How To Screen Scrape A Website

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.
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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What Is Web Scraping And How Does It Work? |

What Is Web Scraping And How Does It Work? |

In today’s competitive world everybody is looking for ways to innovate and make use of new technologies. Web scraping (also called web data extraction or data scraping) provides a solution for those who want to get access to structured web data in an automated fashion. Web scraping is useful if the public website you want to get data from doesn’t have an API, or it does but provides only limited access to the data.
In this article, we are going to shed some light on web scraping, here’s what you will learn:
What is web scraping? The basics of web scrapingWhat is the web scraping process? What is web scraping used for? The best resources to learn more about web scraping
What is web scraping?
Web scraping is the process of collecting structured web data in an automated fashion. It’s also called web data extraction. Some of the main use cases of web scraping include price monitoring, price intelligence, news monitoring, lead generation, and market research among many others.
In general, web data extraction is used by people and businesses who want to make use of the vast amount of publicly available web data to make smarter decisions.
If you’ve ever copy and pasted information from a website, you’ve performed the same function as any web scraper, only on a microscopic, manual scale. Unlike the mundane, mind-numbing process of manually extracting data, web scraping uses intelligent automation to retrieve hundreds, millions, or even billions of data points from the internet’s seemingly endless frontier.
Web scraping is popular
And it should not be surprising because web scraping provides something really valuable that nothing else can: it gives you structured web data from any public website.
More than a modern convenience, the true power of data web scraping lies in its ability to build and power some of the world’s most revolutionary business applications. ‘Transformative’ doesn’t even begin to describe the way some companies use web scraped data to enhance their operations, informing executive decisions all the way down to individual customer service experiences.
The basics of web scraping
It’s extremely simple, in truth, and works by way of two parts: a web crawler and a web scraper. The web crawler is the horse, and the scraper is the chariot. The crawler leads the scraper, as if by hand, through the internet, where it extracts the data requested. Learn the difference between web crawling & web scraping and how they work.
The crawler
A web crawler, which we generally call a “spider, ” is an artificial intelligence that browses the internet to index and search for content by following links and exploring, like a person with too much time on their hands. In many projects, you first “crawl” the web or one specific website to discover URLs which then you pass on to your scraper.
The scraper
A web scraper is a specialized tool designed to accurately and quickly extract data from a web page. Web scrapers vary widely in design and complexity, depending on the project. An important part of every scraper is the data locators (or selectors) that are used to find the data that you want to extract from the HTML file – usually, XPath, CSS selectors, regex, or a combination of them is applied.
The web data scraping process
If you do it yourself
This is what a general DIY web scraping process looks like:
Identify the target websiteCollect URLs of the pages where you want to extract data fromMake a request to these URLs to get the HTML of the pageUse locators to find the data in the HTMLSave the data in a JSON or CSV file or some other structured format
Simple enough, right? It is! If you just have a small project. But unfortunately, there are quite a few challenges you need to tackle if you need data at scale. For example, maintaining the scraper if the website layout changes, managing proxies, executing javascript, or working around antibots. These are all deeply technical problems that can eat up a lot of resources. There are multiple open-source web data scraping tools that you can use but they all have their limitations. That’s part of the reason many businesses choose to outsource their web data projects.
If you outsource it
1. Our team gathers your requirements regarding your project.
2. Our veteran team of web data scraping experts writes the scraper(s) and sets up the infrastructure to collect your data and structure it based on your requirements.
3. Finally, we deliver the data in your desired format and desired frequency.
Ultimately, the flexibility and scalability of web scraping ensure your project parameters, no matter how specific, can be met with ease. Fashion retailers inform their designers with upcoming trends based on web scraped insights, investors time their stock positions, and marketing teams overwhelm the competition with deep insights, all thanks to the burgeoning adoption of web scraping as an intrinsic part of everyday business.
What is web scraping used for?
Price intelligence
In our experience, price intelligence is the biggest use case for web scraping. Extracting product and pricing information from e-commerce websites, then turning it into intelligence is an important part of modern e-commerce companies that want to make better pricing/marketing decisions based on data.
How web pricing data and price intelligence can be useful:
Dynamic pricingRevenue optimizationCompetitor monitoringProduct trend monitoringBrand and MAP compliance
Market research
Market research is critical – and should be driven by the most accurate information available. High quality, high volume, and highly insightful web scraped data of every shape and size is fueling market analysis and business intelligence across the globe.
Market trend analysisMarket pricingOptimizing point of entryResearch & developmentCompetitor monitoring
Alternative data for finance
Unearth alpha and radically create value with web data tailored specifically for investors. The decision-making process has never been as informed, nor data as insightful – and the world’s leading firms are increasingly consuming web scraped data, given its incredible strategic value.
Extracting Insights from SEC FilingsEstimating Company FundamentalsPublic Sentiment IntegrationsNews Monitoring
Real estate
The digital transformation of real estate in the past twenty years threatens to disrupt traditional firms and create powerful new players in the industry. By incorporating web scraped product data into everyday business, agents and brokerages can protect against top-down online competition and make informed decisions within the market.
Appraising Property ValueMonitoring Vacancy RatesEstimating Rental YieldsUnderstanding Market Direction
News & content monitoring
Modern media can create outstanding value or an existential threat to your business – in a single news cycle. If you’re a company that depends on timely news analyses, or a company that frequently appears in the news, web scraping news data is the ultimate solution for monitoring, aggregating, and parsing the most critical stories from your industry.
Investment Decision MakingOnline Public Sentiment AnalysisCompetitor MonitoringPolitical CampaignsSentiment Analysis
Lead generation
Lead generation is a crucial marketing/sales activity for all businesses. In the 2020 Hubspot report, 61% of inbound marketers said generating traffic and leads was their number 1 challenge. Fortunately, web data extraction can be used to get access to structured lead lists from the web.
Brand monitoring
In today’s highly competitive market, it’s a top priority to protect your online reputation. Whether you sell your products online and have a strict pricing policy that you need to enforce or just want to know how people perceive your products online, brand monitoring with web scraping can give you this kind of information.
Business automation
In some situations, it can be cumbersome to get access to your data. Maybe you need to extract data from a website that is your own or your partner’s in a structured way. But there’s no easy internal way to do it and it makes sense to create a scraper and simply grab that data. As opposed to trying to work your way through complicated internal systems.
MAP monitoring
Minimum advertised price (MAP) monitoring is the standard practice to make sure a brand’s online prices are aligned with their pricing policy. With tons of resellers and distributors, it’s impossible to monitor the prices manually. That’s why web scraping comes in handy because you can keep an eye on your products’ prices without lifting a finger.
Learn more about web scraping
Here at Zyte (formerly Scrapinghub), we have been in the web scraping industry for 12 years. With our data extraction services and automatic web scraper, Zyte Automatic Extraction, we have helped extract web data for more than 1, 000 clients ranging from Government agencies and Fortune 100 companies to early-stage startups and individuals. During this time we gained a tremendous amount of experience and expertise in web data extraction.
Here are some of our best resources if you want to deepen your web scraping knowledge:
What are the elements of a web scraping project? Web scraping toolsHow to architect a web scraping solutionIs web scraping legal? Web scraping best practices

Frequently Asked Questions about how to screen scrape a website

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 you scrape a website?

The web data scraping processIdentify 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.

How do you scrape a website safely?

5 Tips For Web Scraping Without Getting Blocked or BlacklistedIP Rotation. … Set a Real User Agent. … Set Other Request Headers. … Set Random Intervals In Between Your Requests. … Set a Referrer. … Use a Headless Browser. … Avoid Honeypot Traps. … Detect Website Changes.More items…

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