What is the Process of Scraping eBay Products Data Using Python?

Stepwise Process for Scraping of eBay Product Data Using Python

In this module, we'll explore the stepwise prices to scrape the eBay platform for various products and prices.

Choosing the Mandatory Information

The primary task in eBay web scraping is to find the targeted web page. From this eBay page you must collect all the required product information.

Here, we are scraping eBay to extract product listing data so we can open the websites and feed our product into the search bar to explore it. After clicking the enter key, the page will load with all the product listings of the submitted product. Now you need to pull out that from the web browser. You can consider it as a targeted URL. In our example, https://www.ebay.com/sch/i.html_from=R40&_nkw=galaxy+note+8&_sacat=0&_pgn=1 is the targeted URL.

Note the page number and new keyword from this URL using the pgn and nkw notations, respectively. These parameters denote the search term in the URL. If we make the pgn to two, it will display page number two of the product listing for the Samsung Galaxy Note 8 mobile device. Instead of changing the page number, if we had changed it now to iPhone 8, it would have displayed iPhone 8 on the screen with corresponding outputs.

Deciding Tags to Collect from eBay

Once we decide on the target page for the scraper, we must understand its HTML to crawl the results. It is an essential web scraping requirement. Further, if you are a newbie, you must have basic HTML knowledge to handle this step.

When exploring the target page, inspect the element and enter it into the developer tool window or press control + shift+ I; you will get a new window with the source code for the selected target page. In our example, we've collected all the lists from the list elements.

To collect the HTML element, we must have an identifier with it. Identifiers can be an element, HTML attribute, or class name of a specific element. Here, we are using the identifier class name. Each list has the same class name- s-item.

Inspecting further, we got the product price and product name with names of classes s-item__price and s-item__title, respectively. Using this data, we finished the second step successfully.

Locating the Scraped Data in Usable Format.

After getting identifiers or extractors, we must collect a particular HTML content portion. Then, we should reform this data into usable structured formats. Here, we are making the table with names of eBay products in one column and prices in another.

Optional Step to Visualize the Outputs

We will plot boxplots to learn the price offering distribution on both iPhone X and galaxy note 8 mobile devices. We will analyze outputs as we compare the price offerings on two mobiles. It is an optional Step in web data scraping, but you can try this to convert the scraped product information to get actionable insights.

Installation of Required Python Libraries

To execute the web data scraping for eBay products, you need Python, BeautifulSoup, and pip libraries for eBay scraping. Additionally, you will require numpy and pandas libraries to organize the extracted product data in a digestible format.

Installing PIP and Python

Depending on the operating system on your device, you can follow the link to this blog to set up pip and Python in the system.

Installation of BeautifulSoup Library

Installing numpy and Pandas

We finished the setup for scraping implementation using Python. The execution includes the above steps.

Executing eBay Scraping using Python

Here, we'll perform two data scraping operations to extract data for Galaxy Note 8 and iPhone 8 mobile phone devices, respectively. For simple comprehension, we've repeated the execution for two devices. We've combined these two actions without the requirement to get a more optimized scraping version.

Scraping eBay using Python to get Galaxy Note 8 Data

Compiled Galaxy Note 8 Data

Scraping eBay to get iPhone 8 Data

Compile iPhone 8 Data

Visualizing eBay Product Prices

Now, it is time to analyze extracted outputs. Here, we'll use boxplots to analyze the price distribution of mobile devices.

The box plot helps us to visualize numerical value trends. In the scraped product price information, the green line denotes the median. The box extends the quartile values from Q1 to Q3, with Q2 being the median value of the data. To show the data range, the whiskers extend from the box edges.

Galaxy Note 8 mobiles' price ranges from 25 to 30 thousand, and iPhone prices range from 25k to 35k in Indian rupees.

But the price variation for iPhones is more than that of Galaxy Note 8 devices, as iPhone starting price is around fifteen thousand Indian rupees. Galaxy Note 8 starts at around 22 to 23 thousand Indian rupees on eBay.

Product Data Scrape as a Reliable Web Scraping Partner

Many tools are available to help you with data scraping without help from a technical person. But, if you still require professional help, Product Data Scrape can help you anytime. We have a dedicated technical team and a transparent web scraping process to deliver the required data in the preferred format. Our team has helped various retail and enterprise brands globally in eCommerce and other industries.

You must introduce data scraping in your company operations to accelerate business growth quickly. It will derive insights from the market database to help you make informed decisions.

Conclusion

Here, we discussed scraping eBay for some products with their prices. Using some minor changes, you can scrape other eBay products. We also help in e-commerce data scraping, product matching, price monitoring, retail analytics, and more. If you have any queries in scraping eBay product data using Python, contact Product Data Scrape.