Web scraping is an important domain of the real-time web that holds much promise for multiple fields. Real-time extraction/analysis of data can transform fields such as e-commerce, stock trading, travel, and healthcare. Businesses that can harness the power of web scraping can gain a massive competitive edge in the market.
Web scraping as a practice isn’t easy, but it is something that you can achieve with the right effort and tools. And no, you don’t need to be a coder or a software developer to do a web scraping project. Because the real-time ability to pull data from the web and make informed decisions from it is crucial for businesses.
In this quick article, we are going to explore what web scraping is and the endless possibilities you can get from such a project. Let’s dive in!
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The most important application of web scraping is the ability for businesses to draw real-time information and act on it. Let’s take an example of a person running a drop shipping or e-commerce business who needs to act on data from real-time prices and stock movements.
Let’s assume this person is ordering from Amazon or AliExpress. If the person had the real-time ability to scrap data from these two megastores, they could outwit their competition. This data could be in regard to prices, inventory, or even customer reviews. The person could use these real-time insights to adjust their prices accordingly, build supply, or even tailor offers.
However, e-commerce isn’t the only important application of web scraping. In finance, scraping could be used in fraud detection or even hourly stock prediction. Investments can be informed by macroeconomic indicators such as inflation and consumer spending. Leisure service providers can gain an edge through sentiment analysis, while customers can apply it to flight or hotel booking.
The list is endless when it comes to the potential of web scraping. As a college student or general data enthusiast, knowing how to web scrape will give you a competitive advantage over your peers. Companies will be sure to clamor for your knowledge as you provide this vital resource.
Now, let’s see what web scraping looks like in a practical sense.
There are several different ways to scrape data from websites and these depend on your requirements, scale, and preference.
With this method, you’ll need to copy-paste data from web pages into a spreadsheet. That’s suitable for small-time tasks, but if you need to scrap 10,000 products, it gets messy and tiresome quickly.
Some geniuses already went ahead of the curve and built web scraping tools and frameworks. These automate the scraping process for you. Such tools are usually proprietary. For example, Octoparse is a visual scraping tool while BeautifulSoup is a web scraper that hosts the Python library. Selenium is a scraper that’s mostly used for dynamic content.
With these tools, you can scrap data from web pages using CSS selectors, XPath, ASIN codes, or other methods.
If you’re lucky you’ll get a website that allows you to access their data in certain structured formats. This is more reliable and legal compared to scraping data from web pages directly. If a website doesn’t provide APIs as in most cases, you might encounter Robot.txt files, captcha, or even limited URL requests.
Direct scraping involves sending HTTP requests directly to a web server and then parsing the response to extract data. The response can be in HTML or JSON formats. You can use libraries such as Requests for Python and Axios for JavaScript to send the HTTP requests. Tools like BeautifulSoup can then be used to parse the response.
Tools such as BrightData and Scraperapi provide proxy servers and can also rotate IP addresses, thus making your HTTP requests seem random. This will help you move past Captchas and avoid IP bans if you plan on sending multiple HTTP calls.
Data marketplaces can provide access to pre-scraped data at a fee. However, this might have a limitation in terms of access to real-time data, and also the type of data available.
Keep in mind that scraping data can have legal ramifications by violating a website’s terms of service. You might also end up getting your IP permanently blocked. You can also affect the service, for example, by flooding the server with requests. This is especially where the website’s bandwidth and server resources are not clear.
Websites rendered using JavaScript are dynamic and often difficult to scrap using traditional methods. With a headless browser, you don’t need to access the JS-rendered visual content. Thus, you can focus on the data that truly matters. For example, if you need to scrap customer review data from a buy essay website running JavaScript, you can use a headless browser to view only HTML content.
Headless browsers include Puppeteer (Node.js) and Selenium WebDriver running Python, Java, etc. These can be used to automate interactions with a dynamic website.
If you choose to go the hard route, then the Pandas library for Python comes highly recommended. To achieve this, you’d need to first identify the HTML elements containing the data of interest and understand their structure.
Next, you would need to install Python on your system along with Pandas. Depending on the function, you can optionally install BeautifulSoup, which is a library used to parse HTML and XML docs and convert them into DataFrames. After the data has been extracted, it should be cleaned and transformed to prepare it for analysis.
This is just an overview of what the process looks like. You will need to adjust the tools that you use depending on your time, the scale of data, and available resources.
With web scraping, the internet is at your fingertips. Since these are usually complex projects, you will learn how to be resourceful, wrangle and organize data, and be great at automation.
More importantly, learning how to scrap will give you a competitive edge in the professional environment. This is a skill that is in high demand and holds a lot of ROI for businesses that deal in data. As you embark on your web scraping journey, be mindful of the limitations, risks, and ethical considerations. See you on the next one.
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