Ebay Scraper Python


Scraping eBay: How to Scrape Product Data Using Python

Scraping eBay: How to Scrape Product Data Using Python

Even though Amazon is the leader in e-commerce marketplaces – eBay still has its fair share in the online retail industry. Brands selling online should be monitoring prices on eBay as well to gain a competitive advantage. To scrape product data from eBay at a huge scale regularly is a challenging problem for data scientists. Here is an example of scraping eBay using python to identify the prices of mobile phones. Lets us imagine a use case where you need to monitor the pricing of a product, say a mobile phone from eBay. Also, you want to visualize the range of price offering available on the mobile phone which you want to monitor. Moreover, you have other mobile phones under consideration so you may also want to compare their prices as well. In this blog, we will be scraping eBay to collect the prices of phones and find out the difference between their offerings on the eBay website. Also Read: Busting 8 Myths About Web Scraping Scraping eBay product data step by step In this section, we will walk you through the step by step process of scraping eBay for products and their prices. 1. Selecting the required information The very first task in web scraping is to identify the target web page. It is the web page from which you need to extract all the required information. We will be scraping eBay for the product listings so we can just open the eBay website and type our product in their search bar and hit enter. Once page loads with all the product listing of that product, all you need to do is pull that URL out from the browser. This URL will be our target URL. In our case, the URL will be, “ Notice the two parameters in this URL i. e. “nkw” (new keyword) and “pgn” (page number) parameter. These parameters in the URL defines the search query. If we change “pgn” parameter to 2, then it will open the second page of the product listings for galaxy note 8 phone and if we were to change “nkw” to iPhone X then eBay will search for iPhone X and will show you the corresponding results. 2. Finalizing the tags for extraction Once we have finalized the target web page, we need to understand its HTML layout to scrape the results out. This is the most important and critical part of web scraping and basic HTML knowledge is a pre-requisite for this step. When on the target web page, do “inspect element” and open the developer tools window or just do CTRL+SHIFT+I. In the new window, you will find the source code of the target web page. In our case, all the products are mentioned as list elements so we have to grab all these lists. In order to grab an HTML element, we need to have an identifier associated with it. It can be an id of that element or any class name or any other HTML attribute of the particular element. We are using the class name as the identifier. All the lists have the same class name i. s-item. On further inspection, we got the class names for the product name and product price which are “s-item__title” and “s-item__price” respectively. With this information, we have successfully completed step 2! 3. Putting the scraped data in a structured format After having our extractors/identifiers we only need to extract specific portions out from the HTML content. Once this is done, we need to organize this data into a suitable structured format. We will be creating a table where we will have all the product names in one column and their prices in the other. 4. Visualizing the results (optional) Since we are to compare the price offerings on two different mobile phones, we will visualize the results too. This is not a mandatory step for web scraping but is more of a process to turn your collected data into some actionable insights. We will be plotting boxplots to understand the distribution of the price offerings on both galaxy note 8 and iPhone 8 mobile phones. Also Read: How to Bypass Anti-Scraping Tools on Websites Required libraries and Installation To implement web scraping for this use case, you will need python, pip (package installer for python), and BeautifulSoup library in python for web scraping. You will also need pandas and numpy library to organize the collected data into a structured format. Installing Python and PIP
Depending upon your operating system, you can follow this blog link to setup python and Pip in your stalling Beautiful soup library apt-get install python-bs4 pip install beautifulsoup4 3. Installing pandas and numpy pip install pandas pip install numpy We are done setting up our environment and now can begin with the scraping implementation using python. The implementation consists of the steps discussed in the earlier section. Also Read: Web Scraping to Extract Product Data From E-Commerce Sites Python implementation for scraping eBay In this section, we will perform two scraping operations i. one for the iPhone 8 and another for the galaxy note 8 mobile phones. Implementation has been repeated for the two mobile phones for easier comprehension. A more optimized version can have two separate scrapping activities combined into one which is not required right now though. Scrapping eBay for Galaxy Note 8 products item_name = []
prices = []
for i in range(1, 10):
ebayUrl = “+str(i)
r= (ebayUrl)
listings = nd_all(‘li’, attrs={‘class’: ‘s-item’})
for listing in listings:
prod_name=” ”
prod_price = ” ”
for name in nd_all(‘h3’, attrs={‘class’:”s-item__title”}):
if(str((text=True, recursive=False))! =”None”):
prod_name=str((text=True, recursive=False))
if(prod_name! =” “):
price = (‘span’, attrs={‘class’:”s-item__price”})
prod_price = str((text=True, recursive=False))
prod_price = int(sub(“, “, “”, (“INR”)[1](“. “)[0]))
from scipy import stats
import numpy as np
data_note_8 = Frame({“Name”:item_name, “Prices”: prices})
data_note_8 = [((data_note_8[“Prices”]))< 3, ]
Collected Data for Galaxy note 8 scraping eBay | Note 8 dataScrapping eBay for iPhone 8 item_name = []
data_note_8 = [((data_note_8[“Prices”])) < 3, ]
Collected data for iPhone 8 scraping eBay | Iphone dataVisualizing the price of the products Now is the time to visualize the scraped results. We will be using the boxplots to visualize the distribution of prices of mobile phones. Box plot helps us in visualizing a trend in numerical values. The green line is the median of the collected price data. The box extends from the Q1 to Q3 quartile values of the data, with a line at the median (Q2). The whiskers extend from the edges of the box to show the range of the data. scraping eBay | Price ComparisonFor iPhone 8, most of the prices lie between INR 25k-35k whereas most of the galaxy note 8 phones are available in the price range of 25k-30k. However, variation in prices of the iPhone 8 is much more than galaxy note 8. iPhone 8 is available for minimum INR 15k on eBay whereas the minimum cost of galaxy note 8 on eBay is around 22-23K INR! Also Read: Scraping Amazon Reviews using Scrapy in Python Datahut as a reliable scraping partner There are a lot of tools that can help you scrape data yourself. However, if you need professional assistance with minimal technical know-how, Datahut can help you. We have a well-structured and transparent process for extracting data from the web in real-time and provide in the desired format. We have helped enterprises across various industrial verticals. From assistance to the recruitment industry python/ to retail solutions, Datahut has designed sophisticated solutions for most of these use-cases. You should join the bandwagon of using data-scraping in your operations before it is too late. It will help you boost the performance of your organization. Furthermore, it will help you derive insights that you might not know currently. This will enable informed decision-making in your business processes. Conclusion In this blog, we successfully used python for scraping eBay for two different products and their pricing. We also compared the available prices for galaxy note 8 and iPhone 8 to make a better purchase decision. Web scraping coupled with data science can be leveraged for smart decision making be it in the fortune 500 companies or in your day to day life. Wish to avail web scraping services for your data needs? Contact Datahut to know more. #ebaydatascraping #scrapeebay #scrapingebay #webscrapingwithpython
How To Scrape eBay using Python and LXML - ScrapeHero

How To Scrape eBay using Python and LXML – ScrapeHero

In this article, we will show you how to scrape eBay and extract data such as prices and names of products in all categories by a brand. Web scraping eBay can help with collecting information for eBay keyword monitoring, price monitoring, brand monitoring and pricing intelligence. Scraping eBay listings at regular intervals can be useful to check the details of products and compare them with your competitor sites.
Here are the steps on how to scrape eBay
Construct the URL for the search results to scrape eBay.
Example – Download HTML of the search result page using Python Requests.
Parse the page using LXML – LXML lets you navigate the HTML Tree Structure using Xpaths.
Save the scraped eBay product data to a CSV file.
Below is a screenshot of the data to extract using our eBay scraping tutorial.
You could also scrape details such as the number of products sold or the ratings given by consumers, but for now, we will keep this eBay scraper simple and extract these.
Requirements for eBay Scraping
Install Python 3 and Pip
Here is a guide to install Python 3 in Linux – Mac Users can follow this guide – Windows Users go here – Packages
For this eBay scraping tutorial using Python 3, we will need some packages for downloading and parsing the HTML. Below are the package requirements:
PIP to install the following packages in Python ()
Python Requests, to make requests and download the HTML content of the pages ().
Python LXML, for parsing the HTML Tree Structure using Xpaths ( Learn how to install that here –)
The Code
Since we will be monitoring prices by their brand, here is the one for Apple – You can download the link at if the embed above does not work.
If you would like the code in Python 2. 7, you can check out the link at Running the Python eBay Scraper
We have named the script If you type in the script name in terminal or command prompt with a -h
usage: [-h] brand
positional arguments:
brand Brand Name
optional arguments:
-h, –help show this help message and exit
The brand argument represents any brand available on eBay. You can type in a brand that eBay currently has on its site such as – Samsung, Canon, Dell, etc. The script must be run with the argument for brand. As an example, to find all of the products Apple currently has on eBay, we would run the scraper like this.
python3 apple
In this article we are only scraping the product’s name, price, and URL from the first page of results, so a CSV file should be enough to fit in all the data. If you would like to extract details in bulk, a JSON file is more preferable. You can read about choosing your data format, just to be sure.
This will create a CSV file named that will be in the same folder as the script. Here are some of the data extracted from eBay in a CSV file from the command above.
You can download the code at We would love to know how this scraper worked for you. Let us know in the comments below.
Why Scrape eBay?
eBay scraping using the code above can be useful for a number of reason. Here are a few reasons on how web scraping eBay can be useful –
eBay Keyword monitoring – You can easily monitor eBay for a specific keywords using this tutorial.
Brand monitoring – You can replace the search term in this tutorial to include a brand name and easily monitor which brand products are being sold more often on eBay.
Price monitoring – eBay is one of the largest marketplaces in the world, scraping eBay for price and comparing with the Amazon scraper and Walmart scraper pricing data can help create an efficient price monitoring system.
Known Limitations to eBay Scraping
This code should be able to scrape eBay prices and details of most brands available. If you want to scrape and extract the details of thousands of products for each brand and check the competitor prices of products periodically (on an hourly basis), then you should read Scalable do-it-yourself scraping – How to build and run scrapers on a large scale and How to prevent getting blacklisted while scraping
Disclaimer: Any code provided in our tutorials is for illustration and learning purposes only. We are not responsible for how it is used and assume no liability for any detrimental usage of the source code. The mere presence of this code on our site does not imply that we encourage scraping or scrape the websites referenced in the code and accompanying tutorial. The tutorials only help illustrate the technique of programming web scrapers for popular internet websites. We are not obligated to provide any support for the code, however, if you add your questions in the comments section, we may periodically address you need some professional help with scraping complex websites you can fill up the form below.
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Is web crawling legal? - Towards Data Science

Is web crawling legal? – Towards Data Science

Photo by Sebastian Pichler on UnsplashWeb crawling, also known as web scraping, data scraping or spider, is a computer program technique used to scrape a huge amount of data from websites where regular-format data can be extracted and processed into easy-to-read structured crawling basically is how the internet functions. For example, SEO needs to create sitemaps and gives their permissions to let Google crawl their sites in order to make higher ranks in the search results. Many consultant companies would hire companies to specialize in web scraping to enrich their database so as to provide professional service to their is really hard to determine the legality of web scraping in the era of the digitized crawling can be used in the malicious purpose for example:Scraping private or classified information. Disregard of the website’s terms and service, scrape without owners’ abusive manner of data requests would lead web server crashes under additionally heavy is important to note that a responsible data service provider would refuse your request if:The data is private which would need a username and passcodesThe TOS (Terms of Service) explicitly prohibits the action of web scrapingThe data is copyrightedViolation of the Computer Fraud and Abuse Act (CFAA). Violation of the Digital Millennium Copyright Act (DMCA)Trespass to “just scraped a website” may cause unexpected consequences if you used it probably heard of the HiQ vs Linkedin case in 2017. HiQ is a data science company that provides scraped data to corporate HR departments. Linkedin then sent desist letter to stop HiQ scraping behavior. HiQ then filed a lawsuit to stop Linkedin from blocking their access. As a result, the court ruled in favor of HiQ. It is because that HiQ scrapes data from the public profiles on Linkedin without logging in. That said, it is perfectly legal to scrape the data which is publicly shared on the ’s take another example to illustrate in what case web scraping can be harmful. The law case eBay v. Bidder’s Edge. If you’re doing web crawling for your own purposes, it is legal as it falls under fair use doctrine. The complications start if you want to use scraped data for others, especially commercial purposes. Quoted from, 100 1058 (N. D. Cal. 2000), was a leading case applying the trespass to chattels doctrine to online activities. In 2000, eBay, an online auction company, successfully used the ‘trespass to chattels’ theory to obtain a preliminary injunction preventing Bidder’s Edge, an auction data aggregation, from using a ‘crawler’ to gather data from eBay’s website. The opinion was a leading case applying ‘trespass to chattels’ to online activities, although its analysis has been criticized in more recent long as you are not crawling at a disruptive rate and the source is public you should be fine. I suggest you check the websites you plan to crawl for any Terms of Service clauses related to scraping their intellectual property. If it says “no scraping or crawling”, you should respect ggestion:Scrape discreetly, check “” before you start scrapingGo conservative. Aggressively asking for data can burden the internet server. An ethical way is to be gentle. No one wants to crash the the data wisely. Don’t duplicate the data. You can generate insight from collected data, and help Your business out to the owner of the website before you start ’t randomly pass scraped data to anyone. If it is valuable data, keep it secure.

Frequently Asked Questions about ebay scraper python

How do I scrape on eBay using Python?

Here are the steps on how to scrape eBay Construct the URL for the search results to scrape eBay. Download HTML of the search result page using Python Requests. Parse the page using LXML – LXML lets you navigate the HTML Tree Structure using Xpaths. Save the scraped eBay product data to a CSV file.Sep 18, 2017

Is it legal to scrape eBay?

The law case eBay v. Bidder’s Edge. If you’re doing web crawling for your own purposes, it is legal as it falls under fair use doctrine. The complications start if you want to use scraped data for others, especially commercial purposes.Jul 17, 2019

How do you scrape price on eBay?

Scrape pricing from eBay1) “Go To Web Page” – to open the target webpage.2) Create a pagination loop – to scrape all the results from multiple pages.3) Create a “Loop Item” – to scrape all the items on each page.4) Extract data – to select the data for extraction extracted.More items…•Dec 2, 2019

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