SFTW: Scraping data with Google Refine

For the first Something For The Weekend of 2012 I want to tackle a common problem when you’re trying to scrape a collection of webpage: they have some sort of structure in their URL like this, where part of the URL refers to the name or code of an entity:     http://www.ltscotland.org.uk/scottishschoolsonline/schools/freemealentitlement.asp?iSchoolID=5237521

  tp://www.ltscotland.org.uk/scottishschoolsonline/schools/freemealentitlement.asp?iSchoolID=5237629

    ttp://www.ltscotland.org.uk/scottishschoolsonline/schools/freemealentitlement.asp?iSchoolID=5237823

In this instance, you can see that the URL is identical apart from a 7 digit code at the end: the ID of the school the data refers to.

There are a number of ways you could scrape this data. You could use Google Docs and the =importXML formula, but Google Docs will only let you use this 50 times on any one spreadsheet (you could copy the results and select Edit > Paste Special > Values Only and then use the formula a further 50 times if it’s not too many – here’s one I prepared earlier).

And you could use Scraperwiki to write a powerful scraper – but you need to understand enough coding to do so quickly (here’s a demo I prepared earlier).

A middle option is to use Google Refine, and here’s how you do it.

Assembling the ingredients

With the basic URL structure identified, we already have half of our ingredients. What we need  next is a list of the ID codes that we’re going to use to complete each URL.

An advanced search for “list seed number scottish schools filetype:xls” brings up a link to this spreadsheet (XLS) which gives us just that.

The spreadsheet will need editing: remove any rows you don’t need. This will reduce the time that the scraper will take in going through them. For example, if you’re only interested in one local authority, or one type of school, sort your spreadsheet so that you can delete those above or below them.

Now to combine  the ID codes with the base URL.

Bringing your data into Google Refine

Open Google Refine and create a new project with the edited spreadsheet containing the school IDs.

At the top of the school ID column click on the drop-down menu and select Edit column > Add column based on this column…

In the New column name box at the top call this ‘URL’.

In the Expression box type the following piece of GREL (Google Refine Expression Language):

“http://www.ltscotland.org.uk/scottishschoolsonline/schools/freemealentitlement.asp?iSchoolID=”+value

(Type in the quotation marks yourself – if you’re copying them from a webpage you may have problems)

The ‘value’ bit means the value of each cell in the column you just selected. The plus sign adds it to the end of the URL in quotes.

In the Preview window you should see the results – you can even copy one of the resulting URLs and paste it into a browser to check it works. (On one occasion Google Refine added .0 to the end of the ID number, ruining the URL. You can solve this by changing ‘value’ to value.substring(0,7) – this extracts the first 7 characters of the ID number, omitting the ‘.0') UPDATE: in the comment Thad suggests “perhaps, upon import of your spreadsheet of IDs, you forgot to uncheck the importer option to Parse as numbers?”

Click OK if you’re happy, and you should have a new column with a URL for each school ID.

Grabbing the HTML for each page

Now click on the top of this new URL column and select Edit column > Add column by fetching URLs…

In the New column name box at the top call this ‘HTML’.

All you need in the Expression window is ‘value’, so leave that as it is.

Click OK.

Google Refine will now go to each of those URLs and fetch the HTML contents. As we have a couple thousand rows here, this will take a long time – hours, depending on the speed of your computer and internet connection (it may not work at all if either isn’t very fast). So leave it running and come back to it later.

Extracting data from the raw HTML with parseHTML

When it’s finished you’ll have another column where each cell is a bunch of HTML. You’ll need to create a new column to extract what you need from that, and you’ll also need some GREL expressions explained here.

First you need to identify what data you want, and where it is in the HTML. To find it, right-click on one of the webpages containing the data, and search for a key phrase or figure that you want to extract. Around that data you want to find a HTML tag like <table class=”destinations”> or <div id=”statistics”>. Keep that open in another window while you tweak the expression we come onto below…

Back in Google Refine, at the top of the HTML column click on the drop-down menu and select Edit column > Add column based on this column…

In the New column name box at the top give it a name describing the data you’re going to pull out.

In the Expression box type the following piece of GREL (Google Refine Expression Language):

value.parseHtml().select(“table.destinations”)[0].select(“tr”).toString()

(Again, type the quotation marks yourself rather than copying them from here or you may have problems)

I’ll break down what this is doing:

value.parseHtml()

parse the HTML in each cell (value)

.select(“table.destinations”)

find a table with a class (.) of “destinations” (in the source HTML this reads <table class=”destinations”>. If it was <div id=”statistics”> then you would write .select(“div#statistics”) – the hash sign representing an ‘id’ and the full stop representing a ‘class’.

[0]

This zero in square brackets tells Refine to only grab the first table – a number 1 would indicate the second, and so on. This is because numbering (“indexing”) generally begins with zero in programming.

.select(“tr”)

Now, within that table, find anything within the tag <tr>

.toString()

And convert the results into a string of text.

The results of that expression in the Preview window should look something like this:

<tr> <th></th> <th>Abbotswell School</th> <th>Aberdeen City</th> <th>Scotland</th> </tr> <tr> <th>Percentage of pupils</th> <td>25.5%</td> <td>16.3%</td> <td>22.6%</td> </tr>

This is still HTML, but a much smaller and manageable chunk. You could, if you chose, now export it as a spreadsheet file and use various techniques to get rid of the tags (Find and Replace, for example) and split the data into separate columns (the =SPLIT formula, for example).

Or you could further tweak your GREL code in Refine to drill further into your data, like so:

value.parseHtml().select(“table.destinations”)[0].select(“td”)[0].toString()

Which would give you this:

<td>25.5%</td>

Or you can add the .substring function to strip out the HTML like so (assuming that the data you want is always 5 characters long):

value.parseHtml().select(“table.destinations”)[0].select(“td”)[0].toString().substring(5,10)

When you’re happy, click OK and you should have a new column for that data. You can repeat this for every piece of data you want to extract into a new column.

Then click Export in the upper right corner and save as a CSV or Excel file.

Source: http://onlinejournalismblog.com/2012/01/13/sftw-scraping-data-with-google-refine/

Data Scraping - Hand Scraped Hardwood Flooring Gives Your Home That Exclusive Look

Today hand scraped hardwood flooring is becoming extremely popular in the more opulent homes as well as in some commercial properties. Although this type of flooring has only recently become fashionable it has been around for many centuries.

Certainly before the invention of modern sanding techniques all floors where hand scraped at the location where they were to be installed to ensure that the floor would be flat and even. However today this method is used instead to provide texture, richness as well as a unique look and feel to the flooring.

Although manufacturers have produced machines which can provide a scraped look to their flooring it looks cheap compared to the real thing. Unfortunately the main problem with using a machine to scrape the flooring is that it provides a uniform look to the pattern of the wood. Because of this it lacks the natural feel that you would see with a floor which has been scraped by hand.

When done by hand, scraping creates a truly unique look to the floor. However the actual look and feel of each floor will vary as it depends on the skills of the person actually carrying out the work. If there is no control in place whilst the work is being carried out this can result in disastrous look to the finished product.

Many manufacturers who actually provide hand scraped hardwood flooring will either just dent, scoop or rough the floor up. But others will use sanding techniques in order to create a worn and uneven look to the flooring. The more professional teams will scrape the entire surface of the wood in order to create the unique hand made look for their customers.

Many companies will allow their customers to choose what type of scraping takes place on their wood. They can choose between light, medium and heavy. The companies who are really good at hand scraping will be able give the hardwood floor a reclaimed look by including wormholes, splits and other naturally-occurring features within the wood.

If you do decide to choose hand scraped hardwood flooring you will need to factor the costs that are associated with it into your budget. Unfortunately this type of flooring does not come cheap and you can find yourself paying upwards of $15 per sq ft. But once it is installed it will give a room a unique and warm rich feel to it and is certainly going to wow your friends and family when they see it for the first time.

Source: http://ezinearticles.com/?Hand-Scraped-Hardwood-Flooring-Gives-Your-Home-That-Exclusive-Look&id=572577

Making data on the web useful: scraping

Introduction

Many times data is not easily accessible – although it does exist. As much as we wish everything was available in CSV or the format of our choice – most data is published in different forms on the web. What if you want to use the data to combine it with other datasets and explore it independently?

Scraping to the rescue!

Scraping describes the method to extract data hidden in documents – such as Web Pages and PDFs and make it useable for further processing. It is among the most useful skills if you set out to investigate data – and most of the time it’s not especially challenging. For the most simple ways of scraping you don’t even need to know how to write code.

This example relies heavily on Google Chrome for the first part. Some things work well with other browsers, however we will be using one specific browser extension only available on Chrome. If you can’t install Chrome, don’t worry the principles remain similar.

Code-free Scraping in 5 minutes using Google Spreadsheets & Google Chrome

Knowing the structure of a website is the first step towards extracting and using the data. Let’s get our data into a spreadsheet – so we can use it further. An easy way to do this is provided by a special formula in Google Spreadsheets.

Save yourselves hours of time in copy-paste agony with the ImportHTML command in Google Spreadsheets. It really is magic!

Recipes

In order to complete the next challenge, take a look in the Handbook at one of the following recipes:

    Extracting data from HTML tables.

    Scraping using the Scraper Extension for Chrome

Both methods are useful for:

    Extracting individual lists or tables from single webpages

The latter can do slightly more complex tasks, such as extracting nested information. Take a look at the recipe for more details.

Neither will work for:

    Extracting data spread across multiple webpages

Challenge

Task: Find a website with a table and scrape the information from it. Share your result on datahub.io (make sure to tag your dataset with schoolofdata.org)

Tip

Once you’ve got your table into the spreadsheet, you may want to move it around, or put it in another sheet. Right click the top left cell and select “paste special” – “paste values only”.

Scraping more than one webpage: Scraperwiki

Note: Before proceeding into full scraping mode, it’s helpful to understand the flesh and bones of what makes up a webpage. Read the Introduction to HTML recipe in the handbook.

Until now we’ve only scraped data from a single webpage. What if there are more? Or you want to scrape complex databases? You’ll need to learn how to program – at least a bit.

It’s beyond the scope of this course to teach how to scrape, our aim here is to help you understand whether it is worth investing your time to learn, and to point you at some useful resources to help you on your way!

Structure of a scraper

Scrapers are comprised of three core parts:

1.    A queue of pages to scrape
2.    An area for structured data to be stored, such as a database
3.    A downloader and parser that adds URLs to the queue and/or structured information to the database.

Fortunately for you there is a good website for programming scrapers: ScraperWiki.com

ScraperWiki has two main functions: You can write scrapers – which are optionally run regularly and the data is available to everyone visiting – or you can request them to write scrapers for you. The latter costs some money – however it helps to contact the Scraperwiki community (Google Group) someone might get excited about your project and help you!.

If you are interested in writing scrapers with Scraperwiki, check out this sample scraper – scraping some data about Parliament. Click View source to see the details. Also check out the Scraperwiki documentation: https://scraperwiki.com/docs/python/

When should I make the investment to learn how to scrape?

A few reasons (non-exhaustive list!):

1.    If you regularly have to extract data where there are numerous tables in one page.

2.    If your information is spread across numerous pages.

3.    If you want to run the scraper regularly (e.g. if information is released every week or month).

4.    If you want things like email alerts if information on a particular webpage changes.

…And you don’t want to pay someone else to do it for you!

Summary:

In this course we’ve covered Web scraping and how to extract data from websites. The main function of scraping is to convert data that is semi-structured into structured data and make it easily useable for further processing. While this is a relatively simple task with a bit of programming – for single webpages it is also feasible without any programming at all. We’ve introduced =importHTML and the Scraper extension for your scraping needs.

Further Reading

1.    Scraping for Journalism: A Guide for Collecting Data: ProPublica Guides

2.    Scraping for Journalists (ebook): Paul Bradshaw

3.    Scrape the Web: Strategies for programming websites that don’t expect it : Talk from PyCon

4.    An Introduction to Compassionate Screen Scraping: Will Larson

Any questions? Got stuck? Ask School of Data!

ScraperWiki has two main functions: You can write scrapers – which are optionally run regularly and the data is available to everyone visiting – or you can request them to write scrapers for you. The latter costs some money – however it helps to contact the Scraperwiki community (Google Group) someone might get excited about your project and help you!.

If you are interested in writing scrapers with Scraperwiki, check out this sample scraper – scraping some data about Parliament. Click View source to see the details. Also check out the Scraperwiki documentation: https://scraperwiki.com/docs/python/

When should I make the investment to learn how to scrape?

A few reasons (non-exhaustive list!):

1.    If you regularly have to extract data where there are numerous tables in one page.

2.    If your information is spread across numerous pages.

3.    If you want to run the scraper regularly (e.g. if information is released every week or month).

4.    If you want things like email alerts if information on a particular webpage changes.

…And you don’t want to pay someone else to do it for you!

Summary:

In this course we’ve covered Web scraping and how to extract data from websites. The main function of scraping is to convert data that is semi-structured into structured data and make it easily useable for further processing. While this is a relatively simple task with a bit of programming – for single webpages it is also feasible without any programming at all. We’ve introduced =importHTML and the Scraper extension for your scraping needs.

Source: http://schoolofdata.org/handbook/courses/scraping/

Scraping Services - Assuring Scraping Success with Proxy Data Scraping

Have you ever heard of "Data Scraping?" Data Scraping is the process of collecting useful data that has been placed in the public domain of the internet (private areas too if conditions are met) and storing it in databases or spreadsheets for later use in various applications. Data Scraping technology is not new and many a successful businessman has made his fortune by taking advantage of data scraping technology.

Sometimes website owners may not derive much pleasure from automated harvesting of their data. Webmasters have learned to disallow web scrapers access to their websites by using tools or methods that block certain ip addresses from retrieving website content. Data scrapers are left with the choice to either target a different website, or to move the harvesting script from computer to computer using a different IP address each time and extract as much data as possible until all of the scraper's computers are eventually blocked.

Thankfully there is a modern solution to this problem. Proxy Data Scraping technology solves the problem by using proxy IP addresses. Every time your data scraping program executes an extraction from a website, the website thinks it is coming from a different IP address. To the website owner, proxy data scraping simply looks like a short period of increased traffic from all around the world. They have very limited and tedious ways of blocking such a script but more importantly -- most of the time, they simply won't know they are being scraped.

You may now be asking yourself, "Where can I get Proxy Data Scraping Technology for my project?" The "do-it-yourself" solution is, rather unfortunately, not simple at all. Setting up a proxy data scraping network takes a lot of time and requires that you either own a bunch of IP addresses and suitable servers to be used as proxies, not to mention the IT guru you need to get everything configured properly. You could consider renting proxy servers from select hosting providers, but that option tends to be quite pricey but arguably better than the alternative: dangerous and unreliable (but free) public proxy servers.

There are literally thousands of free proxy servers located around the globe that are simple enough to use. The trick however is finding them. Many sites list hundreds of servers, but locating one that is working, open, and supports the type of protocols you need can be a lesson in persistence, trial, and error. However if you do succeed in discovering a pool of working public proxies, there are still inherent dangers of using them. First off, you don't know who the server belongs to or what activities are going on elsewhere on the server. Sending sensitive requests or data through a public proxy is a bad idea. It is fairly easy for a proxy server to capture any information you send through it or that it sends back to you. If you choose the public proxy method, make sure you never send any transaction through that might compromise you or anyone else in case disreputable people are made aware of the data.

A less risky scenario for proxy data scraping is to rent a rotating proxy connection that cycles through a large number of private IP addresses. There are several of these companies available that claim to delete all web traffic logs which allows you to anonymously harvest the web with minimal threat of reprisal. Companies such as offer large scale anonymous proxy solutions, but often carry a fairly hefty setup fee to get you going.

The other advantage is that companies who own such networks can often help you design and implementation of a custom proxy data scraping program instead of trying to work with a generic scraping bot. After performing a simple Google search, I quickly found one company (www.ScrapeGoat.com) that provides anonymous proxy server access for data scraping purposes. Or, according to their website, if you want to make your life even easier, ScrapeGoat can extract the data for you and deliver it in a variety of different formats often before you could even finish configuring your off the shelf data scraping program.

Whichever path you choose for your proxy data scraping needs, don't let a few simple tricks thwart you from accessing all the wonderful information stored on the world wide web!

Source: http://ezinearticles.com/?Assuring-Scraping-Success-with-Proxy-Data-Scraping&id=248993

Getting Data from the Web Scraping

You’ve tried everything else, and you haven’t managed to get your hands on the data you want. You’ve found the data on the web, but, alas — no download options are available and copy-paste has failed you. Fear not, there may still be a way to get the data out. For example you can:

•    Get data from web-based APIs, such as interfaces provided by online databases and many modern web applications (including Twitter, Facebook and many others). This is a fantastic way to access government or commercial data, as well as data from social media sites.

•    Extract data from PDFs. This is very difficult, as PDF is a language for printers and does not retain much information on the structure of the data that is displayed within a document. Extracting information from PDFs is beyond the scope of this book, but there are some tools and tutorials that may help you do it.

•    Screen scrape web sites. During screen scraping, you’re extracting structured content from a normal web page with the help of a scraping utility or by writing a small piece of code. While this method is very powerful and can be used in many places, it requires a bit of understanding about how the web works.

With all those great technical options, don’t forget the simple options: often it is worth to spend some time searching for a file with machine-readable data or to call the institution which is holding the data you want.

In this chapter we walk through a very basic example of scraping data from an HTML web page.

What is machine-readable data?

The goal for most of these methods is to get access to machine-readable data. Machine readable data is created for processing by a computer, instead of the presentation to a human user. The structure of such data relates to contained information, and not the way it is displayed eventually. Examples of easily machine-readable formats include CSV, XML, JSON and Excel files, while formats like Word documents, HTML pages and PDF files are more concerned with the visual layout of the information. PDF for example is a language which talks directly to your printer, it’s concerned with position of lines and dots on a page, rather than distinguishable characters.

Scraping web sites: what for?

Everyone has done this: you go to a web site, see an interesting table and try to copy it over to Excel so you can add some numbers up or store it for later. Yet this often does not really work, or the information you want is spread across a large number of web sites. Copying by hand can quickly become very tedious, so it makes sense to use a bit of code to do it.

The advantage of scraping is that you can do it with virtually any web site — from weather forecasts to government spending, even if that site does not have an API for raw data access.

What you can and cannot scrape

There are, of course, limits to what can be scraped. Some factors that make it harder to scrape a site include:

•    Badly formatted HTML code with little or no structural information e.g. older government websites.

•    Authentication systems that are supposed to prevent automatic access e.g. CAPTCHA codes and paywalls.

•    Session-based systems that use browser cookies to keep track of what the user has been doing.

•    A lack of complete item listings and possibilities for wildcard search.

•    Blocking of bulk access by the server administrators.

Another set of limitations are legal barriers: some countries recognize database rights, which may limit your right to re-use information that has been published online. Sometimes, you can choose to ignore the license and do it anyway — depending on your jurisdiction, you may have special rights as a journalist. Scraping freely available Government data should be fine, but you may wish to double check before you publish. Commercial organizations — and certain NGOs — react with less tolerance and may try to claim that you’re “sabotaging” their systems. Other information may infringe the privacy of individuals and thereby violate data privacy laws or professional ethics.

Tools that help you scrape

There are many programs that can be used to extract bulk information from a web site, including browser extensions and some web services. Depending on your browser, tools like Readability (which helps extract text from a page) or DownThemAll (which allows you to download many files at once) will help you automate some tedious tasks, while Chrome’s Scraper extension was explicitly built to extract tables from web sites. Developer extensions like FireBug (for Firefox, the same thing is already included in Chrome, Safari and IE) let you track exactly how a web site is structured and what communications happen between your browser and the server.

ScraperWiki is a web site that allows you to code scrapers in a number of different programming languages, including Python, Ruby and PHP. If you want to get started with scraping without the hassle of setting up a programming environment on your computer, this is the way to go. Other web services, such as Google Spreadsheets and Yahoo! Pipes also allow you to perform some extraction from other web sites.

How does a web scraper work?

Web scrapers are usually small pieces of code written in a programming language such as Python, Ruby or PHP. Choosing the right language is largely a question of which community you have access to: if there is someone in your newsroom or city already working with one of these languages, then it makes sense to adopt the same language.

While some of the click-and-point scraping tools mentioned before may be helpful to get started, the real complexity involved in scraping a web site is in addressing the right pages and the right elements within these pages to extract the desired information. These tasks aren’t about programming, but understanding the structure of the web site and database.

When displaying a web site, your browser will almost always make use of two technologies: HTTP is a way for it to communicate with the server and to request specific resource, such as documents, images or videos. HTML is the language in which web sites are composed.

The anatomy of a web page

Any HTML page is structured as a hierarchy of boxes (which are defined by HTML “tags”). A large box will contain many smaller ones — for example a table that has many smaller divisions: rows and cells. There are many types of tags that perform different functions — some produce boxes, others tables, images or links. Tags can also have additional properties (e.g. they can be unique identifiers) and can belong to groups called ‘classes’, which makes it possible to target and capture individual elements within a document. Selecting the appropriate elements this way and extracting their content is the key to writing a scraper.

Viewing the elements in a web page: everything can be broken up into boxes within boxes.

To scrape web pages, you’ll need to learn a bit about the different types of elements that can be in an HTML document. For example, the <table> element wraps a whole table, which has <tr> (table row) elements for its rows, which in turn contain <td> (table data) for each cell. The most common element type you will encounter is <div>, which can basically mean any block of content. The easiest way to get a feel for these elements is by using the developer toolbar in your browser: they will allow you to hover over any part of a web page and see what the underlying code is.

Tags work like book ends, marking the start and the end of a unit. For example <em> signifies the start of an italicized or emphasized piece of text and </em> signifies the end of that section. Easy.

Figure 57. The International Atomic Energy Agency’s (IAEA) portal (news.iaea.org)

An example: scraping nuclear incidents with Python

NEWS is the International Atomic Energy Agency’s (IAEA) portal on world-wide radiation incidents (and a strong contender for membership in the Weird Title Club!). The web page lists incidents in a simple, blog-like site that can be easily scraped.

To start, create a new Python scraper on ScraperWiki and you will be presented with a text area that is mostly empty, except for some scaffolding code. In another browser window, open the IAEA site and open the developer toolbar in your browser. In the “Elements” view, try to find the HTML element for one of the news item titles. Your browser’s developer toolbar helps you connect elements on the web page with the underlying HTML code.

Investigating this page will reveal that the titles are <h4> elements within a <table>. Each event is a <tr> row, which also contains a description and a date. If we want to extract the titles of all events, we should find a way to select each row in the table sequentially, while fetching all the text within the title elements.

In order to turn this process into code, we need to make ourselves aware of all the steps involved. To get a feeling for the kind of steps required, let’s play a simple game: In your ScraperWiki window, try to write up individual instructions for yourself, for each thing you are going to do while writing this scraper, like steps in a recipe (prefix each line with a hash sign to tell Python that this not real computer code). For example:

# Look for all rows in the table

# Unicorn must not overflow on left side.

Try to be as precise as you can and don’t assume that the program knows anything about the page you’re attempting to scrape.

Once you’ve written down some pseudo-code, let’s compare this to the essential code for our first scraper:

import scraperwiki

In this first section, we’re importing existing functionality from libraries — snippets of pre-written code. scraperwiki will give us the ability to download web sites, while lxml is a tool for the structured analysis of HTML documents. Good news: if you are writing a Python scraper with ScraperWiki, these two lines will always be the same.

doc_text = scraperwiki.scrape(url)

doc = html.fromstring(doc_text)

Next, the code makes a name (variable): url, and assigns the URL of the IAEA page as its value. This tells the scraper that this thing exists and we want to pay attention to it. Note that the URL itself is in quotes as it is not part of the program code but a string, a sequence of characters.

We then use the url variable as input to a function, scraperwiki.scrape. A function will provide some defined job — in this case it’ll download a web page. When it’s finished, it’ll assign its output to another variable, doc_text. doc_text will now hold the actual text of the website — not the visual form you see in your browser, but the source code, including all the tags. Since this form is not very easy to parse, we’ll use another function, html.fromstring, to generate a special representation where we can easily address elements, the so-called document object model (DOM).

In this final step, we use the DOM to find each row in our table and extract the event’s title from its header. Two new concepts are used: the for loop and element selection (.cssselect). The for loop essentially does what its name implies; it will traverse a list of items, assigning each a temporary alias (row in this case) and then run any indented instructions for each item.

The other new concept, element selection, is making use of a special language to find elements in the document. CSS selectors are normally used to add layout information to HTML elements and can be used to precisely pick an element out of a page. In this case (Line. 6) we’re selecting #tblEvents tr which will match each <tr> within the table element with the ID tblEvents (the hash simply signifies ID). Note that this will return a list of <tr> elements.

As can be seen on the next line (Line. 7), where we’re applying another selector to find any <a> (which is a hyperlink) within a <h4> (a title). Here we only want to look at a single element (there’s just one title per row), so we have to pop it off the top of the list returned by our selector with the .pop() function.

Note that some elements in the DOM contain actual text, i.e. text that is not part of any markup language, which we can access using the [element].text syntax seen on line 8. Finally, in line 9, we’re printing that text to the ScraperWiki console. If you hit run in your scraper, the smaller window should now start listing the event’s names from the IAEA web site.

You can now see a basic scraper operating: it downloads the web page, transforms it into the DOM form and then allows you to pick and extract certain content. Given this skeleton, you can try and solve some of the remaining problems using the ScraperWiki and Python documentation:

•    Can you find the address for the link in each event’s title?

•    Can you select the small box that contains the date and place by using its CSS class name and extract the element’s text?

•    ScraperWiki offers a small database to each scraper so you can store the results; copy the relevant example from their docs and adapt it so it will save the event titles, links and dates.

•    The event list has many pages; can you scrape multiple pages to get historic events as well?

As you’re trying to solve these challenges, have a look around ScraperWiki: there are many useful examples in the existing scrapers — and quite often, the data is pretty exciting, too. This way, you don’t need to start off your scraper from scratch: just choose one that is similar, fork it and adapt to your problem.

Source: http://datajournalismhandbook.org/1.0/en/getting_data_3.html

Data Scraping Services - Scraping Yelp Business Data With Python Scraping Script

Yelp is a great source of business contact information with details like address, postal code, contact information; website addresses etc. that other site like Google Maps just does not. Yelp also provides reviews about the particular business. The yelp business database can be useful for telemarketing, email marketing and lead generation.

Are you looking for yelp business details database? Are you looking for scraping data from yelp website/business directory? Are you looking for yelp screen scraping software? Are you looking for scraping the business contact information from the online Yelp? Then you are at the right place.

Here I am going to discuss how to scrape yelp data for lead generation and email marketing. I have made a simple and straight forward yelp data scraping script in python that can scrape data from yelp website. You can use this yelp scraper script absolutely free.

I have used urllib, BeautifulSoup packages. Urllib package to make http request and parsed the HTML using BeautifulSoup, used Threads to make the scraping faster.

Yelp Scraping Python Script

import urllib from bs4 import BeautifulSoup import re from threading import Thread #List of yelp urls to scrape url=['http://www.yelp.com/biz/liman-fisch-restaurant-hamburg','http://www.yelp.com/biz/casa-franco-caramba-hamburg','http://www.yelp.com/biz/o-ren-ishii-hamburg','http://www.yelp.com/biz/gastwerk-hotel-hamburg-hamburg-2','http://www.yelp.com/biz/superbude-hamburg-2','http://www.yelp.com/biz/hotel-hafen-hamburg-hamburg','http://www.yelp.com/biz/hamburg-marriott-hotel-hamburg','http://www.yelp.com/biz/yoho-hamburg'] i=0 #function that will do actual scraping job def scrape(ur): html = urllib.urlopen(ur).read() soup = BeautifulSoup(html) title = soup.find('h1',itemprop="name") saddress = soup.find('span',itemprop="streetAddress") postalcode = soup.find('span',itemprop="postalCode") print title.text print saddress.text print postalcode.text print "-------------------" threadlist = [] #making threads while i<len(url): t = Thread(target=scrape,args=(url[i],)) t.start() threadlist.append(t) i=i+1 for b in

threadlist: b.join()

import urllib

from bs4 import BeautifulSoup

import re

from threading import Thread

 #List of yelp urls to scrape

url=['http://www.yelp.com/biz/liman-fisch-restaurant-hamburg','http://www.yelp.com/biz/casa-franco-caramba-hamburg','http://www.yelp.com/biz/o-ren-ishii-hamburg','http://www.yelp.com/biz/gastwerk-hotel-hamburg-hamburg-2','http://www.yelp.com/biz/superbude-hamburg-2','http://www.yelp.com/biz/hotel-hafen-hamburg-hamburg','http://www.yelp.com/biz/hamburg-marriott-hotel-hamburg','http://www.yelp.com/biz/yoho-hamburg']

 i=0

#function that will do actual scraping job

def scrape(ur):

           html = urllib.urlopen(ur).read()

          soup = BeautifulSoup(html)

       title = soup.find('h1',itemprop="name")

          saddress = soup.find('span',itemprop="streetAddress")

          postalcode = soup.find('span',itemprop="postalCode")

          print title.text

          print saddress.text

          print postalcode.text

          print "-------------------"

 threadlist = []

#making threads

while i<len(url):

          t = Thread(target=scrape,args=(url[i],))

          t.start()

          threadlist.append(t)

          i=i+1

for b in threadlist:

          b.join()

Recently I had worked for one German company and did yelp scraping project for them and delivered data as per their requirement. If you looking for scraping data from business directories like yelp then send me your requirement and I will get back to you with sample.

Source: http://webdata-scraping.com/scraping-yelp-business-data-python-scraping-script/

Web Scraping Services : What are the ethics of web scraping?

Someone recently asked: "Is web scraping an ethical concept?" I believe that web scraping is absolutely an ethical concept. Web scraping (or screen scraping) is a mechanism to have a computer read a website. There is absolutely no technical difference between an automated computer viewing a website and a human-driven computer viewing a website. Furthermore, if done correctly, scraping can provide many benefits to all involved.

There are a bunch of great uses for web scraping. First, services like Instapaper, which allow saving content for reading on the go, use screen scraping to save a copy of the website to your phone. Second, services like Mint.com, an app which tells you where and how you are spending your money, uses screen scraping to access your bank's website (all with your permission). This is useful because banks do not provide many ways for programmers to access your financial data, even if you want them to. By getting access to your data, programmers can provide really interesting visualizations and insight into your spending habits, which can help you save money.

That said, web scraping can veer into unethical territory. This can take the form of reading websites much quicker than a human could, which can cause difficulty for the servers to handle it. This can cause degraded performance in the website. Malicious hackers use this tactic in what’s known as a "Denial of Service" attack.

Another aspect of unethical web scraping comes in what you do with that data. Some people will scrape the contents of a website and post it as their own, in effect stealing this content. This is a big no-no for the same reasons that taking someone else's book and putting your name on it is a bad idea. Intellectual property, copyright and trademark laws still apply on the internet and your legal recourse is much the same. People engaging in web scraping should make every effort to comply with the stated terms of service for a website. Even when in compliance with those terms, you should take special care in ensuring your activity doesn't affect other users of a website.

One of the downsides to screen scraping is it can be a brittle process. Minor changes to the backing website can often leave a scraper completely broken. Herein lies the mechanism for prevention: making changes to the structure of the code of your website can wreak havoc on a screen scraper's ability to extract information. Periodically making changes that are invisible to the user but affect the content of the code being returned is the most effective mechanism to thwart screen scrapers. That said, this is only a set-back. Authors of screen scrapers can always update them and, as there is no technical difference between a computer-backed browser and a human-backed browser, there's no way to 100% prevent access.

Going forward, I expect screen scraping to increase. One of the main reasons for screen scraping is that the underlying website doesn't have a way for programmers to get access to the data they want. As the number of programmers (and the need for programmers) increases over time, so too will the need for data sources. It is unreasonable to expect every company to dedicate the resources to build a programmer-friendly access point. Screen scraping puts the onus of data extraction on the programmer, not the company with the data, which can work out well for all involved.

Source: https://quickleft.com/blog/is-web-scraping-ethical/

Improving performance for web scraping code

2 down vote favorite

I have a website in which the code scrapes other websites for getting the accurate data. While the code works good but there a decent lag in performance because the code firsts downloads the html stream from various sites(some times 9 websites), extracts the relative part and then renders the html page.

What should I do to get an optimal performance. Should I change from shared hosting (godaddy) to my own server or it has nothing to do with my hosting and I need to make changes to my code?

1 Answer

API/CSV

Ask those websites if they provide an API, or, if you don't need an up-to-date information or the information you need doesn't change frequently, if they can sell/give you for free the data itself (for example in an CSV file). Some small websites may have fancier ways to access data, like a CSV file for the older information, and an RSS feed for the changed one.

Those websites would probably be happy to help you, since providing you with an API would reduce their own CPU and bandwidth usage by you.

Profile

Screen scrapping is really ugly when it comes to performance and scaling. You may be limited by:

    your machine performance, since parsing, sometimes an invalid HTML file, takes time,

    your network speed,

    their network speed usage, i.e. how fast can you access the pages of their website depending on the restrictions they set, like the DOS protection and the number of requests per second for screen scrappers and search engine crawlers,

    their machine performance: if they spend 500 ms. to generate every page, you can't do anything to reduce this delay.

If, despite your requests to them, those websites cannot provide any convenient way to access their data, but they give you a written consent to screen scrape their website, then profile your code to determine the bottleneck. It may be the internet speed. It may be your database queries. It may be anything.

For example, you may discover that you spend too much time finding with regular expressions the relevant information in the received HTML. In that case, you would want to stop doing it wrong and use a parser instead of regular expressions, then see how this improve the performance.

You may also find that the bottleneck is the time the remote server spends generating every page. In this case, there is nothing to do: you may have the fastest server, the fastest connection and the most optimized code, the performance will be the same.

Do things in parallel:

Remember to use parallel computing wisely and to always profile what you're doing, instead of doing premature optimization, in hope that you're smarter than the profiler.

Especially when it comes to using network, you may be very surprised. For example, you may believe that making more requests in parallel will be faster, but as Steve Gibson explains in episode 345 of Security Now, this is not always the case.

Legal aspects

Also note that screen scrapping is explicitly forbidden by the conditions of use (like on IMDB) on many websites. And if nothing is said on this subject in conditions of use, it doesn't mean that you can screen scrape those websites.

The fact that the information is available publicly on the internet doesn't give you the right to copy and reuse it this way neither.

Why? you may ask. For two reasons:

    Most websites are relying on advertisement and marketing. When people use one of those websites directly, they waste some CPU/network bandwidth of the website, but in response, they may click on an ad or buy something sold on the website. When you screen scrape, your bot waste their CPU/network bandwidth, but will never click on an ad or buy something.

    Displaying the information you screen scrapped on your website can have even worse effects. Example: in France, there are two major websites selling hardware. The first one is easy and fast to use, has a nice visual design, better SEO, and in general is very well done. The second one is a crap, but the prices are lower. If you screen scrape them and give the raw results (prices with links) to your users, they will obviously click on the lower price every time, which means that the website with pretty design will have less chances to sell the products.

    People made an effort in collecting, processing and displaying some data. Sometimes they paid to get it. Why would they enjoy seeing you pulling this data conveniently and for free?

Source: http://programmers.stackexchange.com/questions/141403/improving-performance-for-web-scraping-code/141406#141406

5 tips for scraping big websites

Scraping bigger websites can be a challenge if done the wrong way.

Bigger websites would have more data, more security and more pages. We’ve learned a lot from our years of crawling such large complex websites, and these tips could help solve some of your challenges

1. Cache pages visited for scraping

When scraping big websites, its always a good idea to cache the data you have already downloaded. So you don’t have to put load on the website again, in case you have start over again or that page is required again during scraping. Its effortless to cache to a key value store like redis.But Databases and filesystem caches are also good.

2. Don’t flood the website with large number of parallel requests , take it slow

Big websites posses algorithms to detect webscraping, large number of parallel requests from the same ip address will identify you as a Denial Of Service Attack on their website, and blacklist your IPs immediately. A better idea is to time your requests properly one after the other, giving it some human behavior. Oh!.. but scraping like that will take you ages. So balance requests using the average response time of the websites, and play around with the number of parallel requests to the website to get the right number.

3. Store the URLs that you have already fetched

You may want to keep a list of URLs you have already fetched, in a database or a key value store. What would you do if you scraper crashes after scraping 70% of the website. If you need to complete the remaining 30%, with out this list of URLs, you’ll waste a lot of time and bandwidth. Make sure you store this list of URLs

somewhere permanent, till you have all the required data. This could also be combined with the cache. This way, you’ll be able to resume scraping.

4. Split scraping into different phases

Its easier and safer if you split the scraping into multiple smaller phases. For example, you could split scraping a huge site into two. One for gathering links to the pages from which you need to extract data and another for downloading these pages to scrape content.

5. Take only whats required

Don’t grab or follow every link unless its required. You can define a proper navigation scheme to make the scraper visit only the pages required. Its always tempting to grab everything, but its just a waste of bandwidth, time and storage.

Source: http://learn.scrapehero.com/5-tips-for-scraping-big-websites/

How Web Data Extraction Services Impact Startups

Starting a business has its fair share of ebbs and flows – it can be extremely challenging to get a new business off the blocks, and extremely rewarding when everything goes according to plan and yields desired results. For startups, it is important to get the nuances of running a business right from day one. To succeed in an immensely competitive space, startups need to perform above and beyond expectation right from the start, and one of the factors that can be of great help during the growing years of a startup is web data extraction.

Web data extraction through crawling and scraping, a highly efficient information gathering process, can be used in many creative ways to bring about major change in the performance graph of a startup. With effective web data extraction services acquired by outsourcing to a reputed company, the business intelligence gathered and the numerous possibilities associated with it, web crawling and extraction services can indeed become the difference maker for a startup, propelling it to the heights of success.

What drives the success of web data extraction?

When it comes to figuring out the perfect, balanced web data collection methodology for startups, there are a lot of crucial factors that come into play. Some of these are associated with the technical aspects of data collection, the approach used, the time invested, and the tools involved. Others have more to do with the processing and analysis of collected information and its judicious use in formulating strategies to take things forward.

Web Crawling Services & Web Scraping Services

With the advent of highly professional web data extraction services providers, massive amounts of structured, relevant data can be gathered and stored in real time, and in time, productively used to further the business interests of a startup. As a new business owner, it is important to have a high-level knowledge of the modern and highly functional web scraping tools available for use. This will help to utilize the prowess of competent data extraction services. This in turn can assist both in the immediate and long-term revenue generation context.

Web Data Extraction for Startups

From the very beginning, the dynamics of startups is different from that of older, well-established businesses. The time taken by the new business entity in proving its capabilities and market position needs to be used completely and effectively. Every day of growth and learning needs to add up to make a substantial difference. In this period, every plan and strategy, every execution effort, and every move needs to be properly thought out.

In such a trying situation where there is little margin for error, it pays to have accurate, reliable, relevant and actionable business intelligence. This can put you in firm control of things by allowing you to make informed business decisions and formulate targeted, relevant and growth oriented business strategies. With powerful web crawling, the volume of data gathered is varied, accurate and relevant. This data can then be studied minutely, analyzed in detail and arranged into meaningful clusters. With this weapon in your arsenal, you can take your startup a long way with smart decisions and clever implementations.

Web data extraction is a task best handled by professionals who have had rich experience in the field. Often, in-house web scraping teams are difficult to assemble and not economically viable to maintain, especially for startups. For a better solution, you can outsource your web scraping needs to a reliable web data extraction service for data collection. This way, you can get all the relevant intelligence you need without overstraining your workforce or having to employ additional personnel to handle web scraping. The company you outsource your work to can easily scrape data from multiple sources as per your requirements, and furnish you with actionable business intelligence that can help you take a lead in a competitive market.

Different Ways for Startups to use Web Data Extraction

Web scraping can be employed for many different purposes to yield different kinds of relevant data that generate actionable insights. For a startup, the important decision is how to use this powerful technique to provide valuable information that can make a difference for the future prospects of the company. Here are some interesting possibilities when it comes to impactful web data extraction for startups –

Fishing for Social Rankings and Backlinks

One of the most important business processes for a startup is competition analysis. This is one area where web data extraction can come across as an invaluable enabler. In the past, many startups have effectively used web scraping to fish for backlinks and social rankings related to competing companies.

Backlinks are important to reach a greater mass of better-targeted audiences, which can go on to increase customer base with minimal efforts. Social ranking is also an immensely important factor, as social actions on the internet are building blocks of opinion and reputation generation in this day and age. Keeping this in mind, you can use web data extraction to scrape for social rankings and backlinks related to content generated by your competing companies. After careful analysis, it is possible to arrive at concrete conclusions regarding what your competitors are doing well, and what sells the best.

This information is gold for marketers and sales personnel, and can be used to discern exactly what needs to be done to increase social buzz, generate favorable opinion, and win over customers from your competitors. You can also use this technique to develop high authority backlinks that help with SEO, targeted reach and organic traffic for your business website. For competition analysis, web scraping is a formidable tool.

Sourcing Contact Information

Another important aspect of business that startups can never ignore is good networking. Whether it is with customers, prospective customers, industry peers, partners, or competitors, excellent networking and open, transparent communication is essential for the success of your startup. For effective communication and networking, you need a large, solid list of contact information pertaining to your exact requirements.

Scraping data from multiple web sources gives you the perfect method of achieving this. With automated, fast web scraping, you can in a short time collect a wealth of important contact information that can be leveraged in many different ways. Whether it is the formation of lasting business relationships or making potential customers aware of what you have on offer, this information has the power to propel your startup to new levels of recognition.

For Ecommerce

If you sell your products and services online and want to stay on top of the competition when it comes to variety, pricing analysis, and special deals and offers, web scraping is the way to go. For many e-commerce startups, the problem of high CTR and low conversion is a stumbling block to higher bottom lines. To remedy problems like these and to ensure better sales, it is always a good idea to have a clear insight about your competition.

Future of Retail Industry

With web data extraction, you can be always aware of what competing companies are doing in terms of pricing strategies, product diversity and special customer offers. By considering that information while evaluating and cementing your own strategies, you can always ensure that you provide better value and range of products and services than your competitors, and therefore stay ahead of the competition.

For Marketing, Brand Promotion and Advertisement

For startups, the first wave of promotion and marketing is the one that holds the key to your long-term business success. It is during this phase that the first and most important public perception of your company is formed, and the rudiments of public opinion start taking shape. For this reason, it is crucial to be on point with your marketing and promotion during the early, formative years of your business.

To achieve this, you need a clear, in-depth understanding of your target audience. You need to categorize your target audience on the basis of many factors like age, gender, demographics, income groups and tastes and preferences. Such detailed understanding can only be possible when you have a large wealth of social data pertaining to your target audience. There is no better way of achieving this than by web data extraction.

Love your brand

With the help of data extraction services, you can gather large chunks of relevant data regarding your target audience which can help you accurately evaluate the potential of each prospective customers as a possible addition to your business family. To ensure that you have a steady, early wave of customers to take your business off the blocks at a rapid pace, you need to devise marketing campaigns, promotional strategies and advertisements in accordance with the customer knowledge you drive through your web scraping efforts. This is a foolproof strategy to have marketing and promotional plans in place that achieve goals, bring in new business and provide your company with enough initial momentum to carry it through the later years of success.

To conclude, web data extraction can be a veritable tool in the hands of a startup. With the proper use and leveraging of this technique, your startup can gather the required business intelligence to shine in a competitive market and become a favorite with the customer base. Working with the right web data extraction company can be one of the most important business decisions you make as a startup owner.

Source: https://www.promptcloud.com/blog/web-data-extraction-services-for-startups/

Metadata Scraping Service

As mentioned in Robert's last blog post we set up a scraping service which supports users working with citations by extracting automatically references from digital library or publisher websites. We use a very similar service in BibSonomy to support our users while posting a new reference. However, the service is independent from BibSonomy. Our main goal is to make the metadata of other websites easily accessible to every user who needs bibliographic metadata. Therefore we offer the extracted information in BibTeX format. Most tools allow to import BibTeX so it should be very easy for everyone to get the data into his own tool. The service is running under the following URL:

http://scraper.bibsonomy.org/

Currently we support more than 60 different websites (here the full list) and we are working on further extensions. In the near future we will make the source code of our scrapers publicly available under GPL and we hope that other people will find it useful and start to help us by implementing their own scrapers.

How does the service work?

In principle there are two ways to use the service. One uses a so

called bookmarklet and the other is simply based on the URL. If you

have a webpage of a supported site e.g. from ACM digital library the

following page:

Logsonomy - social information retrieval with logdata

then you can copy this URL into the form on the service homepage and the service will return you the extracted BibTeX information. As this is not a very convenient way to access the data we provide a ScrapePublication button. This button is a small piece of JavaScript and can be copied to the toolbar of the browser. By pressing this button while visiting a digital library webpage the URL will be automatically copied and sent to the scraping service and the metadata is extracted.

The service has three options which can be used to customize it and to make it useful for other systems. Obviously one parameter is the URL itself which is used by the bookmarklet, too. The next is the selection parameter which allows to send text to the service and the last parameter allows to change the output format from html to plain BibTeX. This last parameter makes integration with other systems very simple.

If needed we can provide the metadata in other formats as well but currently we support only BibTeX.

Source: http://blog.bibsonomy.org/2008/11/metadata-scraping-service.html

Web Scraping: Startups, Services & Market

I got recently interested in startups using web scraping in a way or another and since I find the topic very interesting I wanted to share with you some thoughts. [Note that I’m not an expert. To correct me / share your knowledge please use the comment section]

Web scraping is everything but a new technique. However with more and more data shared on internet (from user generated content like social networks & review websites to public/government data and the growing number of online services) the amount of data collected and the use cases possible are increasing at an incredible pace.

We’ve entered the age of “Big Data” and web scraping is one of the sources to feed big data engines with fresh new data, let it be for predictive analytics, competition monitoring or simply to steal data.

From what I could see the startups and services which are using “web scraping” at their core can be divided into three categories:

•    the shovel sellers (a.k.a we sell you the technology to do web scraping)

•    the shovel users (a.k.a we use web scraping to extract gold and sell it to our users)

•    the shovel police (a.k.a the security services which are here to protect website owners from these bots)

The shovel sellers

From a technology point of view efficient web scraping is quite complicated. It exists a number of open source projects (like Beautiful Soup) which enable anyone to get up and running a web scraper by himself. However it’s a whole different story when it has to be the core of your business and that you need not only to maintain your scrapers but also to scale them and to extract smartly the data you need.

This is the reason why more and more services are selling “web scraping” as a service. Their job is to take care about the technical aspects so you can get the data you need without any technical knowledge. Here some examples of such services:

    Grepsr
    Krakio
    import.io
    promptcloud
    80legs
    Proxymesh (funny service: it provides a proxy rotator for web scraping. A shovel seller for shovel seller in a way)
    scrapingHub
    mozanda

The shovel users

It’s the layer above. Web scraping is the technical layer. What is interesting is to make sense of the data you collect. The number of business applications for web scraping is only increasing and some startups are really using it in a truly innovative way to provide a lot of value to their customers.

Basically these startups take care of collecting data then extract the value out of it to sell it to their customers. Here some examples:

Sales intelligence. The scrapers screen marketplaces, competitors, data from public markets, online directories (and more) to find leads. Datanyze, for example, track websites which add or drop javascript tags from your competitors so you can contact them as qualified leads.

Marketing. Web scraping can be used to monitor how your competitors are performing. From reviews they get on marketplaces to press coverage and financial published data you can learn a lot. Concerning marketing there is even a growth hacking class on udemy that teaches you how to leverage scraping for marketing purposes.

Price Intelligence. A very common use case is price monitoring. Whether it’s in the travel, e-commerce or real-estate industry monitoring your competitors’ prices and adjusting yours accordingly is often key. These services not only monitor prices but with their predictive algorithms they can give you advice on where the puck will be. Ex: WisePricer, Pricing Assistant.

Economic intelligence, Finance intelligence etc. with more and more economical, financial and political data available online a new breed of services, which collect and make sense of it, are rising. Ex: connotate.

The shovel police

Web scraping lies in a gray area. Depending on the country or the terms of service of each website, automatically collecting data via robots can be illegal. Whatever the laws say it becomes crucial for some services to try to block these crawlers to protect themselves. The IT security industry has understood it and some startups are starting to tackle this problem. Here are 3 services which claim to provide solutions to stop bots from crawling your website:

•    Distil
•    ScrapeSentry
•    Fireblade

From a market point of view

A couple of points on the market to conclude:

•    It’s hard to assess how big the “web scraping economy” is since it is at the intersection of several big industries (billion dollars): IT security, sales, marketing & finance intelligence. This technique is of course a small component of these industries but is likely to grow in the years to come.

•    A whole underground economy also exists since a lot of web scraping is done through “botnets” (networks of infected computers)

•    It’s a safe bet to say that more and more SaaS (like Datanyze pr Pricing Assistant) will find innovative applications for web scraping. And more and more startups will tackle web scraping from the security point of view.

•    Since these startups are often entering big markets through a niche product / approach (web scraping is a not the solution to everything, there are more a feature) they are likely to be acquired by bigger players (in the security, marketing or sales tools industries). The technological barrier are there.

Source: http://clementvouillon.com/article/web-scraping-startups-services-market/

Earn Money From Price Comparison Through Web Scraping

Many individuals discover the pot of gold just within their reach. They have realized that there is money in the web. Cyber technology has blessed mankind with so many benefits that makes money very possible by just some clicks on the mouse and keyboard. Building a price comparison website is an effective way of helping clients find their desired products while you as the owner earn money at the same time.

Building price comparison websites

There is indeed much money in building price comparison websites but it is not an easy task especially for a novice in maintaining a website of one’s own. Since this entails some serious programming and ample familiarity with data feeds, you have to have a good working plan. In addition, what you are venturing into is greater than the usual blogs about just anything that you can think of. Furthermore, you are stepping into the vast field of electronic marketing, therefore you must be ready.

The first point of consideration is to identify which products or services are you going to include in your website. Choose a product or service that you and a majority of clients are mostly interested in. Suppose you want you to choose sports as your theme then you can include items and prices of sports gear, clothing such as uniforms, training videos, books, and other safety stuff. You need to do some research and even a survey to determine whether the goods and services you are promoting on your website are in demand and are what most people want to know. Moreover, it is on this stage that you may need the help of experts and veterans in the field of building to be assured that you are on the right track.

In addition, be willing to change in case your chosen category is not gaining readership or visitors. Then evaluate whether you need to expand or to be more specific in your description of the products and the comparison of the prices. Make your site prominent by search engine optimization (SEO) and make sure to acknowledge also that not too many people visit a site that is not free.

Helping visitors choose the best product/services

Good marketing strategy starts with knowing who your target audience are. There is indeed a need to do a lot of planning and research in order to understand your client’s needs and preferences. Moreover, knowing them thoroughly leads to achieving 100% consumer satisfaction. When you have provided everything they need to know about certain products, they would not need to seek elsewhere which will also gain you more regular visitors. Remember that your audience are members of communities and social networks such that there is a great possibility that they would spread the word around about the good services you are offering.

If there is a need to conduct a survey in addition to research, you should resort to it. In this manner you can discover what goods and services are not yet completely exhausted by the other websites or web creators. Ample knowledge about your potential visitors and consumers will surely make you effectively provide them with adequate statistics for their needs.

Your site will then look like a complete guidebook for them that will give them the best value for their money. Therefore, it must be thoroughly filled with product details, uses, options, and prices.

Making money as affiliate of eCommerce websites

Maintaining a price comparison website gives you less worry about getting paid or having your products bought and sold because income comes in through advertising and affiliate sales. Affiliate marketing is a way of earning money online by serving as a publisher for promotion of products, services or sites of businesses. The affiliate receives rewards from businesses for each visitor or client that comes to the business website or buys its product through the efforts of the advertising and promotion that is made by the affiliate. This is the online version of the concept of agent or referral fee sales channel. Aside from website owners, bloggers as well as members of community forums can also serve as affiliates. The affiliate earns money in three ways: through pay per link; pay per sale and pay per lead.

Trust in the reliability of the product - You should have a personal belief or confidence in the product you are promoting not only because it makes you sound more convincing, but also because you need to maintain your clients and establish credibility in your blog or website. In other words, don’t just pick any product. If you cannot use them personally, they should at least have several positive reviews and no negative ones.

Maintain credibility with readers and fellow bloggers - Befriend your readers and your co-bloggers by answering their queries sincerely and quickly. Your friendly attitude can win you their trust which is a very vital element of affiliate marketing.

Do reviews - In addition to publishing price comparison, you can gain more visitors by writing about the product and do proper SEO (Search Engine Optimization). So the expected happens, the more prominent the product becomes online, the higher will be your income.

Link with friends thru social media - Your friends have friends and their friends have also friends. Just think of how powerful your social media site can be when you post your link on your account on Facebook, Twitter or MySpace and others. Since trust is built on friendship, it is easy to get clients from among your friends and their friends.

Overall, you get all pertinent information about certain products through web data mining or web scraping. All you need to do is to be keen to the needs of your clients and use web content extraction efficiently.

Source: http://www.loginworks.com/earn-money-price-comparison-web-scraping/

Scraping a website from a windows service

Question

Hi there.  I have a windows forms application that scrapes a website to retrieve some data.  I would like to implement the same functionality as a windows service.  The reason for this is to allow the program to run 24/7 without  having a user signed in. 

To that end, my current version of the program uses a web browser control (system.windows.forms.webbrowser) to navigate the pages, click the buttons, allow scripts to do their thing, etc.  I cannot figure out a way to do the same without the web browser control, but the web browser control cannot be instantiated in a windows service (because there is no user interface in a web service).

Does anyone have any brilliant ideas on how to get around this?

Thank you very much!

Answers

Hi Andy,

There is a tool which could let you manipulate anything you want on the website. This agile HTML parser builds a read/write DOM and supports plain XPATH or XSLT. It is a .NET code library that allows you to parse "out of the web" HTML files. The parser is very tolerant with "real world" malformed HTML. The object model is very similar to what proposes System.Xml, but for HTML documents (or streams). More information, please check:

http://htmlagilitypack.codeplex.com/

Have a nice day.

Best regards

All replies

You are not telling if you are using a .NET Express edition or not

You are not telling which Framework

You are not realy saying what data you are getting from the web site.

So

I made an example of service that work on any Studio edition (including the Express)

to install it, I supposed that you have at least the Framework2, so you will use something similar to:

    %SystemRoot%\Microsoft.NET\Framework\v2.0.50727\installutil /i C:\Test\MyWindowService\MyWindowService\bin\Release\MyWindowService.exe

In the example, I supposed that you are downloading some file from the site

You will need a reference to Windows.Form for the timer


Imports System.ServiceProcess

Imports System.Configuration.Install

Public Class WindowsService : Inherits ServiceBase

  Private Minute As Integer = 60000

  Private WithEvents Timer As New Timer With {.Interval = 30 * Minute, .Enabled = True}

  Public Sub New()

    Me.ServiceName = "MyService"

    Me.EventLog.Log = "Application"

    Me.CanHandlePowerEvent = True

    Me.CanHandleSessionChangeEvent = True

    Me.CanPauseAndContinue = True

    Me.CanShutdown = True

    Me.CanStop = True

  End Sub


  Private Sub Timer_Tick(ByVal sender As Object, ByVal e As System.EventArgs) Handles Timer.Tick

    If IO.File.Exists("C:\MyPath.Data") Then IO.File.Delete("C:\MyPath.Data")

    My.Computer.Network.DownloadFile("http://MyURL.com", "C:\MyPath.Data", "MyUserName", "MyPassword")

    'Do Something with the data downloaded

  End Sub

End Class

<Microsoft.VisualBasic.HideModuleName()> _

Module MainModule

  Public TheServiceName As String

  Public Sub main()

    Dim TheServiceApplication As New WindowsService

    TheServiceName = TheServiceApplication.ServiceName

    ServiceBase.Run(TheServiceApplication)

  End Sub

End Module

<System.ComponentModel.RunInstaller(True)> _

Public Class WindowsServiceInstaller : Inherits Installer

  Public Sub New()

    Dim serviceProcessInstaller As ServiceProcessInstaller = New ServiceProcessInstaller()

    Dim serviceInstaller As ServiceInstaller = New ServiceInstaller()

    serviceProcessInstaller.Account = ServiceAccount.LocalSystem

    serviceProcessInstaller.Username = Nothing

    serviceProcessInstaller.Password = Nothing

    serviceInstaller.DisplayName = "My Windows Service"

    serviceInstaller.StartType = ServiceStartMode.Automatic

    serviceInstaller.ServiceName = TheServiceName

    Me.Installers.Add(serviceProcessInstaller)

    Me.Installers.Add(serviceInstaller)

  End Sub

End Class



 Hello Andy,

Thanks for your post.

What do you want to scrape from the page? HttpWebRequest class ans WebClient class may be what you need. More information, please check:

The HttpWebRequest class provides support for the properties and methods defined in WebRequest and for additional properties and methods that enable the user to interact directly with servers using HTTP.

http://msdn.microsoft.com/en-us/library/system.net.httpwebrequest.aspx

The WebClient class provides common methods for sending data to or receiving data from any local, intranet, or Internet resource identified by a URI

http://msdn.microsoft.com/en-us/library/system.net.webclient.aspx

If you have any concenrs, please feel free to follow up.

Best regards



Hi Andy,

What about this problem on your side now? If you have any concerns, please feel free to follow up.

Have a nice day.

Best regards



Hi Andy,

When you come back, if you need further assistance about this issue, please feel free to let us know. We will continue to work with this issue.

Have a nice day.

Best regards



Thank you for the reply. Sorry it has taken me so long to respond.  I did not receive any notification that someone had replied!

I am using Visual Studio 2010 Ultimate Edition and the .NET framework 4.0.  Actually, I am upgrading some old code written in VB 6.0, but I can use the latest and greatest thats available.

The application uses a browser control to go to the page, fill in values, click on UI elements, read the HTML that returns, etc.  The purpose of the application is to collection useful information regularily/automatically.

I know how to create a web service, but using the web control in such a service is problematic because the web browser control was meant to be placed on a windows form.  I am not able to create a new instance of it in a project designated as a windows service.



Andy

Thank you for the reply. Sorry it has taken me so long to respond.  I did not receive any notification that someone had replied!

I thought a web request was for web services (retrieving information from them).  I am trying to retreive useful information from a website designed for interaction by a human, such as selecting items from lists and clicking buttons.   I currently use a web browser control to programmatically do what a person would do and get the pages back which in turn get parsed.

Andy



Hi Andy,

There is a tool which could let you manipulate anything you want on the website. This agile HTML parser builds a read/write DOM and supports plain XPATH or XSLT. It is a .NET code library that allows you to parse "out of the web" HTML files. The parser is very tolerant with "real world" malformed HTML. The object model is very similar to what proposes System.Xml, but for HTML documents (or streams). More information, please check:

http://htmlagilitypack.codeplex.com/

Have a nice day.

Best regards



Thanks for the suggestion.  I will go to that link and see if it will work.  I will update this post with what I find.

I am writing to check the status of the issue on your side. Would you mind letting us know the result of the suggestions? If you have any concerns, please feel free to follow up.

Have a nice day.



Best regards

Hi Liliane

Thank for the follow up reply.  I don't have an answer as of yet.  Implementing this is going to take time and I haven't been given the go-ahead by my boss to spend the time to pursue it.



Hi Andy,

Never minde. You could have a try when you feel free. If you have any further questions about this issue, please feel free to let us know. We will continue to work with you on this issue.

Have a nice day.

Best regards

Source: https://social.msdn.microsoft.com/Forums/vstudio/en-US/f5d565b1-236b-43c2-90c7-f5cc3b2c341b/scraping-a-website-from-a-windows-service

Three Common Methods For Web Data Extraction

Probably the most common technique used traditionally to extract data from web pages this is to cook up some regular expressions that match the pieces you want (e.g., URL's and link titles). Our screen-scraper software actually started out as an application written in Perl for this very reason. In addition to regular expressions, you might also use some code written in something like Java or Active Server Pages to parse out larger chunks of text. Using raw regular expressions to pull out the data can be a little intimidating to the uninitiated, and can get a bit messy when a script contains a lot of them. At the same time, if you're already familiar with regular expressions, and your scraping project is relatively small, they can be a great solution.

Other techniques for getting the data out can get very sophisticated as algorithms that make use of artificial intelligence and such are applied to the page. Some programs will actually analyze the semantic content of an HTML page, then intelligently pull out the pieces that are of interest. Still other approaches deal with developing "ontologies", or hierarchical vocabularies intended to represent the content domain.

There are a number of companies (including our own) that offer commercial applications specifically intended to do screen-scraping. The applications vary quite a bit, but for medium to large-sized projects they're often a good solution. Each one will have its own learning curve, so you should plan on taking time to learn the ins and outs of a new application. Especially if you plan on doing a fair amount of screen-scraping it's probably a good idea to at least shop around for a screen-scraping application, as it will likely save you time and money in the long run.

So what's the best approach to data extraction? It really depends on what your needs are, and what resources you have at your disposal. Here are some of the pros and cons of the various approaches, as well as suggestions on when you might use each one:

Raw regular expressions and code

Advantages:

- If you're already familiar with regular expressions and at least one programming language, this can be a quick solution.

- Regular expressions allow for a fair amount of "fuzziness" in the matching such that minor changes to the content won't break them.

- You likely don't need to learn any new languages or tools (again, assuming you're already familiar with regular expressions and a programming language).

- Regular expressions are supported in almost all modern programming languages. Heck, even VBScript has a regular expression engine. It's also nice because the various regular expression implementations don't vary too significantly in their syntax.

Disadvantages:

- They can be complex for those that don't have a lot of experience with them. Learning regular expressions isn't like going from Perl to Java. It's more like going from Perl to XSLT, where you have to wrap your mind around a completely different way of viewing the problem.

- They're often confusing to analyze. Take a look through some of the regular expressions people have created to match something as simple as an email address and you'll see what I mean.

- If the content you're trying to match changes (e.g., they change the web page by adding a new "font" tag) you'll likely need to update your regular expressions to account for the change.

- The data discovery portion of the process (traversing various web pages to get to the page containing the data you want) will still need to be handled, and can get fairly complex if you need to deal with cookies and such.

When to use this approach: You'll most likely use straight regular expressions in screen-scraping when you have a small job you want to get done quickly. Especially if you already know regular expressions, there's no sense in getting into other tools if all you need to do is pull some news headlines off of a site.

Ontologies and artificial intelligence

Advantages:

- You create it once and it can more or less extract the data from any page within the content domain you're targeting.

- The data model is generally built in. For example, if you're extracting data about cars from web sites the extraction engine already knows what the make, model, and price are, so it can easily map them to existing data structures (e.g., insert the data into the correct locations in your database).

- There is relatively little long-term maintenance required. As web sites change you likely will need to do very little to your extraction engine in order to account for the changes.

Disadvantages:

- It's relatively complex to create and work with such an engine. The level of expertise required to even understand an extraction engine that uses artificial intelligence and ontologies is much higher than what is required to deal with regular expressions.

- These types of engines are expensive to build. There are commercial offerings that will give you the basis for doing this type of data extraction, but you still need to configure them to work with the specific content domain you're targeting.

- You still have to deal with the data discovery portion of the process, which may not fit as well with this approach (meaning you may have to create an entirely separate engine to handle data discovery). Data discovery is the process of crawling web sites such that you arrive at the pages where you want to extract data.

When to use this approach: Typically you'll only get into ontologies and artificial intelligence when you're planning on extracting information from a very large number of sources. It also makes sense to do this when the data you're trying to extract is in a very unstructured format (e.g., newspaper classified ads). In cases where the data is very structured (meaning there are clear labels identifying the various data fields), it may make more sense to go with regular expressions or a screen-scraping application.

Screen-scraping software

Advantages:

- Abstracts most of the complicated stuff away. You can do some pretty sophisticated things in most screen-scraping applications without knowing anything about regular expressions, HTTP, or cookies.

- Dramatically reduces the amount of time required to set up a site to be scraped. Once you learn a particular screen-scraping application the amount of time it requires to scrape sites vs. other methods is significantly lowered.

- Support from a commercial company. If you run into trouble while using a commercial screen-scraping application, chances are there are support forums and help lines where you can get assistance.

Disadvantages:

- The learning curve. Each screen-scraping application has its own way of going about things. This may imply learning a new scripting language in addition to familiarizing yourself with how the core application works.

- A potential cost. Most ready-to-go screen-scraping applications are commercial, so you'll likely be paying in dollars as well as time for this solution.

- A proprietary approach. Any time you use a proprietary application to solve a computing problem (and proprietary is obviously a matter of degree) you're locking yourself into using that approach. This may or may not be a big deal, but you should at least consider how well the application you're using will integrate with other software applications you currently have. For example, once the screen-scraping application has extracted the data how easy is it for you to get to that data from your own code?

When to use this approach: Screen-scraping applications vary widely in their ease-of-use, price, and suitability to tackle a broad range of scenarios. Chances are, though, that if you don't mind paying a bit, you can save yourself a significant amount of time by using one. If you're doing a quick scrape of a single page you can use just about any language with regular expressions. If you want to extract data from hundreds of web sites that are all formatted differently you're probably better off investing in a complex system that uses ontologies and/or artificial intelligence. For just about everything else, though, you may want to consider investing in an application specifically designed for screen-scraping.

As an aside, I thought I should also mention a recent project we've been involved with that has actually required a hybrid approach of two of the aforementioned methods. We're currently working on a project that deals with extracting newspaper classified ads. The data in classifieds is about as unstructured as you can get. For example, in a real estate ad the term "number of bedrooms" can be written about 25 different ways. The data extraction portion of the process is one that lends itself well to an ontologies-based approach, which is what we've done. However, we still had to handle the data discovery portion. We decided to use screen-scraper for that, and it's handling it just great. The basic process is that screen-scraper traverses the various pages of the site, pulling out raw chunks of data that constitute the classified ads. These ads then get passed to code we've written that uses ontologies in order to extract out the individual pieces we're after. Once the data has been extracted we then insert it
into a database.

Source: http://ezinearticles.com/?Three-Common-Methods-For-Web-Data-Extraction&id=165416

How to Properly Scrape Windows During The Cleaning Process

Removing ordinary dirt such as dust, fingerprints, and oil from windows seem simple enough. However, sometimes, you may find stubborn caked-on dirt or debris on your windows that cannot be removed by standard window cleaning techniques such as scrubbing or using a squeegee. The best way to remove caked-on dirt on your windows is to scrape it off. Nonetheless, you have to be extra careful when you are scraping your windows, because they can be easily scratched and damaged. Here are a number of rules that you need to follow when you are scraping windows.

Rule No. 1: It is recommended that you use a professional window scraper to remove caked-on dirt and debris from your windows. This type of scraper is specially made for use on glass, and it comes with certain features that can prevent scratching and other kinds of damage.

Rule No. 2: It is important to inspect your window scraper before using it. Take a look at the blade of the scraper and make sure that it is not rusted. Also, it must not be bent or chipped off at the corners. If you are not certain whether the blade is in a good enough condition, you should just play it safe by using a new blade.

Rule No. 3: When you are working with a window scraper, always use forward plow-like scraping motions. Scrape forward and lift the scraper off the glass, and then scrape forward again. Try not to slide the scraper backwards, because you may trap debris under the blade when you do so. Consequently, the scraper may scratch the glass.

Rule No. 4: Be extra cautious when you are using a window scraper on tempered glass. Tempered glass may have raised imperfections, which make it more vulnerable to scratches. To find out if the window that you are scraping is made of tempered glass, you have to look for a label in one of its corners.

Window Scraping Procedures

Before you start scraping, you have to wet your window with soapy water first. Then, find out how the window scraper works by testing it in a corner. Scrape on the same spot three or four times in forward motion. If you find that the scraper is moving smoothly and not scratching the glass, you can continue to work on the rest of the window. On the other hand, if you feel as if the scraper is sliding on sandpaper, you have to stop scraping. This indicates that the glass may be flawed and have raised imperfections, and scraping will result in scratches.

After you have ascertained that it is safe to scrape your window, start working along the edges. It is best that you start scraping from the middle of an edge, moving towards the corners. Work in a one or two inch pattern, until all the edges of the glass are properly scraped. After that, scrape the rest of the window in a straight pattern of four or five inches, working from top to bottom. If you find that the window is beginning to dry while you are working, wet it with soapy water again.

Source: http://ezinearticles.com/?How-to-Properly-Scrape-Windows-During-The-Cleaning-Process&id=6592930