The amount of data on the internet has increased exponentially. In return, this has increased the demand for data analytics. As data analytics is very widespread, one needs to generate analytics from more than one resource. Therefore companies need to collect this data from a variety of resources.
Before getting into the details of web scraping, let’s start from scratch.
Web scraping is the art of extracting data from the internet in an automated fashion and then utilizing it for meaningful purposes. Let’s suppose you are copying and pasting the content from the internet into an excel file. This is also web scraping but on a very small scale.
Web scraping has now become a very diverse field and is done mostly through software. Most web scrapers consist of bots that visit the website and grab the relevant information for their users. By automating them, these bots can do the same job in a very short period. The data keeps on continuously updating, and it has many potential benefits in this fast-moving era.
The type of data to be scraped depends upon the organization. Common data types collection include images, text, product information, customer sentiments, pricing, and reviews.
When it comes to the uses of web scraping, it has a countless number of applications.
However, it should be noted that there could be dangerous consequences if web scraping is not done properly. Bad scrapers often collect the wrong information, which can ultimately leave very bad impacts.
Let’s now analyze how the web scraper works.
Now let’s go into the details of each step.
Whenever you visit a website, you make an HTTP request to that website. It’s just like knocking on the door and entering inside the house. Upon the request approval, you can access the information given on that website. Therefore, the web scraper needs to send an HTTP request to the site they are targeting.
Once the scraper successfully gains access to the website, the bot can read and extract the site’s HTML or XML code. The code analyzes the website’s structure. According to the analyzed code, the scraper will parse the code to extract the required elements from the website.
The final step involves saving the relevant data locally. Once the HTML or XML has been accessed, scraped, and parsed, it’s time to save the data. Data is usually in a structured form. For example, it is stored in different excel formats like .csv or .xls.
Once you are done with this job, you can further utilize data for your intended purposes. For example, one can generate different types of data analytics or analyze that information to generate sales, etc.
Now let’s see how to scrape the data in a step-wise manner.
The steps involved in web scraping depend upon the tool you are using, but we will briefly introduce the steps involved in it.
The first thing one needs to do is to figure out the websites of their choice. There is a variety of information present on the internet, so one needs to narrow down their requirements.
It is very important to know the page structure, like different HTML tags, etc., before getting started with the web scraping because you need to tell your web scraper what needs to be scrapped.
Let’s suppose you want to have the book reviews on Amazon. You will need to identify where it is located in the backend. Most browsers automatically highlight the selected frontend content with its corresponding backend. One needs to identify the unique tags that enclose or nest the relevant content.
Once you find the appropriate nested tags, you will need to incorporate them into your code. This will tell the bot what type of specific information you want to be extracted. Web scraping is most often done using Python libraries. One needs to specify explicitly the type of data types and information required. For instance, you might be looking for book reviews. Therefore you will need information like the book title, author name, and rating, etc.
The next step involves the code execution where the scrape requests the site, extracts the data and parses it accordingly.
After collecting and parsing the relevant information and data, the final step involves storing it. There are various formats in which the data can be stored, and it’s totally your choice what suits you the best. Different formats of excel are most common to store the data, but some other formats used are CSV and JSON.
In this article, we have seen the essentials of web scraping by diving into the basics, like what web scraping is and its different applications, by considering practical use cases. Furthermore, we have also gone into the depth of functionality of web scraping and the steps involved in scraping the web data. I hope this article was useful and will add more knowledge to the readers.
That was all for this one. See you in the next ones!