How do you get the finest deals on Amazon using web scraping? This blog uses Amazon product search pages for Apple Airpods to show how to extract data in less than 5 minutes.
This blog shows how we efficiently monitor the pricing changes and find the most updated data on the products you are looking for.
Let’s create an easy Web Scraping Python Script.
Step 1: Open Amazon Website and Search for the Item You are Interested in.
Here, we need to purchase new Airpods. Just copy its URL from browser.
Step 2: Import Packages Using Jupyter Notebook and Other Python IDEs
First, import the BeatifulScoup and Request library into a workplace. Request library assists us in requesting HTML data online. BeatifulSoup is a robust library that helps us clean and locates particular items in an HTML pulling.
After that, copy URL from a browser, and paste that into requests.get() method. It will scrape HTML data from Amazon Web Server.
If you think about how does HTML data look like, then you can print that using r.text
Looks to be extremely messy data, right? We have to use a BeatifulSoup library to remove some tags. Let’s start a BeatifulSoup object with the code given below.
Step 3: Inspect a Page to Get Relevant Data Tags on a Webpage
Use keys Ctrl + Shift+ I to review the title of product pages.
The key point will assist you in getting a
You may use a prettify() technique to view more structured codes: Here, we are looking at a second products on a page with slicer.
After that, let’s extract the discounted price and more data.
Here, we would like to extract the discounted prices. The key point shows it here to a tag:
$124.00
We need to copy a class name to the select_one() technique. We may print the text by using a code given below.
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We do that for all the interest fields: Product Name, Market Price, Discount Price, Ratings, and Total Reviews.
Step 4: Gather Pricing and Other Information of ALL Product Listings on a Given Page
Lastly, we can repeat all the listed products on a page wish an easy loop.
Just go through all listings and get the data we are concerned about.
Step 5: Put All Together and Get the Best Deals
Finally, we will make a Pandas DataFrame for cleaning and visualizing our data. Then, we will put data in the correct format and deal with all null values. Lastly, we can get the finest deals with the most substantial discounts.
Here, we will do some data engineering to create a new column for discounts and clean the data. To conclude, we sort all the data depending on discount amounts:
Let’s go through the last results:
Therefore, which is the most acceptable deal depending on the discounts?
Here, it’s easy to see that Airpods having a wireless charging case presently have a maximum discount of $52.8. Then, the second-best deal is on Airpod Pro, having $50 as a discount.
Step 6: Conclusion
In this blog, we have used Request and BeatifulSoup Library to extract Amazon for Airpods.
We have opened the concerned URL
We have imported packages in a Jupyter notebook
Then, we reviewed the page to get all applicable data tags on a webpage.
Then, we collected prices and other data about ALL products listed on a page To finish, we have worked on data engineering to get the finest deals based on the discounts. The best two deals include 1. Airpods have a wireless charging case, and 2. AirPods Pro.
To know more about scraping Amazon Prime Day deals data, contact Actowiz Solutions now!
You can also call us for all your mobile app scraping and web scraping services requirements.
