Gumtree Data Scraper
Data scraping: “everybody else was doing it, so I thought it was ok”
By Angus McLean, Partner, Simmons & Simmons LLP
Published: 30 September 2015
I learnt to my cost as a schoolboy that while there can be considerable merit in taking a risk-based approach to compliance decisions, the “everybody else was doing it” defence tends not to hold much water if you are the unlucky one who gets caught. In no area of my practice have I been reminded about this salutary lesson more frequently in recent years than on the issue of data scraping.
A fast growing trend
Call it what you will – data mining, web scraping or any of the other commonly used euphemisms – the practice of systematically extracting data from third party websites (without the permission of the website owner) is on the rise in the hedge fund industry. This can be done manually or, as is more often the case, by specially developed computer programmes. The same legal issues arise in both cases, although it is arguable that manual extraction is marginally less risky because it tends to be harder for a website owner to detect than software-enabled scraping.
The mere fact that data scraping is becoming so ubiquitous seems to be the main cause of the commonly held assumption that it carries no legal risk. However, as the 13 or so European flight price comparison websites that have been the target of Ryanair’s wrath over the last 3-4 years can vouch, my childhood excuse does not provide much insurance against costly litigation.
Is data scraping illegal?
As things currently stand, many acts of data scraping are potentially illegal under UK law. The exact nature of the illegal activity depends on a variety factors. Unfortunately, therefore, every situation needs to be analysed on its own facts. However, the two most common claims that can be brought against data scrapers are (a) breach of contract and (b) IP infringement (specifically, database right infringement). Depending on the precise circumstances, it is possible that a data scraper could also infringe copyright or trade mark rights, breach data protection legislation and/or contravene the Computer Misuse Act 1990.
To have a justified breach of contract claim, the owner of the website in question has to show that its terms and conditions of use (Ts&Cs) are enforceable and have been breached. The second requirement is obviously down to the wording of the Ts&Cs in question. However, it is becoming increasingly common for website Ts&Cs to expressly prohibit data scraping (or equivalent activities). The other issue is whether the data scraper is technically bound by the Ts&Cs in question.
At present there is no clear English case law on this issue. However, it is reasonably safe to assume that any Ts&Cs that a user has had to “click to accept” will be binding. If the Ts&Cs are binding and rule out data scraping, then in the vast majority of cases the website owner will have a valid breach of contract claim.
Determining whether there is also a database right infringement claim is also a highly fact specific exercise. The analysis will depend on:
the type and volume of data that is being extracted;
the frequency with which the data is being extracted; and
the level of investment that was required to develop the database from which the data is being extracted.
If the database required a substantial investment to put together and data is being taken on a systematic basis, database right infringement may also be an issue.
What are the risks in practice?
To date, relatively few European website owners seem to have been sufficiently exercised about third parties extracting data from their sites to pursue full-blown litigation. That said, as the Ryanair cases show, past performance is no guarantee of future results. It is, therefore, important to understand what the consequences of a data scraping complaint might be to provide the proper context for any risk-based analysis of whether those risks are outweighed by the benefits the scraping activities are expected to generate.
Depending on the type of claim that is available to the website owner in question, the key risks faced by a data scraper under UK law are likely to be:
injunction (including pre-trial injunctions);
financial liability (in the form of damages or, in certain circumstances, an account of profits);
disclosure obligations; and
reputational damage.
Although the final two risks are not really formal legal remedies, in my experience they have just as much of a deterrent effect as the more traditional legal remedies (e. g. injunctions and damages or an account of profits). This is because the prospect of having to disclose the type of investment activities for which the data in question is being used, is often seen as the most commercially damaging consequence of a data scraping dispute. Of course, as with the other risks identified above, it may be possible to avoid having to disclose information about the ends to which the data is being applied by settling a potential claim before it escalates into full-blown litigation. However, assuming that will be possible in every case clearly involves a degree of risk in itself.
The calculation method that will be used to determine any financial liability a fund might incur also plays a big part in the risk analysis. The precise calculation method that applies will depend on the type of claims that are available to the website owner (in particular, whether it has a valid claim for database right infringement as well as breach of contract). If it is limited to a contractual claim, a website owner will generally only be able to recover the loss it has incurred. If it does not license out the data in question, its loss may well be negligible. In such circumstances the website owner might be able to claim damages based on a notional reasonable royalty set by the court by reference to the licence fees that are charged for similar datasets.
If a website owner also has a valid claim for database right infringement, it is entitled to opt for an account of the profits the fund has made from its infringing activities. Clearly, such an award could be substantial if the fund generates significant profits directly from the use of the data in question. However, it is often the case that the data in question forms just one data point in a model that includes a variety of other factors. In that case, the fund’s liability should be limited to the proportion of any profits that are attributable to the use of the data in question only.
This means that it may ultimately be difficult for a website owner to identify any significant profits that are directly attributable to the use of the data in question. Unfortunately, that will not necessarily prevent a sufficiently motivated website owner from trying.
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What Is Data Scraping And How Can You Use It? | Target Internet
What Is Data Scraping? Data scraping, also known as web scraping, is the process of importing information from a website into a spreadsheet or local file saved on your computer. It’s one of the most efficient ways to get data from the web, and in some cases to channel that data to another website. Popular uses of data scraping include:Research for web content/business intelligencePricing for travel booker sites/price comparison sitesFinding sales leads/conducting market research by crawling public data sources (e. g. Yell and Twitter)Sending product data from an e-commerce site to another online vendor (e. Google Shopping)And that list’s just scratching the surface. Data scraping has a vast number of applications – it’s useful in just about any case where data needs to be moved from one place to basics of data scraping are relatively easy to master. Let’s go through how to set up a simple data scraping action using Scraping with dynamic web queries in Microsoft ExcelSetting up a dynamic web query in Microsoft Excel is an easy, versatile data scraping method that enables you to set up a data feed from an external website (or multiple websites) into a this excellent tutorial video to learn how to import data from the web to Excel – or, if you prefer, use the written instructions below:Open a new workbook in ExcelClick the cell you want to import data intoClick the ‘Data’ tabClick ‘Get external data’Click the ‘From web’ symbolNote the little yellow arrows that appear to the top-left of web page and alongside certain contentPaste the URL of the web page you want to import data from into the address bar (we recommend choosing a site where data is shown in tables)Click ‘Go’Click the yellow arrow next to the data you wish to importClick ‘Import’An ‘Import data’ dialogue box pops upClick ‘OK’ (or change the cell selection, if you like)If you’ve followed these steps, you should now be able to see the data from the website set out in your great thing about dynamic web queries is that they don’t just import data into your spreadsheet as a one-off operation – they feed it in, meaning the spreadsheet is regularly updated with the latest version of the data, as it appears on the source website. That’s why we call them configure how regularly your dynamic web query updates the data it imports, go to ‘Data’, then ‘Properties’, then select a frequency (“Refresh every X minutes”). Automated data scraping with toolsGetting to grips with using dynamic web queries in Excel is a useful way to gain an understanding of data scraping. However, if you intend to use data regularly scraping in your work, you may find a dedicated data scraping tool more are our thoughts on a few of the most popular data scraping tools on the market:Data Scraper (Chrome plugin)Data Scraper slots straight into your Chrome browser extensions, allowing you to choose from a range of ready-made data scraping “recipes” to extract data from whichever web page is loaded in your tool works especially well with popular data scraping sources like Twitter and Wikipedia, as the plugin includes a greater variety of recipe options for such tried Data Scraper out by mining a Twitter hashtag, “#jourorequest”, for PR opportunities, using one of the tool’s public recipes. Here’s a flavour of the data we got back:As you can see, the tool has provided a table with the username of every account which had posted recently on the hashtag, plus their tweet and its URLHaving this data in this format would be more useful to a PR rep than simply seeing the data in Twitter’s browser view for a number of reasons: It could be used to help create a database of press contactsYou could keep referring back to this list and easily find what you’re looking for, whereas Twitter continuously updatesThe list is sortable and editableIt gives you ownership of the data – which could be taken offline or changed at any momentWe’re impressed with Data Scraper, even though its public recipes are sometimes slightly rough-around-the-edges. Try installing the free version on Chrome, and have a play around with extracting data. Be sure to watch the intro movie they provide to get an idea of how the tool works and some simple ways to extract the data you want. WebHarvyWebHarvy is a point-and-click data scraper with a free trial version. Its biggest selling point is its flexibility – you can use the tool’s in-built web browser to navigate to the data you would like to import, and can then create your own mining specifications to extract exactly what you need from the source is a feature-rich data mining tool suite that does much of the hard work for you. Has some interesting features, including “What’s changed? ” reports that can notify you of updates to specified websites – ideal for in-depth competitor are marketers using data scraping? As you will have gathered by this point, data scraping can come in handy just about anywhere where information is used. Here are some key examples of how the technology is being used by marketers:Gathering disparate dataOne of the great advantages of data scraping, says Marcin Rosinski, CEO of FeedOptimise, is that it can help you gather different data into one place. “Crawling allows us to take unstructured, scattered data from multiple sources and collect it in one place and make it structured, ” says Marcin. “If you have multiple websites controlled by different entities, you can combine it all into one feed. “The spectrum of use cases for this is infinite. ”FeedOptimise offers a wide variety of data scraping and data feed services, which you can find out about at their website. Expediting researchThe simplest use for data scraping is retrieving data from a single source. If there’s a web page that contains lots of data that could be useful to you, the easiest way to get that information onto your computer in an orderly format will probably be data finding a list of useful contacts on Twitter, and import the data using data scraping. This will give you a taste of how the process can fit into your everyday work. Outputting an XML feed to third party sitesFeeding product data from your site to Google Shopping and other third party sellers is a key application of data scraping for e-commerce. It allows you to automate the potentially laborious process of updating your product details – which is crucial if your stock changes often. “Data scraping can output your XML feed for Google Shopping, ” says Target Internet’s Marketing Director, Ciaran Rogers. “ I have worked with a number of online retailers retailer who were continually adding new SKU’s to their site as products came into stock. If your E-commerce solution doesn’t output a suitable XML feed that you can hook up to your Google Merchant Centre so you can advertise your best products that can be an issue. Often your latest products are potentially the best sellers, so you want to get them advertised as soon as they go live. I’ve used data scraping to produce up-to-date listings to feed into Google Merchant Centre. It’s a great solution, and actually, there is so much you can do with the data once you have it. Using the feed, you can tag the best converting products on a daily basis so you can share that information with Google Adwords and ensure you bid more competitively on those products. Once you set it up its all quite automated. The flexibility a good feed you have control of in this way is great, and it can lead to some very definite improvements in those campaigns which clients love. ”It’s possible to set up a simple data feed into Google Merchant Centre for yourself. Here’s how it’s done:How to set up a data feed to Google Merchant CentreUsing one of the techniques or tools described previously, create a file that uses a dynamic website query to import the details of products listed on your site. This file should automatically update at regular details should be set out as specified this file to a password-protected URLGo to Google Merchant Centre and log in (make sure your Merchant Centre account is properly set up first)Go to ProductsClick the plus buttonEnter your target country and create a feed nameSelect the ‘scheduled fetch’ optionAdd the URL of your product data file, along with the username and password required to access itSelect the fetch frequency that best matches your product upload scheduleClick SaveYour product data should now be available in Google Merchant Centre. Just make sure you Click on the ‘Diagnostics’ tab to check it’s status and ensure it’s all working dark side of data scrapingThere are many positive uses for data scraping, but it does get abused by a small minority most prevalent misuse of data scraping is email harvesting – the scraping of data from websites, social media and directories to uncover people’s email addresses, which are then sold on to spammers or scammers. In some jurisdictions, using automated means like data scraping to harvest email addresses with commercial intent is illegal, and it is almost universally considered bad marketing web users have adopted techniques to help reduce the risk of email harvesters getting hold of their email address, including:Address munging: changing the format of your email address when posting it publicly, e. typing ‘patrick[at]’ instead of ‘’. This is an easy but slightly unreliable approach to protecting your email address on social media – some harvesters will search for various munged combinations as well as emails in a normal format, so it’s not entirely ntact forms: using a contact form instead of posting your email address(es) on your if your email address is presented in image form on your website, it will be beyond the technological reach of most people involved in email Data Scraping FutureWhether or not you intend to use data scraping in your work, it’s advisable to educate yourself on the subject, as it is likely to become even more important in the next few are now data scraping AI on the market that can use machine learning to keep on getting better at recognising inputs which only humans have traditionally been able to interpret – like improvements in data scraping from images and videos will have far-reaching consequences for digital marketers. As image scraping becomes more in-depth, we’ll be able to know far more about online images before we’ve seen them ourselves – and this, like text-based data scraping, will help us do lots of things there’s the biggest data scraper of all – Google. The whole experience of web search is going to be transformed when Google can accurately infer as much from an image as it can from a page of copy – and that goes double from a digital marketing you’re in any doubt over whether this can happen in the near future, try out Google’s image interpretation API, Cloud Vision, and let us know what you think. get your free membership now – absolutely no credit card requiredThe Digital Marketing ToolkitExclusive live video learning sessionsComplete library of The Digital Marketing PodcastThe digital skills benchmarking toolsFree online training courses FREE MEMBERSHIP
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
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
soup = BeautifulSoup(content)
for a in ndAll(‘a’, href=True, attrs={‘class’:’_31qSD5′}):
(‘div’, attrs={‘class’:’_3wU53n’})
(‘div’, attrs={‘class’:’_1vC4OE _2rQ-NK’})
(‘div’, attrs={‘class’:’hGSR34 _2beYZw’})
()
Step 5: Run the code and extract the dataTo run the code, use the below command: python 6: Store the data in a required formatAfter extracting the data, you might want to store it in a format. This format varies depending on your requirement. For this example, we will store the extracted data in a CSV (Comma Separated Value) format. To do this, I will add the following lines to my code:df = Frame({‘Product Name’:products, ‘Price’:prices, ‘Rating’:ratings})
_csv(”, index=False, encoding=’utf-8′)Now, I’ll run the whole code again. A file name “” is created and this file contains the extracted data. I hope you guys enjoyed this article on “Web Scraping with Python”. I hope this blog was informative and has added value to your knowledge. Now go ahead and try Web Scraping. Experiment with different modules and applications of Python. If you wish to know about Web Scraping With Python on Windows platform, then the below video will help you understand how to do Scraping With Python | Python Tutorial | Web Scraping Tutorial | EdurekaThis Edureka live session on “WebScraping using Python” will help you understand the fundamentals of scraping along with a demo to scrape some details from a question regarding “web scraping with Python”? You can ask it on edureka! Forum and we will get back to you at the earliest or you can join our Python Training in Hobart get in-depth knowledge on Python Programming language along with its various applications, you can enroll here for live online Python training with 24/7 support and lifetime access.
Frequently Asked Questions about gumtree data scraper
How do I extract data from Gumtree?
Scrape the product information from GumtreeGo To Web Page – to open the targeted web page.Create a pagination loop – to scrape all the details from multiple pages.Create a “Loop Item” – to loop click into each item on each list.Extract data – to select the data for extraction.More items…•Nov 10, 2020
Is data scraping legal in UK?
Is data scraping illegal? As things currently stand, many acts of data scraping are potentially illegal under UK law. … However, the two most common claims that can be brought against data scrapers are (a) breach of contract and (b) IP infringement (specifically, database right infringement).Sep 30, 2015
What does a data scraper do?
Data scraping, also known as web scraping, is the process of importing information from a website into a spreadsheet or local file saved on your computer. … It’s one of the most efficient ways to get data from the web, and in some cases to channel that data to another website.
