What is Web Scraping and How to Use It?

If you need information from a website, you can use web scraping to extract data from that site.

Unlike the long and tedious process of manually collecting data, web scraping uses intelligent automation methods to obtain thousands or even millions of data sets in less time.

What is web scraping?

Web scraping is an automated method for obtaining large volumes of data from websites. This data is mostly unstructured HTML, which is then converted into structured data in spreadsheets or databases so it can be used in various applications. There are many different ways to scrape data from websites. These include using online services, dedicated APIs, and even writing your own code for web scraping from scratch. Many large sites like Google, Twitter, Facebook, StackOverflow, etc., have APIs that allow you to access their data in a structured format. This is the best option, but there are other sites that do not allow users to access large amounts of data in a structured way, or they simply aren't that technologically advanced. In such situations, using a web scraping method to find data on the site is the best approach.

Web scraping consists of two parts: a crawler and a parser. The crawler is an artificial intelligence algorithm that browses web pages in search of the required data, following links across the internet. The parser, on the other hand, is a special tool designed to extract data from a website. The design of the parser can vary greatly depending on the complexity and scale of the project, so that it can extract data quickly and accurately.

How do web scrapers work?

Web scrapers can extract all data from certain sites or only the specific data the user needs. Ideally, you should specify the required data so that the web scraper quickly extracts only that. For example, you might want to scrape an Amazon page to find available types of juicers, but you may only need data on different juicer models, not customer reviews.

So when a web scraper needs to scrape a site, it is first given the URLs. Then it loads the entire HTML code of those sites, and a more advanced parser can even extract all CSS and JavaScript elements. Then the parser extracts the necessary data from this HTML code and outputs it in a format specified by the user. Most often this is an Excel spreadsheet or a CSV file, but the data can also be saved in other formats, such as a JSON file.

Different types of web scrapers

Web scrapers can be categorized by many criteria, including self-built vs. pre-built, browser-based or software-based, cloud-based or local.

You can use self-built web scrapers, but this requires deep knowledge of programming. And if you want additional features in your web scraper, you will need even deeper knowledge. On the other hand, pre-built web scrapers are already created scrapers that can be easily downloaded and run. They also have more advanced options that can be configured.

• Browser extension Web Scrapers - these are extensions that can be added to your browser. They are easy to run because they are integrated with the browser, but they are also limited because of that. Any additional features beyond what the browser can do cannot be implemented in browser extension Web Scrapers. Software-based web scrapers, on the other hand, do not have such limitations because they can be downloaded and installed on a computer. They are more complex than browser-based web scrapers but have advanced capabilities not limited by the browser's framework.

Why is Python a popular programming language for web scraping?

It seems Python is in vogue right now! It is the most popular language for web scraping because it easily handles most processes. Additionally, it has numerous libraries built specifically for web scraping. Scrapy is a very popular open-source framework written in Python. It is ideal for web scraping as well as for extracting data via APIs. Beautiful Soup is another Python library that is excellent for web scraping. It creates a parse tree that can be used to extract data from HTML on a website. Beautiful Soup also has many functions for navigating, searching, and modifying these parse trees.

What is web scraping used for?

Web scraping finds many applications across various industries. Let's look at some of them!

1. Price monitoring
\nWeb scraping can be used by companies to gather data about their products and competing products to see how it affects their pricing strategy. Companies can use this data to set optimal prices for their products to maximize revenue.

2. Market research
\nWeb scraping can be used by companies for market research. High-quality data obtained in large volumes can be very useful for companies to analyze consumer trends and understand which direction the company should take in the future.

3. News monitoring
\nWeb scraping news sites allows companies to receive detailed reports on current news. This is especially important for companies that frequently appear in news feeds or rely on daily news in their daily operations.

4. Sentiment analysis
\nIf companies want to understand the overall consumer sentiment toward their products, sentiment analysis is essential. Companies can use web scraping to collect data from social networks such as Facebook and Twitter about the general sentiment toward their products. This will help them create products that people like and stay ahead of their competitors.

5. Email marketing
\nCompanies can also use web scraping for email marketing. They can collect email IDs from various websites through web scraping, and then send bulk advertising and marketing emails to all people who have those IDs.