How To Scrape Products From E Commerce

Essential Legal Issues Associated With Web Scraping – Robotics …

“Web scraping”, also called crawling or spidering, is the automated gathering of data from someone else’s website.
Scraping is an essential part of how the Internet functions, and it has traditionally been the backbone of many companies’ marketing, lead generation, and market intelligence efforts.
In fact, many online services, large and small, use scraping to build their databases, all of which are worth hundreds of billions of dollars. This article will answer the question – is web scraping legal?
Even though web scraping is ubiquitous, a plethora of legal issues remains fraught with the process. The following analysis explains the position of the law on web scraping in the world today.
Under the GDPR
Under the EU’s General Data Protection Regulation (or GDPR) web scraping does not apply to a person or company except such an entity is scrapping personal data of people within the European Economic Area, including Iceland, Liechtenstein, and Norway.
Examples of personal data include a person’s Name, Physical Address, Email Address, Phone Number, Credit Card Details, Bank Details, IP Address, Date of Birth, Employment Information, Social Security Number, Medical Information and Video/Audio Recording.
Use this guide to web scraping legal issues to ensure your web scraping is GDPR compliant.
However, exceptions can be made for the scraping of the personal data of people within the EEA. The five types of lawful reasons are Consent, Contract, Compliance (with a legal obligation), Vital Interest/Public Interest/Official Authority, Legitimate Interest.
Is scraping e-commerce sites legal? In 1999, the first web scraping case between eBay and Bidder’s Edge was heard in a US court. Therein, Bidder’s Edge accessed eBay’s approximately 100, 000 times without the latter’s authorization.
The case was settled out of court when Bidder’s Edge paid Ebay an undisclosed amount and agreed not to access Ebay’s data. The legal principle has been amended in the US, among a plethora of cases, the latest of which will be addressed in subsequent paragraphs.
Is Scraping Social Media Sites Legal? In late 2019, the US Court of Appeals denied LinkedIn’s request to prevent HiQ, an analytics company, from scraping its data. Consequently, the historic decision showed that any data that is publicly available and is not copyrighted is legally valid for the use of web crawlers.
The decision did not, however, grant HiQ or other web crawlers the freedom to use data obtained for unlimited commercial purposes. For example, a web crawler would be allowed to search YouTube for video titles, but it could not re-post the Youtube videos on its own site since the videos are copyrighted.
To keep your scraping activities ethical and also to avoid any problems follow the best practices, like limiting your requests to every target site so that they will not feel invaded and their servers will not overload. Also, the proxy provider and type of proxy IP you choose is also very important.
Check out this guide to web scraping proxies and find out which proxy providers offer the best combination of reliability and price.
Existing Legal Issues With Web Scraping
Copyright Infringement: In most jurisdictions, web scraping is legal, but using copyright data contains certain restrictions.
Violation of the Computer Fraud and Abuse Act (CFAA): This law, enacted to prevent computer hackers, prevents fetching data by getting unauthorized access to a page.
Trespass to Chattel: Here, a chattel (or data) is violated if the website server is hurt in any way. Thus, trespass to chattel is violated if the server slows or stops because of the scraping.
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How to scrape Prices from any eCommerce website - ScrapeHero

How to scrape Prices from any eCommerce website – ScrapeHero

Price Scraping involves gathering price information of a product from an eCommerce website using web scraping. A price scraper can help you easily scrape prices from website for price monitoring purposes of your competitor and your products.
How to Scrape Prices
1. Create your own Price Monitoring Tool to Scrape Prices
There are plenty of web scraping tutorials on the internet where you can learn how to create your own price scraper to gather pricing from eCommerce websites. However, writing a new scraper for every different eCommerce site could get very expensive and tedious. Below we demonstrate some advanced techniques to build a basic web scraper that could scrape prices from any eCommerce page.
2. Web Scraping using Price Scraping Tools
Web scraping tools such as ScrapeHero Cloud can help you scrape prices without coding, downloading and learning how to use a tool. ScrapeHero Cloud has pre-built crawlers that can help you scrape popular eCommerce websites such as Amazon, Walmart, Target easily. ScrapeHero Cloud also has scraping APIs to help you scrape prices from Amazon and Walmart in real-time, web scraping APIs can help you get pricing details within seconds.
3. Custom Price Monitoring Solution
ScrapeHero Price Monitoring Solutions are cost-effective and can be built within weeks and in some cases days. Our price monitoring solution can easily be scaled to include multiple websites and/or products within a short span of time. We have considerable experience in handling all the challenges involved in price monitoring and have the sufficient know-how about the essentials of product monitoring.
How to Build a Price Scraper
In this tutorial, we will show you how to build a basic web scraper which will help you in scraping prices from eCommerce websites by taking a few common websites as an example.
Let’s start by taking a look at a few product pages, and identify certain design patterns on how product prices are displayed on the websites.
Observations and Patterns
Some patterns that we identified by looking at these product pages are:
Price appears as currency figures (never as words)
The price is the currency figure with the largest font size
Price comes inside first 600 pixels height
Usually the price comes above other currency figures
Of course, there could be exceptions to these observations, we’ll discuss how to deal with exceptions later in this article. We can combine these observations to create a fairly effective and generic crawler for scraping prices from eCommerce websites.
Implementation of a generic eCommerce scraper to scrape prices
Step 1: Installation
This tutorial uses the Google Chrome web browser. If you don’t have Google Chrome installed, you can follow the installation instructions.
Instead of Google Chrome, advanced developers can use a programmable version of Google Chrome called Puppeteer. This will remove the necessity of a running GUI application to run the scraper. However, that is beyond the scope of this tutorial.
Step 2: Chrome Developer Tools
The code presented in this tutorial is designed for scraping prices as simple as possible. Therefore, it will not be capable of fetching the price from every product page out there.
For now, we’ll visit an Amazon product page or a Sephora product page in Google Chrome.
Visit the product page in Google Chrome
Right-click anywhere on the page and select ‘Inspect Element’ to open up Chrome DevTools
Click on the Console tab of DevTools
Inside the Console tab, you can enter any JavaScript code. The browser will execute the code in the context of the web page that has been loaded. You can learn more about DevTools using their official documentation.
Step 3: Run the JavaScript snippet
Copy the following JavaScript snippet and paste it into the console.
let elements = [
cument. querySelectorAll(‘ body *’)]
function createRecordFromElement(element) {
const text = ()
var record = {}
const bBox = tBoundingClientRect()
if( <= 30 &&! (bBox. x == 0 && bBox. y == 0)) { record['fontSize'] = parseInt(getComputedStyle(element)['fontSize'])} record['y'] = bBox. y record['x'] = bBox. x record['text'] = text return record} let records = (createRecordFromElement) function canBePrice(record) { if( record['y'] > 600 ||
record[‘fontSize’] == undefined ||! record[‘text’](/(^(US){0, 1}(rs\. |Rs\. |RS\. |\$|₹|INR|USD|CAD|C\$){0, 1}(\s){0, 1}[\d, ]+(\. \d+){0, 1}(\s){0, 1}(AED){0, 1}$)/))
return false
else return true}
let possiblePriceRecords = (canBePrice)
let priceRecordsSortedByFontSize = (function(a, b) {
if (a[‘fontSize’] == b[‘fontSize’]) return a[‘y’] > b[‘y’]
return a[‘fontSize’] < b['fontSize']}) (priceRecordsSortedByFontSize[0]['text']); Press ‘Enter’ and you should now be seeing the price of the product displayed on the console. If you don’t, then you have probably visited a product page which is an exception to our observations. This is completely normal, we’ll discuss how we can expand our script to cover more product pages of these kinds. You could try one of the sample pages provided in step 2. The animated GIF below shows how we get the price from How it works First, we have to fetch all the HTML DOM elements in the page. We need to convert each of these elements to simple JavaScript objects which stores their XY position values, text content and font size, which looks something like {'text':'Tennis Ball', 'fontSize':'14px', 'x':100, 'y':200}. So we have to write a function for that, as follows. const text = () // Fetches text content of the element var record = {} // Initiates a simple JavaScript object // getBoundingClientRect is a function provided by Google Chrome, it returns // an object which contains x, y values, height and width // getComputedStyle is a function provided by Google Chrome, it returns an // object with all its style information. Since this function is relatively // time-consuming, we are only collecting the font size of elements whose // text content length is atmost 30 and whose x and y coordinates are not 0 Now, convert all the elements collected to JavaScript objects by applying our function on all elements using the JavaScript map function. Remember the observations we made regarding how a price is displayed. We can now filter just those records which match our design observations. So we need a function that says whether a given record matches with our design observations. if( record['y'] > 600 ||
We have used a Regular Expression to check if a given text is a currency figure or not. You can modify this regular expression in case it doesn’t cover any web pages that you’re experimenting with.
Now we can filter just the records that are possibly price records
Finally, as we’ve observed, the Price comes as the currency figure having the highest font size. If there are multiple currency figures with equally high font size, then Price probably corresponds to the one residing at a higher position. We are going to sort out our records based on these conditions, using the JavaScript sort function.
Now we just need to display it on the console
Taking it further
Moving to a GUI-less based scalable program
You can replace Google Chrome with a headless version of it called Puppeteer. Puppeteer is arguably the fastest option for headless web rendering. It works entirely based on the same ecosystem provided in Google Chrome. Once Puppeteer is set up, you can inject our script programmatically to the headless browser, and have the price returned to a function in your program. To learn more, visit our tutorial on Puppeteer.
Improving and enhancing this script
You will quickly notice that some product pages will not work with such a script because they don’t follow the assumptions we have made about how the product price is displayed and the patterns we identified.
Unfortunately, there is no “holy grail” or a perfect solution to this problem. It is possible to generalize more web pages and identify more patterns and enhance this scraper.
A few suggestions for enhancements are:
Figuring out more features, such as font-weight, font color, etc.
Class names or IDs of the elements containing price would probably have the word price. You could figure out such other commonly occurring words.
Currency figures with strike-through are probably regular prices, those could be ignored.
There could be pages that follow some of our design observations but violates some others. The snippet provided above strictly filters out elements that violate even one of the observations. In order to deal with this, you can try creating a score based system. This would award points for following certain observations and penalize for violating certain observations. Those elements scoring above a particular threshold could be considered as price.
The next significant step that you would use to handle other pages is to employ Artificial Intelligence/Machine Learning based techniques. You can identify and classify patterns and automate the process to a larger degree this way. However, this field is an evolving field of study and we at ScrapeHero are using such techniques already with varying degrees of success.
If you need help to scrape prices from you can check out our tutorial specifically designed for
We can help with your data or automation needs
Turn the Internet into meaningful, structured and usable data
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 them.
Pulling Data from the Web: How to Get Data from a Website |

Pulling Data from the Web: How to Get Data from a Website |

The value of web data is increasing in every industry from retail competitive price monitoring to alternative data for investment research. Getting that data from a website is vital to the success of your business. As the trusted research firm, Gartner, stated in their blog:
“Your company’s biggest database isn’t your transaction, CRM, ERP or other internal database. Rather it’s the Web itself…Treat the Internet itself as your organization’s largest data source. ”
In fact, the internet is the largest source of business data on earth and it’s growing by the minute. The infograph below from Domo shows how much web data is created every minute from just a few websites out of a billion.
Source Domo
It’s clear the need for web data integration is greater than ever. This article will walk you through a simple process of pulling data from a webpage using data extraction software. First, let’s look at other uses of web data in business.
How do businesses use data from a website?
Competitive price comparison and alternative data for equity research are two popular uses of website data, but there are others less obvious.
Here are a few examples:
Teaching Movie Studios how to spot a hit manuscript
For StoryFit, data is the fuel that powers its predictive analytic engines. StoryFit’s artificial intelligence and machine learning algorithms are trained using vast amounts of data culled from a variety of sources, including extractors. This data contributes to StoryFit’s core NLP-focused AI to train machine learning models to determine what makes a hit movie.
Predicative Shipping Logistics
ClearMetal is a Predictive Logistics company using data science to unlock unprecedented efficiencies for global trade. They are using web data to mine all container and shipping information in the world then feed predictions back to companies that run terminals.
Market Intelligence
XiKO provides market intelligence around what consumers say online about brands and products. This information allows marketers to increase the efficacy of their programs and advertising. The key to XiKO’s success lies in its ability to apply linguistic modeling to vast amounts of data collected from websites.
Data-driven Marketing
Virtuance uses web data to review listing information from real estate sites to determine which listings need professional marketing and photography. From this data, Virtuance determines who needs their marketing services and develops success metrics based on the aggregated data.
Now that you have some examples of what companies are doing with web data, below are the steps that will show you how to pull data from a website.
Steps to get data from a website
Websites are built for human consumption, not machine. So it’s not always easy to get web data into a spreadsheet for analysis or machine learning. Copying and pasting information from websites is time-consuming, error-prone and not feasible.
Web scraping is a way to get data from a website by sending a query to the requested page, then combing through the HTML for specific items and organizing the data. If you don’t have an engineer on hand, provides a no-coding, point and click web data extraction platform that makes it easy to get web data.
Here’s a quick tutorial on how it works:
Step 1. First, find the page where your data is located. For instance, a product page on
Step 1. First, find the page where your data is located.
Step 2. Copy and paste the URL from that page into, to create an extractor that will attempt to get the right data.
Step 2. Copy and paste the URL from that page into
Step 3. Click Go and will query the page and use machine learning to try to determine what data you want.
Step 4. Once it’s done, you can decide if the extracted data is what you need. In this case, we want to extract the images as well as the product names and prices into columns. We trained the extractor by clicking on the top three items in each column, which then outlines all items belonging to that column in green.
Step 4. Once it’s done, you can decide if the extracted data is what you need.
Step 5. then populates the rest of the column for the product names and prices.
Step 6. Next, click on Extract data from website.
Step 7. has detected that the product listing data spans more than one page, so you can add as many pages as needed to ensure that you get every product in this category into your spreadsheet.
Step 8. Now, you can download the images, product names, and prices.
Step 9. First, download the product name and price into an Excel spreadsheet.
Step 10. Next, download the images as files to use to populate your own website or marketplace.
What else can you do with web scraping?
This is a very simple look at getting a basic list page of data into a spreadsheet and the images into a Zip folder of image files.
There’s much more you can do, such as:
Link this listing page to data contained on the detail pages for each product.
Schedule a change report to run daily to track when prices change or items are removed or added to the category.
Compare product prices on Amazon to other online retailers, such as Walmart, Target, etc.
Visualize the data in charts and graphs using Insights.
Feed this data into your internal processes or analysis tools via the APIs.
Web scraping is a powerful, automated way to get data from a website. If your data needs are massive or your websites trickier, offers data as a service and we will get your web data for you.
No matter what or how much web data you need, can help. We offer the world’s only web data integration platform which not only extracts data from a website, it identifies, prepares, integrates, and consumes it. This platform can meet an organization’s consumption needs for business applications, analytics, and other processes. You can start by talking to a data expert to determine the best solution for your data needs, or you can give the platform a try yourself. Sign up for a free seven day trial, or we’ll handle all the work for you.

Frequently Asked Questions about how to scrape products from e commerce

Is scraping eCommerce legal?

Copyright Infringement: In most jurisdictions, web scraping is legal, but using copyright data contains certain restrictions. Violation of the Computer Fraud and Abuse Act (CFAA): This law, enacted to prevent computer hackers, prevents fetching data by getting unauthorized access to a page.Apr 6, 2020

How do I scrape prices from eCommerce website?

How to scrape Prices from any eCommerce websiteCreate your own Price Monitoring Tool to Scrape Prices.Web Scraping using Price Scraping Tools.Custom Price Monitoring Solution.Sep 13, 2018

How do I extract a product from a website?

Steps to get data from a websiteFirst, find the page where your data is located.Copy and paste the URL from that page into it’s done, you can decide if the extracted data is what you then populates the rest of the column for the product names and prices.Aug 9, 2018

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