Web Scraper – The #1 web scraping extension
More than
400, 000 users are proud of using our solutions!
Point and click
interface
Our goal is to make web data extraction as simple as possible.
Configure scraper by simply pointing and clicking on elements.
No coding required.
Extract data from dynamic
web sites
Web Scraper can extract data from sites with multiple levels of navigation. It can navigate a
website on all levels.
Categories and subcategories
Pagination
Product pages
Built for the modern web
Websites today are built on top of JavaScript frameworks that make user interface easier to use but
are less accessible to scrapers. Web Scraper solves this by:
Full JavaScript execution
Waiting for Ajax requests
Pagination handlers
Page scroll down
Modular selector system
Web Scraper allows you to build Site Maps from different types of selectors.
This system makes it possible to tailor data extraction to different site structures.
Export data in CSV, XLSX and JSON
formats
Build scrapers, scrape sites and export data in CSV format directly from your browser.
Use Web Scraper Cloud to export data in CSV, XLSX and JSON formats, access it via API, webhooks or
get it exported via Dropbox.
Diego Kremer
Simply AMAZING. Was thinking about coding myself a simple scraper for a project
and then found this super easy to use and very powerful scraper. Worked
perfectly with all the websites I tried on. Saves a lot of time. Thanks for
that!
Carlos Figueroa
Powerful tool that beats the others out there. Has a learning curve to it but
once you conquer that the sky’s the limit. Definitely a tool worth making a
donation on and supporting for continued development. Way to go for the
authoring crew behind this tool.
Jonathan H
This is fantastic! I’m saving hours, possibly days. I was trying to scrap and old
site, badly made, no proper divs or markup.
Using the WebScraper magic, it somehow “knew” the pattern after I selected 2
elements. Amazing.
Yes, it’s a learning curve and you HAVE to watch the video and read the docs.
Don’t rate it down just because you can’t be bothered to learn it. If you put
the effort in, this will save your butt one day!
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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Web Scraping Basics – Towards Data Science
How to scrape data from a website in PythonWe always say “Garbage in Garbage out” in data science. If you do not have good quality and quantity of data, most likely you would not get many insights out of it. Web Scraping is one of the important methods to retrieve third-party data automatically. In this article, I will be covering the basics of web scraping and use two examples to illustrate the 2 different ways to do it in is Web ScrapingWeb Scraping is an automatic way to retrieve unstructured data from a website and store them in a structured format. For example, if you want to analyze what kind of face mask can sell better in Singapore, you may want to scrape all the face mask information on an E-Commerce website like you scrape from all the websites? Scraping makes the website traffic spike and may cause the breakdown of the website server. Thus, not all websites allow people to scrape. How do you know which websites are allowed or not? You can look at the ‘’ file of the website. You just simply put after the URL that you want to scrape and you will see information on whether the website host allows you to scrape the for an file of can see that Google does not allow web scraping for many of its sub-websites. However, it allows certain paths like ‘/m/finance’ and thus if you want to collect information on finance then this is a completely legal place to scrape. Another note is that you can see from the first row on User-agent. Here Google specifies the rules for all of the user-agents but the website may give certain user-agent special permission so you may want to refer to information does web scraping work? Web scraping just works like a bot person browsing different pages website and copy pastedown all the contents. When you run the code, it will send a request to the server and the data is contained in the response you get. What you then do is parse the response data and extract out the parts you do we do web scraping? Alright, finally we are here. There are 2 different approaches for web scraping depending on how does website structure their roach 1: If website stores all their information on the HTML front end, you can directly use code to download the HTML contents and extract out useful are roughly 5 steps as below:Inspect the website HTML that you want to crawlAccess URL of the website using code and download all the HTML contents on the pageFormat the downloaded content into a readable formatExtract out useful information and save it into a structured formatFor information displayed on multiple pages of the website, you may need to repeat steps 2–4 to have the complete and Cons for this approach: It is simple and direct. However, if the website’s front-end structure changes then you need to adjust your code roach 2: If website stores data in API and the website queries the API each time when user visit the website, you can simulate the request and directly query data from the APISteps:Inspect the XHR network section of the URL that you want to crawlFind out the request-response that gives you the data that you wantDepending on the type of request(post or get) and also the request header & payload, simulate the request in your code and retrieve the data from API. Usually, the data got from API is in a pretty neat format. Extract out useful information that you needFor API with a limit on query size, you will need to use ‘for loop’ to repeatedly retrieve all the dataPros and Cons for this approach: It is definitely a preferred approach if you can find the API request. The data you receive will be more structured and stable. This is because compared to the website front end, it is less likely for the company to change its backend API. However, it is a bit more complicated than the first approach especially if authentication or token is required. Different tools and library for web scrapingThere are many different scraping tools available that do not require any coding. However, most people still use the Python library to do web scraping because it is easy to use and also you can find an answer in its big most commonly used library for web scraping in Python is Beautiful Soup, Requests, and autiful Soup: It helps you parse the HTML or XML documents into a readable format. It allows you to search different elements within the documents and help you retrieve required information quests: It is a Python module in which you can send HTTP requests to retrieve contents. It helps you to access website HTML contents or API by sending Get or Post lenium: It is widely used for website testing and it allows you to automate different events(clicking, scrolling, etc) on the website to get the results you can either use Requests + Beautiful Soup or Selenium to do web scraping. Selenium is preferred if you need to interact with the website(JavaScript events) and if not I will prefer Requests + Beautiful Soup because it’s faster and Scraping Example:Problem statement: I want to find out about the local market for face mask. I am interested on online face mask price, discount, ratings, sold quantity roach 1 Example(Download HTML for all pages) — Lazada:Step 1: Inspect the website(if using Chrome you can right-click and select inspect)Inspect Lazada page on ChromeHTML result for price on LazadaI can see that data I need are all wrap in the HTML element with the unique class 2: Access URL of the website using code and download all the HTML contents on the page# import libraryfrom bs4 import BeautifulSoupimport requests# Request to website and download HTML contentsurl=”(url)content=req. textRequest content before applying Beautiful SoupI used the requests library to get data from a website. You can see that so far what we have is unstructured 3: Format the downloaded content into a readable formatsoup=BeautifulSoup(content)This step is very straightforward and what we do is just parse unstructured text into Beautiful Soup and what you get is as content after using Beautiful SoupThe output is a much more readable format and you can search different HTML elements or classes in 4: Extract out useful information and save it into a structured formatThis step requires some time to understand website structure and find out where the data is stored exactly. For the Lazada case, it is stored in a Script section in JSON (‘script’)[3]ad_json((“geData=”)[1], orient=’records’)#Store datafor item in [‘listItems’, ‘mods’]: (item[‘brandName’]) (item[‘price’]) (item[‘location’]) (ifnull(item[‘description’], 0)) (ifnull(item[‘ratingScore’], 0))I created 5 different lists to store the different fields of data that I need. I used the for loop here to loop through the list of items in the JSON documents inside. After that, I combine the 5 columns into the output file. #save data into an Frame({‘brandName’:brand_name, ‘price’:price, ‘location’:location, ‘description’:description, ‘rating score’:rating_score})Final output in Python DataFrame formatStep 5: For information displayed on multiple pages of the website, you may need to repeat steps 2–4 to have the complete you want to scrape all the data. Firstly you should find out about the total count of sellers. Then you should loop through pages by passing in incremental page numbers using payload to URL. Below is the full code that I used to scrape and I loop through the first 50 pages to get content on those i in range(1, 50): (max((5, 1), 2)) print(‘page’+str(i)) payload[‘page’]=i (url, params=payload) soup=BeautifulSoup(content) ndAll(‘script’)[3] ad_json((“geData=”)[1], orient=’records’) for item in [‘listItems’, ‘mods’]: (item[‘brandName’]) (item[‘price’]) (item[‘location’]) (ifnull(item[‘description’], 0)) (ifnull(item[‘ratingScore’], 0))Approach 2 example(Query data directly from API) — Ezbuy:Step 1: Inspect the XHR network section of the URL that you want to crawl and find out the request-response that gives you the data that you wantXHR section under Network — Product list API request and responseI can see from the Network that all product information is listed in this API called ‘List Product by Condition’. The response gives me all the data I need and it is a POST 2: Depending on the type of request(post or get) and also the request header & payload, simulate the request in your code and retrieve the data from API. #Define API urlurl_search=”#Define header for the post requestheaders={‘user-agent’:’Mozilla/5. 0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537. 36 (KHTML, like Gecko) Chrome/83. 0. 4103. 116 Safari/537. 36′}#Define payload for the request formdata={ “searchCondition”: {“categoryId”:0, “freeShippingType”:0, “filter: [], “keyWords”:”mask”}, “limit”:100, “offset”:0, “language”:”en”, “dataType”:”new”} (url_search, headers=headers, json=data)Here I create the HTTP POST request using the requests library. For post requests, you need to define the request header(setting of the request) and payload(data you are sending with this post request). Sometimes token or authentication is required here and you will need to request for token first before sending your POST request. Here there is no need to retrieve the token and usually just follow what’s in the request payload in Network and define ‘user-agent’ for the header. Another thing to note here is that inside the payload, I specified limit as 100 and offset as 0 because I found out it only allows me to query 100 data rows at one time. Thus, what we can do later is to use for loop to change offset and query more data 3: Extract out useful information that you need#read the data back as json ()# Store data into the fields for item in j[‘products’]: (item[‘price’]) (item[‘originCode’]) (item[‘name’]) (item[‘leftView’][‘rateScore’]) (item[‘rightView’][‘text’](‘ Sold’)[0]#Combine all the columns Frame({‘Name’:name, ‘price’:price, ‘location’:location, ‘Rating Score’:ratingScore, ‘Quantity Sold’:quantity})Data from API is usually quite neat and structured and thus what I did was just to read it in JSON format. After that, I extract the useful data into different columns and combine them together as output. You can see the data output face mask data outputStep 4: For API with a limit on query size, you will need to use ‘for loop’ to repeatedly retrieve all the data#Define API urlurl_search=”#Define header for the post requestheaders={‘user-agent’:’Mozilla/5. 36′}for i in range(0, 14000, 100): (max((3, 1), 2)) print(i) data={ “searchCondition”: {“categoryId”:0, “freeShippingType”:0, “filters”: [], “keyWords”:”mask”}, “limit”:100, “offset”:i, “language”:”en”, “dataType”:”new”} (url_search, headers=headers, json=data) () for item in j[‘products’]: (item[‘price’]) (item[‘originCode’]) (item[‘name’]) (item[‘leftView’][‘rateScore’]) (item[‘rightView’][‘text’](‘ Sold’)[0])#Combine all the columns Frame({‘Name’:name, ‘price’:price, ‘location’:location, ‘Rating Score’:ratingScore, ‘Quantity Sold’:quantity})Here is the complete code to scrape all rows of face mask data in Ezbuy. I found that the total number of rows is 14k and thus I write a for loop to loop through incremental offset number to query all the results. Another important thing to note here is that I put a random timeout at the start of each loop. This is because I do not want very frequent HTTP requests to harm the traffic of the website and get spotted out by the nally, RecommendationIf you want to scrape a website, I would suggest checking the existence of API first in the network section using inspect. If you can find the response to a request that gives you all the data you need, you can build a stable and neat solution. If you cannot find the data in-network, you should try using requests or Selenium to download HTML content and use Beautiful Soup to format the data. Lastly, please use a timeout to avoid a too frequent visits to the website or API. This may prevent you from being blocked by the website and it helps to alleviate the traffic for the good of the website.
Frequently Asked Questions about html scraper
Is HTML scraping legal?
Web scraping and crawling aren’t illegal by themselves. … 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.
How do you scrape in HTML?
How do we do web scraping?Inspect the website HTML that you want to crawl.Access URL of the website using code and download all the HTML contents on the page.Format the downloaded content into a readable format.Extract out useful information and save it into a structured format.More items…•Jul 15, 2020
How do you scrape a website?
This is what a general DIY web scraping process looks like:Identify the target website.Collect URLs of the pages where you want to extract data from.Make a request to these URLs to get the HTML of the page.Use locators to find the data in the HTML.Save the data in a JSON or CSV file or some other structured format.