understandable.