Data Scraping - About Hand Scraped Flooring

Hand scraped hardwood flooring is one of the best floors that you can install in your house.

Advantages of Hand Scraped Hardwood Flooring

The product comes with a number of advantages which include:

Antique and modern technology: The floor professionally brings out the best elements of both antique and modern technology. The modern elements are in the quality of the product.

Unique patterns: Who doesn't want to be unique? These floors allow you to create your unique design. If you are going to use a machine, all you need to do is to set the machine such that it creates the pattern that you want. If the floor will be scraped by a craftsman, you should ask the craftsman to craft your desired pattern.

Character: The different depths in the floor provide you with character and color that you can't find in other types of floors. As the sun changes its angle during the day, the nooks and valleys on the board lit differently thus providing your board with an endless rich appearance.

Durability: Experts have been able to show that hand-scraped hardwood retains its look for a long time. If your kid or pet hits the floor, the dent just blends with the rest of the character making it hard for people to tell that there is a dent.

Making the floors shine again

Although, the scraped floors are designed to look worn and aged, they are made from modern wood which needs to be taken care of in order to retain its original look.

To make the floors shine again you need to remove all the dust and dirt that might be causing the wood to look dull.

After doing this you should mix 1 gallon of warm water with ½ teaspoon of dishwashing detergent and use it to clean the surface of the floor. The aim of doing this is to remove any stains that might be on the floor. When you complete doing this you should dampen the piece of cloth with club soda and then use another piece of cloth to buff the wood until it shines.

Conclusion

This is what you need to know about hand scraped hardwood flooring. When cleaning the floors you should avoid using oil based soaps as they dull the surface making your efforts worthless.

If the above method of shining the floor doesn't work, you should mix one part white vinegar and one part of cooking oil and use it to clean the floor.

Source: http://ezinearticles.com/?About-Hand-Scraped-Flooring&id=8990255

Rvest: easy web scraping with R

Rvest is new package that makes it easy to scrape (or harvest) data from html web pages, by libraries like beautiful soup. It is designed to work with magrittr so that you can express complex operations as elegant pipelines composed of simple, easily understood pieces. Install it with:

install.packages("rvest")

rvest in action

To see rvest in action, imagine we’d like to scrape some information about The Lego Movie from IMDB. We start by downloading and parsing the file with html():

library(rvest)

lego_movie <- html("http://www.imdb.com/title/tt1490017/")

To extract the rating, we start with selectorgadget to figure out which css selector matches the data we want: strong span. (If you haven’t heard of selectorgadget, make sure to read vignette("selectorgadget") – it’s the easiest way to determine which selector extracts the data that you’re interested in.) We use html_node() to find the first node that matches that selector, extract its contents with html_text(), and convert it to numeric with as.numeric():

lego_movie %>%

  html_node("strong span") %>%
  html_text() %>%
  as.numeric()

#> [1] 7.9

We use a similar process to extract the cast, using html_nodes() to find all nodes that match the selector:

lego_movie %>%

  html_nodes("#titleCast .itemprop span") %>%
  html_text()

#>  [1] "Will Arnett"     "Elizabeth Banks" "Craig Berry"   

#>  [4] "Alison Brie"     "David Burrows"   "Anthony Daniels"

#>  [7] "Charlie Day"     "Amanda Farinos"  "Keith Ferguson"

#> [10] "Will Ferrell"    "Will Forte"      "Dave Franco"   

#> [13] "Morgan Freeman"  "Todd Hansen"     "Jonah Hill"

The titles and authors of recent message board postings are stored in a the third table on the page. We can use html_node() and [[ to find it, then coerce it to a data frame with html_table():

lego_movie %>%

  html_nodes("table") %>%
  .[[3]] %>%
  html_table()

#>                                              X 1            NA

#> 1 this movie is very very deep and philosophical   mrdoctor524

#> 2 This got an 8.0 and Wizard of Oz got an 8.1...  marr-justinm

#> 3                         Discouraging Building?       Laestig

#> 4                              LEGO - the plural      neil-476

#> 5                                 Academy Awards   browncoatjw

#> 6                    what was the funniest part? actionjacksin

Other important functions

    If you prefer, you can use xpath selectors instead of css: html_nodes(doc, xpath = "//table//td")).

    Extract the tag names with html_tag(), text with html_text(), a single attribute with html_attr() or all attributes with html_attrs().

    Detect and repair text encoding problems with guess_encoding() and repair_encoding().
    Navigate around a website as if you’re in a browser with html_session(), jump_to(), follow_link(), back(), and forward(). Extract, modify and submit forms with html_form(), set_values() and submit_form(). (This is still a work in progress, so I’d love your feedback.)

To see these functions in action, check out package demos with demo(package = "rvest").

Source: http://www.r-bloggers.com/rvest-easy-web-scraping-with-r/

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

WordPress Titles: scraping with search url

I’ve blogged for a few years now, and I’ve used several tools along the way. zachbeauvais.com began as a Drupal site, until I worked out that it’s a bit overkill, and switched to WordPress. Recently, I’ve been toying with the idea of using a static site generator (a lá Jekyll or Hyde), or even pulling together a kind of ebook of ramblings. I also want to be able to arrange the posts based on the keywords they contain, regardless of how they’re categorised or tagged.

Whatever I wanted to do, I ended up with a single point of messiness: individual blog posts, and how they’re formatted. When I started, I seem to remember using Drupal’s truly awful WYSIWYG editor, and tweaking the HTML soup it produced. Then, when I moved over to WordPress, it pulled all the posts and metadata through via RSS, and I tweaked with the visual and text tools which are baked into the engine.

A couple years ago, I started to write in Markdown, and completely apart from the blog (thanks to full-screen writing and loud music). This gives me a local .md file, and I copy/paste into WordPress using a plugin to get rid of the visual editor entirely.

So, I wrote a scraper to return a list of blog posts containing a specific term. What I hope is that this very simple scraper is useful to others—WordPress is pretty common, after all—and to get some ideas for improving it, and handle post content. If you haven’t used ScraperWiki before, you might not know that you can see the raw scraper by clicking “view source” from the scraper’s overview page (or going here if you’re lazy).

This scraper is based on WordPress’ built-in search, which can be used by passing the search terms to a url, then scraping the resulting page:

http://zachbeauvais.com/?s=search_term&submit=Search

The scraper uses three Python libraries:

    Requests
    ScraperWiki
    lxml.html

There are two variables which can be changed to search for other terms, or using a different WordPress site:

term = "coffee"

site = "http://www.zachbeauvais.com"

The rest of the script is really simple: it creates a dictionary called “payload” containing the letter “s”, the keyword, and the instruction to search. The “s” is in there to make up the search url: /?s=coffee …

Requests then GETs the site, passing payload as url parameters, and I use Request’s .text function to render the page in html, which I then pass through lxml to the new variable “root”.

payload = {'s': str(term), 'submit': 'Search'}

r = requests.get(site, params=payload)  # This'll be the results page

html = r.text

root = lxml.html.fromstring(html)  # parsing the HTML into the var root

Now, my WordPress theme renders the titles of the retrieved posts in <h1> tags with the CSS class “entry-title”, so I loop through the html text, pulling out the links and text from all the resulting h1.entry-title items. This part of the script would need tweaking, depending on the CSS class and h-tag your theme uses.

for i in root.cssselect("h1.entry-title a"):

    link = i.cssselect("a")

    text = i.text_content()

    data = {

        'uri': link[0].attrib['href'],

        'post-title': str(text),

        'search-term': str(term)

    }

    if i is not None:

        print link

        print text

        print data

        scraperwiki.sqlite.save(unique_keys=['uri'], data=data)

    else:

        print "No results."

These return into an sqlite database via the ScraperWiki library, and I have a resulting database with the title and link to every blog post containing the keyword.

So, this could, in theory, run on any WordPress instance which uses the same search pattern URL—just change the site variable to match.

Also, you can run this again and again, changing the term to any new keyword. These will be stored in the DB with the keyword in its own column to identify what you were looking for.

See? Pretty simple scraping.

So, what I’d like next is to have a local copy of every post in a single format.

Has anyone got any ideas how I could improve this? And, has anyone used WordPress’ JSON API? It might be a logical next step to call the API to get the posts directly from the MySQL DB… but that would be a new blog post!

Source: https://scraperwiki.wordpress.com/2013/03/11/wordpress-titles-scraping-with-search-url/