In this blog, we will explain how to extract eBay product data using Python and other data like product name and prices in different categories. Scraping eBay data can assist in collecting data for eBay price monitoring, keyword’s monitoring, brand’s monitoring, as well as price intelligence. Extracting eBay lists at regular breaks can be helpful to observe product information and match them with the competitor sites.
Follow these steps about how to extract eBay data.
- Create search results URL to extract eBay data.
Example –https://www.ebay.com/sch/i.html?_from=R40&_sacat=0&_nkw=sofa&_blrs=recall_filtering
- You can download HTML for searching result pages with Python Requests.
- You can also parse a page with LXML as it helps you navigate HTML Tree Structure with Xpaths.
- Then save the mined eBay products data in the CSV files.
Here are the screenshot of data scraping using this eBay data scraping. tutorial blog.
You can also mine data like number of total products traded or ratings provided by the consumers, however for present, we would keep the eBay data scraper easy and scrape these.
eBay Scraping Requirements
Installation of Python 3 with Pip
This is the guide for installing Python 3 within the Linux at –
http://docs.python-guide.org/en/latest/starting/install3/linux/
For Mac Users, the guide is available at–
https://docs.python-guide.org/starting/install3/osx/
Packages
Used for eBay product data extracting tutorial with Python 3, we require many packages to download as well as parse with HTML. Just go through some package necessities:
PIP for installation of the following packages with Python at
https://pip.pypa.io/en/stable/installing/
Python Requests to make requests and also download HTML content at
https://docs.python-requests.org/en/latest/user/install/)
You can use Python LXML to parse HTML Tree Structure with Xpaths
http://lxml.de/installation.html
The Code
As we would monitor prices through their brand, this one is for IKEA –
https://www.ebay.com/sch/i.html?_from=R40&_sacat=0&_nkw=sofa&_blrs=recall_filtering
Run the Python eBay Scraper
We have given the script called ebay_scraper.py. In case, you type the script name within command prompt or terminal at a -h
usage: ebay_scraper.py [-h] brand positional arguments: brand Brand Name optional arguments: -h, --help show this help message and exit
The brand arguments represent any brands accessible on eBay. You may type in the brand, which eBay presently has on the website like – Canon, Dell, Samsung, etc. The script has to run with the brand argument. For instance, to get all the products, currently Apple has on eBay, we will run the eBay extractor like this.
python3 ebay_scraper.py apple
In this blog, we are extracting product’s name, pricing, as well as URLs from first page results, therefore a CSV file will be sufficient to fit all details. In case, you need to extract bulk details, JSON files are more desirable.
It will make the CSV file called apple-ebay-scraped-data.csv, which will be within the similar folder like a script. You need to go through a few of the extracted data from eBay within CSV files from a command given above.
We would like to understand how this data scraper has worked for you. Let us understand this in comments given below.
Why Extract eBay?
eBay data scraping with the code given above could be helpful for many reasons. Let’s go through some reasons about how scraping eBay data could be helpful:
eBay Keyword Monitoring
You may easily do eBay keyword monitoring for specific keywords with this tutorial blog.
Brand Monitoring
It’s easy to replace search terms in the tutorial blog to comprise a brand name as well as easily observe which products are getting sold often on the eBay website.
Price Monitoring
eBay is amongst the biggest marketplaces worldwide and scraping eBay data for price comparison with the Walmart scraper and Amazon scraper pricing data could help you create a well-organized price monitoring system.
Limitations of eBay Data Scraping
The code should work in scraping eBay details and prices of the maximum available brands. In case, you need to scrape data of thousands of products for every brand as well as check competitors’ product prices periodically, then you need to read more about scalable scraping, how to create as well as run scrapers on a huge scale, as well as how to stop getting blacklisted when scraping.
