Many modern industries now rely on online data collection for forecasts, monitoring, analysis, and other use cases. And this is one of the major reasons behind the exponential increase in the demand for web scraping. The process involves retrieving data from the web, and with more than 38% of companies engaged in it, it makes complete sense to dive deeper into it. So, before moving on, let’s explore what web scraping is all about.
Web Scraping: The Process
Also known as web harvesting or data mining, web scraping is collecting and extracting data from a site or any online source. Once collected, data is further exported in the preferred format to be used or stored for future analysis or reference. Even simple copy-pasting web page content is considered scraping, but more technically, it is performed using automated software.
Essentially a script, also known as spider, crawler, or bot, the automated software visits a webpage to extract data and eases extensive tasks that may otherwise cost huge amounts of money and take in lots of time and resources.
Also, web scraping is difficult as many sites employ anti-scraping tricks, like IP blocking and CAPTCHA, to prevent users from obtaining available data. But there’s nothing to worry about, as there are other ways to master web scraping as a beginner. Learn more about web scraping through the step-by-step tutorial on the process below:
Choosing the Finest Programming Languages and Tools for Web Scraping
With primary uses like news monitoring, price monitoring, lead generation, price intelligence, and market analysis, web scraping makes it easier for businesses and individuals to use publicly available data to generate valuable insights and make smarter decisions. Since the process involves data searching, crawling, parsing, fetching, and reformatting to make it ready for presentation, using the right programming languages and tools is essential.
As a beginner, you need to know the basics of web data extraction, regardless of whether you are using a web scraper for data retrieval or outsourcing the project to an extraction partner. If you want to do web scraping manually, it requires building a web scraper using the best programming language.
And almost any programming language that you are familiar with will be the right fit for the project. Featured below is a step-by-step guide on web scraping for beginners using any programming language:
Step 1: Get Hold of the Targeted Sites for Scraping
Figuring out which sites you are looking to scrape is the first step of the process. For example, if you are investigating clothing apparel reviews, you may want to scrape useful data from sites like Elle, Amazon, and Macy’s.
Step 2: Examine the Targeted Sites
Even before coding the web scraper, identify what it needs to scrape. For this, you can right-click anywhere on the frontend site where you will find the “view page source” or “inspect element” option. This will give you the site’s backend code or the data that the scraper will read.
Step 3: Segregate Data You Are Looking to Extract
Suppose you look for clothing reviews on Macy’s and then recognize their location in the backend code. You may even find most browsers automatically highlighting chosen frontend content and its corresponding code at the backend. You need to identify the exclusive tags nesting or enclosing relevant content.
Step 4: Writing Required Code
Once you have found the proper nest tags, incorporate them into your preferred scraping tool to inform the bot where to look for data and what to extract. The process is usually done using Python libraries like Beautiful Soup and Urllib3, known for doing all the heavy lifting. Specify the exact types of data you want the scraper to extract and store. You may want details like clothing specifications, fabric, manufacturer, and rating per the example.
Step 5: Code Execution
In this second-lasting step of web scraping, the scraper requests web page access, starts with data extraction, and deconstructs the same as per written code.
Step 6: Storing Collected Data
You can use different formats to store extracted data, the most common being Excel formats. You can even run the code through the Python Regex module for cleaner data extraction that’s easier to read simultaneously.
So, these are the basic steps to scrape the web for required data, but the process requires proper knowledge of programming languages. The most popular language is Python. Also, you can use several open-source web scraping tools but remember they have limitations. That’s why most businesses outsource their web data scraping projects.
Are There Any Alternatives?
Of course, automated web scraping tools and software programs can help you obtain the required information from web pages. But this might have errors; plus, the task is laborious and time-consuming if you extract data from many web pages. Therefore, an easy alternative is purchasing web data from data service providers who will extract the same on your behalf.
At the End
If you are interested in web scraping but do not know where to begin, the step-by-step tutorial above can help. There are even several free data scraping solutions, like simple point-and-click scraping services and developer-focused apps with extensive management and configuration options. Ultimately, the best approach to web scraping depends on your coding expertise and the project.
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