Easy Websites To Scrape

Top 10 Most Scraped Websites in 2020 | Octoparse

Table of Contents
Introduction
Overview
Top 10 scraped websites
Final thoughts
Web scraping is the best data-collection method if you are looking to grab data on web pages. As capital flows around the globe through the Internet, web scraping is widely used among businesses, freelancers and researchers as it helps gather web data on a global basis, accurately and efficiently.
We listed the top 10 most scraped websites here according to how much the Octoparse task templates were used in 2020. As you read along, you may come up with your own web scraping idea. Don’t worry if you are a newbie in web scraping! Octoparse offers pre-built templates for non-coders and you can start your scraping project.
What is an Octoparse task template? For programmers, in order to scrape the web, they are able to write scripts and run it in Python or whatever ways. A task template is like an already written script and the only part you have to do is to figure out what data you want and enter the keywords/URLs on our task template interface.
Note: If you have any problem in the use of templates, please feel free to contact our support:
Ecommerce sites are always the most scraped websites among others, both in frequency and quantity. As shopping online becomes a household lifestyle, ecommerce affects people in all walks of life. Online sellers, storefront retailers and even consumers are all ecommerce data collectors.
Directories sites earn the second rank in the race and this isn’t surprising at all. Directories sites organize businesses by categories thus serve as a functional information filter which is a good pick for efficient data collection. Many are scraping directories sites for contact information to boost their sales leads.
Social media incorporates a wealth of information concerning human opinions, emotions and daily actions. Generally speaking, scraping from social media sites is more challenging than from others. That is because many social media sites employ strong anti-scraping techniques in order to protect users’ privacy. Yet, social media still serves as an important source of information for sentiment analysis and all kinds of research.
Other sites fall into categories such as tourism, job board and search engine. In fact, people of all industries are taking advantage of the web scraping technique to exploit data value to service their interests.
Let’s get to the Top 10 list directly and check out which websites were most scraped in 2020 and how they are helpful for our data collectors!
TOP 10 Most Scraped Websites
Top 10. Mercadolibre
Mercadolibre may not be familiar to all but it is a household ecommerce marketplace in Latin American countries with Brazil as its largest contributor in revenue. The pandemic accelerates its growth and now the company is worth $63 billion on Nasdaq. It is depicted as “Latin America’s answer to China’s Alibaba” in the Financial Times.
found this site the most popular among our Spanish users and we formulated the ready-to-use template where users can enter the listing page URLs and get the product data: product name, price, detail page URL, image URLs, etc.
Top 09. Twitter
According to Statistics, there are around 330 million monthly active users and 145 million daily active users on Twitter. With a great number of users, Twitter is not only a platform for socializing and sharing, but also becomes a perfect place for branding and marketing.
People are seeking data on Twitter for various reasons, namely industrial research, sentiment analysis, customer experience management, etc. And if you read this article about text mining Donald Trump’s tweets, you know tweets data can be used in more different ways.
Task templates for Twitter are widely consulted at our support center and we have delivered a good number of customizable templates for our customers. If you use pre-built templates on Octoparse, you can get post data or profile info from certain authors:
Top 8. Indeed
According to Indeed, the giant job board has received 175 million CVs in total. Seeking jobs online now is so natural that we barely remember how a traditional job fair looks like. Building a job aggregator, especially for niche markets, has become a profitable business in recent years. And guess how people do this? Yes, web scraping is the trick.
Job board builders are not the only people benefit from job sites data. Human Resources professionals, job-seekers, to-be job hoppers, researchers focused on recruitment and job markets are all eager for jobs data. If you are seeking a job, having a big picture of the market always helps with your bargain.
Here is the Indeed sample data captured with Octoparse and actually there are more to explore:
Top 7. Tripadvisor
Travel industry has seen a blow during the pandemic and now the recovery is happening. The need to scrape tourism websites could bounce up as well. While why would people scrape websites like, tripadvisor, Airbnb? One of the examples could be service agents who offer integrated service for tourists, including ticketing, hotel/restaurant booking.
Web scraping is also widely used for price comparison and this is how smart people build price comparison sites to service the public. If you try, you may build a price comparison site for flight tickets to help tourists book the most economic one!
Octoparse’s Tripadvisor template is available both in English and Spanish versions and the data sample below shows hotel details on Tripadvisor. Just enter the search result URL, this is what you can get:
Top 6. Google
With its super machine learning algorithm, Google could be the robot who knows everybody better than their families and friends. That’s all about data. From an individual’s perspective, what can we get from Google?
SEO marketers may be the bunch of people most interested in Google search. They scrape Google search results to monitor a set of keywords, to gather TDK (short for Title, Description, Keywords: metadata of a web page that shows on the result list and has critical influence on the click-through rate) information for a SEO optimization strategy.
In addition to google search result extraction, Octoparse offer template for Google Map as well. Enter the URL of the search result page, Octoparse will get you well-organized data of the related stores:
Top 5. Yellowpages
According to Wikipedia,, also known as “YP”, was founded in 1996 and over decades of development, the site has developed into the most well-known directory web site and hosts 60 million visitors per month.
Well, in the eyes of web scraping people, yellowpages is the perfect place to gather contact information and addresses of businesses based on location. If you are a retailer and finding competitors in your area is as simple as a few clicks. If you are a salesman and looking to generate sales leads efficiently? Check out this story and you will know what I am talking about.
Below screenshot shows what data Octoparse template can get for you: shop name, rating, address, phone number, etc. And the data can be exported into forms like Excel, CSV and JSON. Inspired by the sample data below? Check out this leads generation with web scraping step by step guide.
Top 4. Yelp
Same as, Yelp can get you businesses data based on location. And there’s more. When you are travelling around and a question pops up in your mind: who has the best pizza in the city? That’s where Yelp comes into the scene. Yelp serves not only as a business directory but also a free consultant for consumers in food-hunting, home services and who are looking for a good massage.
That’s about ranking and reviews, which is gold data for businesses. Those scraping Yelp are capitalizing on the reviews and ranking data to get an idea of what their business looks like in a customer’s eye and also for competition analysis.
>>You may interested in this video: Scrape from Yelp SIMPLE & EASY
Yelp template is available on Octoparse. This is how the data looks like:
Top 3. Walmart
If you are interested in the retail business landscape, this article from Vox has portrayed an image of how retailers use data to track every move of their customers in order to promote sales. While the real thing is that data is also used to form a transparent market and serve shoppers’ interests.
Price comparison sites are generated under the work of web scraping. Walmart can be one of the targets to scrape from as its slogan reads “Save Money Live better”. That’s one of the reasons people are scraping from Walmart. For retailers and groceries, Walmart is also an important source of information to get the product data for a market research.
>>Check out this guide to scrape from Walmart
Walmart template is available on Octoparse. This is how the data looks like:
Top 2. eBay
Ecommerce websites are always those most popular websites for web scraping and eBay is definitely one of them. We have many users running their own businesses on eBay and getting data from eBay is an important way to keep track of their competitors and follow the market trend.
There is a customer story mostly impressive to me. The customer is an eBay seller and he is diligently scraping data from eBay and other ecommerce marketplaces regularly, building up his own database across time for in-depth market research.
>>If you are interested in using Octoparse eBay template, check this out: Scraping from eBay guide and if you are confident to build your own crawler on Octoparse, this video can guide you through the crawler building process.
Top 1. Amazon
Yes it is not surprising that Amazon ranks the most scraped website. Amazon is taking the giant shares in the ecommerce business which means that Amazon data is the most representative for any kind of market research. It has the largest database.
While, getting ecommerce data faces challenges. The biggest challenge for scraping Amazon could be the captcha and we get it handled. Captcha is a way to prevent the site’s from crashing as too many are craving for Amazon data and frequent scraping can overload the servers. Octoparse employs cloud extraction and IP rotation which can perfectly nail it.
Scraping from Amazon can give you data for all below purposes:
Price tracking
Competition analysis
MAP monitoring
Product selection
Sentiment analysis
…
>>More to know about why scraping ecommerce websites
Using Octoparse Amazon template, you can gather product data like ASIN, star rating, price, color, style, reviews and more.
style=”font-size: 10pt;”>Octoparse Amazon scraper sample data
Final Thoughts
Data is the new oil while without a handy tool, not everyone is able to exploit the value out of it. Octoparse is working to make data more easily accessible to the public whether they can code or not. In this way, all of us can get a hand on the needed data and create value for the world through data analysis.
If you are interested in generating original opinions and just lack the data to back you, get your data!
Author: Cici
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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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A Beginner’s Guide to learn web scraping with python! – Edureka

Last updated on Sep 24, 2021 641. 9K Views Tech Enthusiast in Blockchain, Hadoop, Python, Cyber-Security, Ethical Hacking. Interested in anything… Tech Enthusiast in Blockchain, Hadoop, Python, Cyber-Security, Ethical Hacking. Interested in anything and everything about Computers. 1 / 2 Blog from Web Scraping Web Scraping with PythonImagine you have to pull a large amount of data from websites and you want to do it as quickly as possible. How would you do it without manually going to each website and getting the data? Well, “Web Scraping” is the answer. Web Scraping just makes this job easier and faster. In this article on Web Scraping with Python, you will learn about web scraping in brief and see how to extract data from a website with a demonstration. I will be covering the following topics: Why is Web Scraping Used? What Is Web Scraping? Is Web Scraping Legal? Why is Python Good For Web Scraping? How Do You Scrape Data From A Website? Libraries used for Web Scraping Web Scraping Example: Scraping Flipkart Website Why is Web Scraping Used? Web scraping is used to collect large information from websites. But why does someone have to collect such large data from websites? To know about this, let’s look at the applications of web scraping: Price Comparison: Services such as ParseHub use web scraping to collect data from online shopping websites and use it to compare the prices of products. Email address gathering: Many companies that use email as a medium for marketing, use web scraping to collect email ID and then send bulk emails. Social Media Scraping: Web scraping is used to collect data from Social Media websites such as Twitter to find out what’s trending. Research and Development: Web scraping is used to collect a large set of data (Statistics, General Information, Temperature, etc. ) from websites, which are analyzed and used to carry out Surveys or for R&D. Job listings: Details regarding job openings, interviews are collected from different websites and then listed in one place so that it is easily accessible to the is Web Scraping? Web scraping is an automated method used to extract large amounts of data from websites. The data on the websites are unstructured. Web scraping helps collect these unstructured data and store it in a structured form. There are different ways to scrape websites such as online Services, APIs or writing your own code. In this article, we’ll see how to implement web scraping with python. Is Web Scraping Legal? Talking about whether web scraping is legal or not, some websites allow web scraping and some don’t. To know whether a website allows web scraping or not, you can look at the website’s “” file. You can find this file by appending “/” to the URL that you want to scrape. For this example, I am scraping Flipkart website. So, to see the “” file, the URL is in-depth Knowledge of Python along with its Diverse Applications Why is Python Good for Web Scraping? Here is the list of features of Python which makes it more suitable for web scraping. Ease of Use: Python is simple to code. You do not have to add semi-colons “;” or curly-braces “{}” anywhere. This makes it less messy and easy to use. Large Collection of Libraries: Python has a huge collection of libraries such as Numpy, Matlplotlib, Pandas etc., which provides methods and services for various purposes. Hence, it is suitable for web scraping and for further manipulation of extracted data. Dynamically typed: In Python, you don’t have to define datatypes for variables, you can directly use the variables wherever required. This saves time and makes your job faster. Easily Understandable Syntax: Python syntax is easily understandable mainly because reading a Python code is very similar to reading a statement in English. It is expressive and easily readable, and the indentation used in Python also helps the user to differentiate between different scope/blocks in the code. Small code, large task: Web scraping is used to save time. But what’s the use if you spend more time writing the code? Well, you don’t have to. In Python, you can write small codes to do large tasks. Hence, you save time even while writing the code. Community: What if you get stuck while writing the code? You don’t have to worry. Python community has one of the biggest and most active communities, where you can seek help Do You Scrape Data From A Website? When you run the code for web scraping, a request is sent to the URL that you have mentioned. As a response to the request, the server sends the data and allows you to read the HTML or XML page. The code then, parses the HTML or XML page, finds the data and extracts it. To extract data using web scraping with python, you need to follow these basic steps: Find the URL that you want to scrape Inspecting the Page Find the data you want to extract Write the code Run the code and extract the data Store the data in the required format Now let us see how to extract data from the Flipkart website using Python, Deep Learning, NLP, Artificial Intelligence, Machine Learning with these AI and ML courses a PG Diploma certification program by NIT braries used for Web Scraping As we know, Python is has various applications and there are different libraries for different purposes. In our further demonstration, we will be using the following libraries: Selenium: Selenium is a web testing library. It is used to automate browser activities. BeautifulSoup: Beautiful Soup is a Python package for parsing HTML and XML documents. It creates parse trees that is helpful to extract the data easily. Pandas: Pandas is a library used for data manipulation and analysis. It is used to extract the data and store it in the desired format. Subscribe to our YouTube channel to get new updates..! Web Scraping Example: Scraping Flipkart WebsitePre-requisites: Python 2. x or Python 3. x with Selenium, BeautifulSoup, pandas libraries installed Google-chrome browser Ubuntu Operating SystemLet’s get started! Step 1: Find the URL that you want to scrapeFor this example, we are going scrape Flipkart website to extract the Price, Name, and Rating of Laptops. The URL for this page is 2: Inspecting the PageThe data is usually nested in tags. So, we inspect the page to see, under which tag the data we want to scrape is nested. To inspect the page, just right click on the element and click on “Inspect” you click on the “Inspect” tab, you will see a “Browser Inspector Box” 3: Find the data you want to extractLet’s extract the Price, Name, and Rating which is in the “div” tag respectively. Learn Python in 42 hours! Step 4: Write the codeFirst, let’s create a Python file. To do this, open the terminal in Ubuntu and type gedit with extension. I am going to name my file “web-s”. Here’s the command:gedit, let’s write our code in this file. First, let us import all the necessary libraries:from selenium import webdriver
from BeautifulSoup import BeautifulSoup
import pandas as pdTo configure webdriver to use Chrome browser, we have to set the path to chromedriverdriver = (“/usr/lib/chromium-browser/chromedriver”)Refer the below code to open the URL: products=[] #List to store name of the product
prices=[] #List to store price of the product
ratings=[] #List to store rating of the product
(“)
Now that we have written the code to open the URL, it’s time to extract the data from the website. As mentioned earlier, the data we want to extract is nested in