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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)
soup=BeautifulSoup(data)
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))
(prod_name)
if(prod_name! =” “):
price = (‘span’, attrs={‘class’:”s-item__price”})
prod_price = str((text=True, recursive=False))
prod_price = int(sub(“, “, “”, (“INR”)[1](“. “)[0]))
(prod_price)
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 Product Data - ParseHub

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How to Scrape eBay Product Data – ParseHub

eBay has over 1. 3 billion product ’s a lot of data can be valuable for market research, competitor research, to find your next hot bid, and much ever, there’s no easy way to just export all the product data housed on ’s true unless you’re taking advantage of web ‘re going to show you how you can scrape eBay to extract data like product details, prices, sellers and more! Web Scraping eBay with ParseHubA web scraper is a piece of software that will allow you to choose the exact data you want from eBay’s website and let you download it as an Excel or JSON our example today, we will use ParseHub, a free and powerful web scraping that can work with any sure to download ParseHub for free before getting raping eBay DataFor our example today, we will scrape data from the eBay search results page for the term “Smartphone”. This will include product details, pricing and ’s get stall and Open ParseHub. Click on “New Project” and enter the URL you will be scraping. For this example, we will scrape the search results page for the term “smartphone”. The page will now be rendered inside the by clicking on the title of the name of the first product on the list. It will be highlighted in green to indicate it has been selected. On the left sidebar, rename your selection to “product” rest of the product names in the page will be highlighted in Yellow, click on the second one on the list to select them all. They will all now be highlighted in Green. ParseHub is now pulling the name and URL of each product on the ’s extract more data. Start by clicking on the PLUS(+) sign next to your product selection and choose the Relative Select the Relative Select command, click on the name of the first product on the list and then on its price. An arrow will appear to show the association you’re might have to repeat this process for another product to fully train the scraper On the left sidebar, rename your selection to “price” steps 5-6 in order to pull more data for each product. In this case, we will also pull the day lefts, shipping costs and units sold. Your project should looks somewhat like this:Scraping more detailsParseHub is currently only scraping data from the search results page. But there’s only so much that we can pull from ’s now setup ParseHub to click on each listing and pull additional on the PLUS(+) sign next to your “product” selection and choose the Click command. A pop-up will appear asking you if this is a “next page” link. Click on “no” and enter a name for this template. We will call it “product_page”. You will now be taken to the product page of the first listing on the product page will render in a new browser tab and you will be able to make your first selection of data to extract. In this case, we will select the seller name by clicking on it. In the left sidebar, rename your selection to “seller” on the PLUS(+) sign next to the “page” selection, choose the Select command and you will be able to create new select commands and click on more data to extract. We will do this to also pull the seller rating and number of seller reviews. Your project should look like this:ParseHub is now pulling all the data we have selected for the products on the first page of results. Let’s not set it up to extract additional pages of to your main_template using the left-side tabs. Use the browser tabs to go back to the search results on the PLUS(+) sign next to your “page” selection and choose the Select command. Scroll all the way to the bottom of the page and click on the “next page” link. Rename your selection to “pagination” your “pagination” selection using the icon next to both “extract” commands under your “pagination” selection. Click on the PLUS(+) sign next to your “pagination” selection and choose the Click command.
A pop-up will appear asking you if this is a “next page” link. Click on “Yes” and enter the number of additional pages you would like to scrape. In this case, we will scrape 5 more nning Your ScrapeYou are now ready to run your scraping project and let ParseHub extract all the data for this by clicking on the green “Get Data” button on the left, you will be able to test, run or schedule your project. If you want up-to-date data on a daily or weekly basis, you can go with the schedule this case, we will just run it right osing ThoughtsParseHub is now off to run your scrape in our servers. Meaning that your computer resources are not being used for scraping. You can freely work on something else while you your scrape is complete, ParseHub will notify you and you will be able to download your scrape as an Excel or JSON you run into any issues while setting up your project, reach out to us via the live chat on our site and we will be happy to assist Scraping! Download ParseHub For FreeRead the other how to guides we have made to scrape different types of websites:How to Scrape Data from a News WebsiteHow to Scrape Yahoo Finance Data: Stock Prices, Bids, Price Change and to Scrape Reddit Data: Links, Comments, Images and more.
Is Web Scraping Illegal? Depends on What the Meaning of the Word Is

Is Web Scraping Illegal? Depends on What the Meaning of the Word Is

Depending on who you ask, web scraping can be loved or hated.
Web scraping has existed for a long time and, in its good form, it’s a key underpinning of the internet. “Good bots” enable, for example, search engines to index web content, price comparison services to save consumers money, and market researchers to gauge sentiment on social media.
“Bad bots, ” however, fetch content from a website with the intent of using it for purposes outside the site owner’s control. Bad bots make up 20 percent of all web traffic and are used to conduct a variety of harmful activities, such as denial of service attacks, competitive data mining, online fraud, account hijacking, data theft, stealing of intellectual property, unauthorized vulnerability scans, spam and digital ad fraud.
So, is it Illegal to Scrape a Website?
So is it legal or illegal? Web scraping and crawling aren’t illegal by themselves. After all, you could scrape or crawl your own website, without a hitch.
Startups love it because it’s a cheap and powerful way to gather data without the need for partnerships. Big companies use web scrapers for their own gain but also don’t want others to use bots against them.
The general opinion on the matter does not seem to matter anymore because in the past 12 months it has become very clear that the federal court system is cracking down more than ever.
Let’s take a look back. Web scraping started in a legal grey area where the use of bots to scrape a website was simply a nuisance. Not much could be done about the practice until in 2000 eBay filed a preliminary injunction against Bidder’s Edge. In the injunction eBay claimed that the use of bots on the site, against the will of the company violated Trespass to Chattels law.
The court granted the injunction because users had to opt in and agree to the terms of service on the site and that a large number of bots could be disruptive to eBay’s computer systems. The lawsuit was settled out of court so it all never came to a head but the legal precedent was set.
In 2001 however, a travel agency sued a competitor who had “scraped” its prices from its Web site to help the rival set its own prices. The judge ruled that the fact that this scraping was not welcomed by the site’s owner was not sufficient to make it “unauthorized access” for the purpose of federal hacking laws.
Two years later the legal standing for eBay v Bidder’s Edge was implicitly overruled in the “Intel v. Hamidi”, a case interpreting California’s common law trespass to chattels. It was the wild west once again. Over the next several years the courts ruled time and time again that simply putting “do not scrape us” in your website terms of service was not enough to warrant a legally binding agreement. For you to enforce that term, a user must explicitly agree or consent to the terms. This left the field wide open for scrapers to do as they wish.
Fast forward a few years and you start seeing a shift in opinion. In 2009 Facebook won one of the first copyright suits against a web scraper. This laid the groundwork for numerous lawsuits that tie any web scraping with a direct copyright violation and very clear monetary damages. The most recent case being AP v Meltwater where the courts stripped what is referred to as fair use on the internet.
Previously, for academic, personal, or information aggregation people could rely on fair use and use web scrapers. The court now gutted the fair use clause that companies had used to defend web scraping. The court determined that even small percentages, sometimes as little as 4. 5% of the content, are significant enough to not fall under fair use. The only caveat the court made was based on the simple fact that this data was available for purchase. Had it not been, it is unclear how they would have ruled. Then a few months back the gauntlet was dropped.
Andrew Auernheimer was convicted of hacking based on the act of web scraping. Although the data was unprotected and publically available via AT&T’s website, the fact that he wrote web scrapers to harvest that data in mass amounted to “brute force attack”. He did not have to consent to terms of service to deploy his bots and conduct the web scraping. The data was not available for purchase. It wasn’t behind a login. He did not even financially gain from the aggregation of the data. Most importantly, it was buggy programing by AT&T that exposed this information in the first place. Yet Andrew was at fault. This isn’t just a civil suit anymore. This charge is a felony violation that is on par with hacking or denial of service attacks and carries up to a 15-year sentence for each charge.
In 2016, Congress passed its first legislation specifically to target bad bots — the Better Online Ticket Sales (BOTS) Act, which bans the use of software that circumvents security measures on ticket seller websites. Automated ticket scalping bots use several techniques to do their dirty work including web scraping that incorporates advanced business logic to identify scalping opportunities, input purchase details into shopping carts, and even resell inventory on secondary markets.
To counteract this type of activity, the BOTS Act:
Prohibits the circumvention of a security measure used to enforce ticket purchasing limits for an event with an attendance capacity of greater than 200 persons.
Prohibits the sale of an event ticket obtained through such a circumvention violation if the seller participated in, had the ability to control, or should have known about it.
Treats violations as unfair or deceptive acts under the Federal Trade Commission Act. The bill provides authority to the FTC and states to enforce against such violations.
In other words, if you’re a venue, organization or ticketing software platform, it is still on you to defend against this fraudulent activity during your major onsales.
The UK seems to have followed the US with its Digital Economy Act 2017 which achieved Royal Assent in April. The Act seeks to protect consumers in a number of ways in an increasingly digital society, including by “cracking down on ticket touts by making it a criminal offence for those that misuse bot technology to sweep up tickets and sell them at inflated prices in the secondary market. ”
In the summer of 2017, LinkedIn sued hiQ Labs, a San Francisco-based startup. hiQ was scraping publicly available LinkedIn profiles to offer clients, according to its website, “a crystal ball that helps you determine skills gaps or turnover risks months ahead of time. ”
You might find it unsettling to think that your public LinkedIn profile could be used against you by your employer.
Yet a judge on Aug. 14, 2017 decided this is okay. Judge Edward Chen of the U. S. District Court in San Francisco agreed with hiQ’s claim in a lawsuit that Microsoft-owned LinkedIn violated antitrust laws when it blocked the startup from accessing such data. He ordered LinkedIn to remove the barriers within 24 hours. LinkedIn has filed to appeal.
The ruling contradicts previous decisions clamping down on web scraping. And it opens a Pandora’s box of questions about social media user privacy and the right of businesses to protect themselves from data hijacking.
There’s also the matter of fairness. LinkedIn spent years creating something of real value. Why should it have to hand it over to the likes of hiQ — paying for the servers and bandwidth to host all that bot traffic on top of their own human users, just so hiQ can ride LinkedIn’s coattails?
I am in the business of blocking bots. Chen’s ruling has sent a chill through those of us in the cybersecurity industry devoted to fighting web-scraping bots.
I think there is a legitimate need for some companies to be able to prevent unwanted web scrapers from accessing their site.
In October of 2017, and as reported by Bloomberg, Ticketmaster sued Prestige Entertainment, claiming it used computer programs to illegally buy as many as 40 percent of the available seats for performances of “Hamilton” in New York and the majority of the tickets Ticketmaster had available for the Mayweather v. Pacquiao fight in Las Vegas two years ago.
Prestige continued to use the illegal bots even after it paid a $3. 35 million to settle New York Attorney General Eric Schneiderman’s probe into the ticket resale industry.
Under that deal, Prestige promised to abstain from using bots, Ticketmaster said in the complaint. Ticketmaster asked for unspecified compensatory and punitive damages and a court order to stop Prestige from using bots.
Are the existing laws too antiquated to deal with the problem? Should new legislation be introduced to provide more clarity? Most sites don’t have any web scraping protections in place. Do the companies have some burden to prevent web scraping?
As the courts try to further decide the legality of scraping, companies are still having their data stolen and the business logic of their websites abused. Instead of looking to the law to eventually solve this technology problem, it’s time to start solving it with anti-bot and anti-scraping technology today.
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Frequently Asked Questions about ebay web scraper

Does eBay allow web scraping?

Web Scraping eBay with ParseHub A web scraper is a piece of software that will allow you to choose the exact data you want from eBay’s website and let you download it as an Excel or JSON file. … Make sure to download ParseHub for free before getting started.Jul 27, 2020

Is it legal to scrape a website?

Web scraping and crawling aren’t illegal by themselves. After all, you could scrape or crawl your own website, without a hitch. … Big companies use web scrapers for their own gain but also don’t want others to use bots against them.

How do I extract data from 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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