Data Scraping - Enjoy the Appeal of the Hand Scraped Flooring

Hand scraped flooring is appreciated for the character it brings into the home. This style of flooring relies on hand scraped planks of wood and not the precise milled boards. The irregularities in the planks provide a certain degree of charm and help to create a more unique feature in the home.

Distressed vs. Hand scraped

There are two types of flooring in the market that have an aged and unique charm with a non perfect finish. However, there is a significant difference in the process used to manufacture the planks. The more standard distresses flooring is cut on a factory production line. The grooves, scratches, dents, or other irregularities in these planks are part of the manufacturing process and achieved by rolling or pressed the wood onto a patterned surface.

The real hand scraped planks are made by craftsmen and they work on each plant individually. By using this working technique, there is complete certainty that each plank will be unique in appearance.

Scraping the planks

The hand scraping process on the highest-quality planks is completed by the trained carpenter or craftsmen who will produce a high-quality end product and take great care in their workmanship. It can benefit to ask the supplier of the flooring to see who completes the work.

Beside the well scraped lumber, there are also those planks that have been bought from the less than desirable sources. This is caused by the increased demand for this type of flooring. At the lower end of the market the unskilled workers are used and the end results aren't so impressive.

The high-quality plank has the distinctive look that feels and functions perfectly well as solid flooring, while the low-quality work can appear quite ugly and cheap.

Even though it might cost a little bit more, it benefits to source the hardwood floor dealers that rely on the skilled workers to complete the scraping process.

Buying the right lumber

Once a genuine supplier is found, it is necessary to determine the finer aspects of the wooden flooring. This hand scraped flooring is available in several hardwoods, such as oak, cherry, hickory, and walnut. Plus, it comes in many different sizes and widths. A further aspect relates to the finish with darker colored woods more effective at highlighting the character of the scraped boards. This makes the shadows and lines appear more prominent once the planks have been installed at home.

Why not visit Bellacerafloors.com for the latest collection of luxury floor materials, including the Handscraped Hardwood Flooring.

Source: http://ezinearticles.com/?Enjoy-the-Appeal-of-the-Hand-Scraped-Flooring&id=8995784

Web Scraping: working with APIs

APIs present researchers with a diverse set of data sources through a standardised access mechanism: send a pasted together HTTP request, receive JSON or XML in return. Today we tap into a range of APIs to get comfortable sending queries and processing responses.

These are the slides from the final class in Web Scraping through R: Web scraping for the humanities and social sciences

This week we explore how to use APIs in R, focusing on the Google Maps API. We then attempt to transfer this approach to query the Yandex Maps API. Finally, the practice section includes examples of working with the YouTube V2 API, a few ‘social’ APIs such as LinkedIn and Twitter, as well as APIs less off the beaten track (Cricket scores, anyone?).

I enjoyed teaching this course and hope to repeat and improve on it next year. When designing the course I tried to cram in everything I wish I had been taught early on in my PhD (resulting in information overload, I fear). Still, hopefully it has been useful to students getting started with digital data collection, showing on the one hand what is possible, and on the other giving some idea of key steps in achieving research objectives.

Download the .Rpres file to use in Rstudio here

A regular R script with code-snippets only can be accessed here

Slides from the first session here

Slides from the second session here

Slides from the third session here

Source: http://www.r-bloggers.com/web-scraping-working-with-apis/

Web Scraping Services : Data Discovery vs. Data Extraction

Looking at screen-scraping at a simplified level, there are two primary stages involved: data discovery and data extraction. Data discovery deals with navigating a web site to arrive at the pages containing the data you want, and data extraction deals with actually pulling that data off of those pages. Generally when people think of screen-scraping they focus on the data extraction portion of the process, but my experience has been that data discovery is often the more difficult of the two.

The data discovery step in screen-scraping might be as simple as requesting a single URL. For example, you might just need to go to the home page of a site and extract out the latest news headlines. On the other side of the spectrum, data discovery may involve logging in to a web site, traversing a series of pages in order to get needed cookies, submitting a POST request on a search form, traversing through search results pages, and finally following all of the "details" links within the search results pages to get to the data you're actually after. In cases of the former a simple Perl script would often work just fine. For anything much more complex than that, though, a commercial screen-scraping tool can be an incredible time-saver. Especially for sites that require logging in, writing code to handle screen-scraping can be a nightmare when it comes to dealing with cookies and such.

In the data extraction phase you've already arrived at the page containing the data you're interested in, and you now need to pull it out of the HTML. Traditionally this has typically involved creating a series of regular expressions that match the pieces of the page you want (e.g., URL's and link titles). Regular expressions can be a bit complex to deal with, so most screen-scraping applications will hide these details from you, even though they may use regular expressions behind the scenes.

As an addendum, I should probably mention a third phase that is often ignored, and that is, what do you do with the data once you've extracted it? Common examples include writing the data to a CSV or XML file, or saving it to a database. In the case of a live web site you might even scrape the information and display it in the user's web browser in real-time. When shopping around for a screen-scraping tool you should make sure that it gives you the flexibility you need to work with the data once it's been extracted.

Source: http://ezinearticles.com/?Data-Discovery-vs.-Data-Extraction&id=165396

Scraping the Royal Society membership list

To a data scientist any data is fair game, from my interest in the history of science I came across the membership records of the Royal Society from 1660 to 2007 which are available as a single PDF file. I’ve scraped the membership list before: the first time around I wrote a C# application which parsed a plain text file which I had made from the original PDF using an online converting service, looking back at the code it is fiendishly complicated and cluttered by boilerplate code required to build a GUI. ScraperWiki includes a pdftoxml function so I thought I’d see if this would make the process of parsing easier, and compare the ScraperWiki experience more widely with my earlier scraper.

The membership list is laid out quite simply, as shown in the image below, each member (or Fellow) record spans two lines with the member name in the left most column on the first line and information on their birth date and the day they died, the class of their Fellowship and their election date on the second line.

Later in the document we find that information on the Presidents of the Royal Society is found on the same line as the Fellow name and that Royal Patrons are formatted a little differently. There are also alias records where the second line points to the primary record for the name on the first line.

pdftoxml converts a PDF into an xml file, wherein each piece of text is located on the page using spatial coordinates, an individual line looks like this:

<text top="243" left="135" width="221" height="14" font="2">Abbot, Charles, 1st Baron Colchester </text>

This makes parsing columnar data straightforward you simply need to select elements with particular values of the “left” attribute. It turns out that the columns are not in exactly the same positions throughout the whole document, which appears to have been constructed by tacking together the membership list A-J with that of K-Z, but this can easily be resolved by accepting a small range of positions for each column.

Attempting to automatically parse all 395 pages of the document reveals some transcription errors: one Fellow was apparently elected on 16th March 197 – a bit of Googling reveals that the real date is 16th March 1978. Another fellow is classed as a “Felllow”, and whilst most of the dates of birth and death are separated by a dash some are separated by an en dash which as far as the code is concerned is something completely different and so on. In my earlier iteration I missed some of these quirks or fixed them by editing the converted text file. These variations suggest that the source document was typed manually rather than being output from a pre-existing database. Since I couldn’t edit the source document I was obliged to code around these quirks.

ScraperWiki helpfully makes putting data into a SQLite database the simplest option for a scraper. My handling of dates in this version of the scraper is a little unsatisfactory: presidential terms are described in terms of a start and end year but are rendered 1st January of those years in the database. Furthermore, in historical documents dates may not be known accurately so someone may have a birth date described as “circa 1782? or “c 1782?, even more vaguely they may be described as having “flourished 1663-1778? or “fl. 1663-1778?. Python’s default datetime module does not capture this subtlety and if it did the database used to store dates would need to support it too to be useful – I’ve addressed this by storing the original life span data as text so that it can be analysed should the need arise. Storing dates as proper dates in the database, rather than text strings means we can query the database using date based queries.

ScraperWiki provides an API to my dataset so that I can query it using SQL, and since it is public anyone else can do this too. So, for example, it’s easy to write queries that tell you the the database contains 8019 Fellows, 56 Presidents, 387 born before 1700, 3657 with no birth date, 2360 with no death date, 204 “flourished”, 450 have birth dates “circa” some year.

I can count the number of classes of fellows:

select distinct class,count(*) from `RoyalSocietyFellows` group by class

Make a table of all of the Presidents of the Royal Society

select * from `RoyalSocietyFellows` where StartPresident not null order by StartPresident desc

…and so on. These illustrations just use the ScraperWiki htmltable export option to display the data as a table but equally I could use similar queries to pull data into a visualisation.

Comparing this to my earlier experience, the benefits of using ScraperWiki are:

•    Nice traceable code to provide a provenance for the dataset;

•    Access to the pdftoxml library;

•    Strong encouragement to “do the right thing” and put the data into a database;

•    Publication of the data;

•    A simple API giving access to the data for reuse by all.

My next target for ScraperWiki may well be the membership lists for the French Academie des Sciences, a task which proved too complex for a simple plain text scraper…

Source: https://scraperwiki.wordpress.com/2012/12/28/scraping-the-royal-society-membership-list/

Data Scraping Services - Login to Website Programmatically using C# for Web Scraping

In many scenario the data is available after login that you want to scrape. So to reach at the page where data is located you need to implement code in web scraper  that automatically takes usename/email and password to login into website, once login is done you can do crawling and parsing as required.

Many third party web scraping application provides functionality where you can locate login url and set login parameters and that login task will be called when scraper start and do web scraping.

Below is C# example of programmatically  login to demo login page

http://demo.webdata-scraping.com/login.php

Below is HTML code of Login form:

<form class="form-signin" id="login" method="post" role="form"> <h3 class="form-signin-heading">Please sign in</h3> <a href="#" id="flipToRecover" class="flipLink"> <div id="triangle-topright"></div> </a> <input type="email" class="form-control" name="loginEmail" id="loginEmail" placeholder="Email address" required autofocus> <input type="password" class="form-control" name="loginPass" id="loginPass" placeholder="Password" required> <button class="btn btn-lg btn-primary btn-block" name="login_submit" id="login_submit" type="submit">Sign in</button> </form>

<form class="form-signin" id="login" method="post" role="form">

            <h3 class="form-signin-heading">Please sign in</h3>

            <a href="#" id="flipToRecover" class="flipLink">

              <div id="triangle-topright"></div>

            </a>

            <input type="email" class="form-control" name="loginEmail" id="loginEmail" placeholder="Email address" required autofocus>

            <input type="password" class="form-control" name="loginPass" id="loginPass" placeholder="Password" required>

            <button class="btn btn-lg btn-primary btn-block" name="login_submit" id="login_submit" type="submit">Sign in</button>

</form>

In this code you can notice there is ID for email input box that is id=”loginEmail”  and password input box that is id=”loginPass”

so by taking this ID we will use below two method of webBrowser control and fill the value of each input box using following code

webBrowser1.Document.GetElementById("loginEmail").InnerText =textBox1.Text.ToString(); webBrowser1.Document.GetElementById("loginPass").InnerText = textBox2.Text.ToString();

webBrowser1.Document.GetElementById("loginEmail").InnerText =textBox1.Text.ToString();

webBrowser1.Document.GetElementById("loginPass").InnerText = textBox2.Text.ToString();

After the value filled to Email and Password input box we will just call click event of submit button which is named as Sign In

webBrowser1.Document.GetElementById("login_submit").InvokeMember("click");

webBrowser1.Document.GetElementById("login_submit").InvokeMember("click");

So this is very basic example how you can login to website programatically when you need to access data that is available after login to website.  This is very simple way in which you can work with Web Browser control but there are some other way as well using which you can do same thing.

Source: http://webdata-scraping.com/login-website-programmatically-using-c-web-scraping/

Data Extraction Services

Are you finding it tedious to perform your routine tasks as well as finding time to research for some information? Don't worry; all you have to do is outsource data extraction requirements to reliable service providers such as Hi-Tech BPO Services.

We can assist you in finding, extracting, gathering, processing and validating all the required data through our effective data extraction services. We can extract data from any given source such as websites, databases, printed documents, directories, etc.

With a whole plethora of data extraction services solutions; we are definitely a one stop solution to all your data extraction services requirements.

For utilizing our data extraction services, all you have to do is outsource data extraction requirements to us, and we will create effective strategies and extract the required data from all preferred sources. Then we will arrange all the extracted data in a systematic order.

Types of data extraction services provided by our data extraction India unit:

The data extraction India unit of Hi-Tech BPO Services can attend to all types of outsource data extraction requirements. Following are just some of the data extraction services we have delivered:

•    Data extraction from websites
•    Data extraction from databases
•    Extraction of data from directories
•    Extracting data from books
•    Data extraction from forms
•    Extracting data from printed materials

Features of Our Data Extraction Services:

•    Reliable collection of resources for data extraction
•    Extensive range of data extraction services
•    Data can be extracted from any available source be it a digital source or a hard copy source
•    Proper researching, extraction, gathering, processing and validation of data
•    Reasonably priced data extraction services
•    Quality and confidentiality ensured through various strict measures

Our data extraction India unit has the competency to handle any of your data extraction services requirements. Just provide us with your specific requirements and we will extract data accordingly from your preferred resources, if particularly specified. Otherwise we will completely rely on our collection of resources for extracting data for you.

Source: http://www.hitechbposervices.com/data-extraction.php

Which language is the most flexible for scraping websites?

3 down vote favorite

I'm new to programming. I know a little python and a little objective c, and I've been going through tutorials for each. Then it occurred to me, I need to know which language is more flexible (python, obj c, something else) for screen scraping a website for content.

What do I mean by "flexible"?

Well, ideally, I need something that will be easy to refactor and tweak for similar projects. I'm trying to avoid doing a lot of re-writing (well, re-coding) if I wanted to switch some of the variables in the program (i.e., the website to be scraped, the content to fetch, etc).

Anyways, if you could please give me your opinion, that would be great. Oh, and if you know any existing frameworks for the language you recommend, please share. (I know a little about Selenium and BeautifulSoup for python already).

4 Answers

I recently wrote a relatively complex web scraper to harvest a TON of data. It had to do some relatively complex parsing, I needed it to stuff it into a database, etc. I'm C# programmer now and formerly a Perl guy.

I wrote my original scraper using Python. I started on a Thursday and by Sunday morning I was harvesting over about a million scores from a show horse site. I used Python and SQLlite because they were fast.

HOWEVER, as I started putting together programs to regularly keep the data updated and to populate the SQL Server that would backend my MVC3 application, I kept hitting snags and gaps in my Python knowledge.

In the end, I completely rewrote the scraper/parser in C# using the HtmlAgilityPack and it works better than before (and just about as fast).

Because I KNEW THE LANGUAGE and the environment so much better I was able to add better database support, better logging, better error handling, etc. etc.

So... short answer.. Python was the fastest to market with a "good enough for now" solution, but the language I know best (C#) was the best long-term solution.

EDIT: I used BeautifulSoup for my original crawler written in Python.

5 down vote

The most flexible is the one that you're most familiar with.

Personally, I use Python for almost all of my utilities. For scraping, I find that its functionality specific to parsing and string manipulation requires little code, is fast and there are a ton of examples out there (strong community). Chances are that someone's already written whatever you're trying to do already, or there's at least something along the same lines that needs very little refactoring.

1 down vote

I think its safe to say that Python is a better place to start than Objective C. Honestly, just about any language meets the "flexible" requirement. All you need is well thought out configuration parameters. Also, a dynamic language like Python can go a long way in increasing flexibility, provided that you account for runtime type errors.

1 down vote

I recently wrote a very simple web-scraper; I chose Common Lisp as I'm learning the language.

On the basis of my experience - both of the language and the availability of help from experienced Lispers - I recommend investigating Common Lisp for your purpose.

There are excellent XML-parsing libraries available for CL, as well as libraries for parsing invalid HTML, which you'll need unless the sites you're parsing consist solely of valid XHTML.

Also, Common Lisp is a good language in which to implement DSLs; a DSL for web-scraping may be a solution to your requirement for flexibility & re-use.

Source: http://programmers.stackexchange.com/questions/74998/which-language-is-the-most-flexible-for-scraping-websites/75006#75006