How does web scraping work, and how can you make the best use of your data gathering attempts? These are both important questions to ask, as it can be a tool that could be of great benefit for your business’ data collection attempts. Today, we will be looking to answer these questions so that you can make the best use of web scraping in Python.
Meaning of Web Scraping, and How Do Web Scrapers Work?
Before looking at how web scraping works, it’s first essential to have a solid understanding of what it is. We’ll touch on this briefly now, but we could go into pages about the topic alone – so make sure you’ve done some additional research into this topic before making any decisions!
In short, web scraping is the process of downloading structured data from the internet. You can use data extracted after the process for other activities, such as for analysis. It is an automated process and is often used by businesses looking for market research data. For example, this might include price intelligence and monitoring, as well as lead generation.
What is Web Scraping in Python Used For?
The most apparent reason why web scraping companies exist is for price comparison and data. By gathering data on the prices that competitor companies are charging for their products and services, a successful business can then use it to compare the cost of their services to that of the industry norm.
In the past, it was used on a small scale by individuals. However, the introduction of automation has made it easier for companies to source information and data where they need it. Indeed, the new automation of web scraping has allowed many businesses to revolutionize how they conduct market research!
How does It work?
Now that we’ve clarified the basics let’s go back to our original question of how is web scraping done?
Web scraping is an automated process, so the first thing to understand here is that it will run continually without needing input. In some ways, it is very similar to copying and pasting data from a website – except the automated nature allows it to be used on a much larger scale. As such, it can be used to extract data from a massive number of websites quickly and accurately, opening up access to a considerable amount of legally accessible information for businesses to consider.
The Two Components:
If you want to start web scraping in Python, you must understand the two primary entities involved in the process. These are the crawler and the scraper itself.
The crawler is an artificial intelligence that “crawls” the internet, searching for relevant content and websites. It can be used for either a single website, to find relevant URLs, or on a larger scale.
The second is the scraper. The scraper receives URLs and information from the crawler AI. It then uses this information to rapidly and accurately extract data from the URLs that were sent by the crawler. In doing so, they can then provide the relevant data and information for the user to view. They do this using data locator tools (sometimes these are referred to as data selectors) that extract the data selected from HTML files; these are usually made up from one, or a combination of, the following: XPath, CSS selectors, and regex.
The crawler and the scraper work alongside each other to extract and process the data from websites. As such, both components must be working effectively if the web scraping is to be successful.
The Web Scraping Process:
When it comes to understanding the question of “how scraping works,” it’s easiest to break this down into a few simple steps. This is the case, regardless of the software you use to develop your scraping tool.
- Identifying the target website and URLs. This is done by the web crawler in an automated system, although in a smaller system, it is possible to do it manually as well. The target URLs are the ones that will have data extracted by the web scraper itself.
- Request HTML data from the URLs. This is the second step in the process and involves requesting access to the website’s HTML code from the code owner. Not every website will necessarily want to make its HTML code publicly available. This is achieved by sending a GET request to the server.
- Locate data from within the HTML code. At this step, data is located from the HTML code by the web scraper tool. The Python library can be used at this time to search for the parse tree. This data is then saved in a JSON or CSV file where it can be used and analyzed.
What is BeautifulSoup?
Web scraping is made easy by using Python software. There are many different ways to go about this, such as the BeautifulSoup library, which probably is the most popular one.
Numerous web scraping libraries are available for use with Python. BeautifulSoup is one of them that can easily be installed and run. The program automates the process of opening and reading the HTML code. In addition to this, the software also serves to automatically clean tags and allows users to search for specific tags where needed.
How Does Web Scraping Process Work In A Nutshell
Web scraping services allows individuals and businesses to find and extract the data they need from websites. Often, it is used for activities such as monitoring prices with competitor companies and the like. If done correctly, it can be highly effective for allowing efficient and useful market research and monitoring.
Web scraping was mostly a slow and tedious process that was carried out manually by individuals in the past. However, modern web scraping in Python can allow businesses and users to automate collecting data, which, in turn, can allow data to be collected in real-time. To get started with our free consultation, contact us.