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Have you thought about getting the discounted prices earlier? This blog discusses how to scrape the best deals data from discounts and promo code websites using web scraping on a smaller Raspberry Pi device.
Many Raspberry Pi projects are available online, and most of those projects need some electrical engineering. In our use cases, we have just used Raspberry Pi as a web scraping server, which works 24x7.
Python is the language with numerous powerful libraries that are easy to use and prototype new applications. We have used Python 3 here.
Scrapy is among the most acceptable open-source scraping frameworks of Python. Scrapy is powerful and fast and is the central part of our toolset. Though many new versions are available, the main mechanisms haven’t changed much. This blog uses the latest version of Python 3.6.10 and Scrapy 2.0.1.
A modern browser has developed tools that help inspect objects and scrape HTML tags very quickly.
Many websites offer promo codes and discounts, for instance, SlickDeals, DealMoon, and Dealnews. There would be different mechanisms on HTML to scrape, but choosing a website you are interested in is not reserved here. An essential part of data scraping to succeed is to select a website with sufficient traffic. In this blog, we have used SlickDeals as a scraping website.
1. Go to a website called SlickDeals.
2. The majority of great deals are available on the section named Frontpage Slickdeals. Every item has the given data, including product titles, images, website or store, current pricing, original pricing, likes, shipping details, and more.
3. Open a developer’s tool on a browser or check the element on a website. Most developer tools should highlight the selection and concentrate on your selected HTML tags. An important step is finding a parallel pattern to scrape data in Python’s loop. Moving toward the following items, you may see similar titles again. Here, a div tag using the class “fpItem” is known for every product
4. After identifying , we have to get the rest of the data from the parent. To get the name of every class, you could repeat the mentioned steps here using Developer Tools in a browser, find all the exciting fields and scrape them.
We have all the data on which class to scrape data from. We can put everything in the Python Scrapy project to do a trial run. Just go through the code to know more.
The given code is the spider.py file provided in the Scrapy Spider folder. Initially, we define a crawler’s name called — “slickdeals.” After that, as discussed here, we have to get the list item using Selector & calling
Then, we can iterate them and scrape data. Here, we have used XPath to check if a class has the keyword we are searching for.
Eventually, we store data in the CSV file to do more investigation. You can send an email with the keyword you need. Python’s email module could be used here. Here is the sample without content.
To test this program, in a project root directory, just execute
And the results would look like the given, and you’d observe the data fields we scraped.
As the program will run 24x7, the energy-efficient Raspberry Pi makes more sense to achieve the goal. When the code gets verified for execution, we can use a web crawler to run using Linux’s crontab.
You can begin with crontab -e and add the given command. We will execute the data crawler every 15 minutes using crontab */15 * * * *
Congratulations! We have got a data scraping program working 24x7 with the requests you have asked. It doesn’t matter what the objective is getting good deals, coupons, and freebies. Our small program runs very hard and silently in the back office to monitor the most satisfactory sales and send alerts on its results. We hope this blog can offer you insights and the ability to do web scraping to help you start building advanced programs on smaller devices like Raspberry Pi.
For more information about how to scrape the best deals data from discounts and promo code websites using web scraping, contact Actowiz Solutions now!
You can also discuss all your mobile app scraping and web scraping services requirements with us!
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