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/

Web Scraping : Data Mining vs Screen-Scraping

Data mining isn't screen-scraping. I know that some people in the room may disagree with that statement, but they're actually two almost completely different concepts.

In a nutshell, you might state it this way: screen-scraping allows you to get information, where data mining allows you to analyze information. That's a pretty big simplification, so I'll elaborate a bit.

The term "screen-scraping" comes from the old mainframe terminal days where people worked on computers with green and black screens containing only text. Screen-scraping was used to extract characters from the screens so that they could be analyzed. Fast-forwarding to the web world of today, screen-scraping now most commonly refers to extracting information from web sites. That is, computer programs can "crawl" or "spider" through web sites, pulling out data. People often do this to build things like comparison shopping engines, archive web pages, or simply download text to a spreadsheet so that it can be filtered and analyzed.

Data mining, on the other hand, is defined by Wikipedia as the "practice of automatically searching large stores of data for patterns." In other words, you already have the data, and you're now analyzing it to learn useful things about it. Data mining often involves lots of complex algorithms based on statistical methods. It has nothing to do with how you got the data in the first place. In data mining you only care about analyzing what's already there.

The difficulty is that people who don't know the term "screen-scraping" will try Googling for anything that resembles it. We include a number of these terms on our web site to help such folks; for example, we created pages entitled Text Data Mining, Automated Data Collection, Web Site Data Extraction, and even Web Site Ripper (I suppose "scraping" is sort of like "ripping"). So it presents a bit of a problem-we don't necessarily want to perpetuate a misconception (i.e., screen-scraping = data mining), but we also have to use terminology that people will actually use.

Source: http://ezinearticles.com/?Data-Mining-vs-Screen-Scraping&id=146813

Twitter Scraper Python Library

I wanted to save the tweets from Transparency Camp. This prompted me to turn Anna‘s basic Twitter scraper into a library. Here’s how you use it.

Import it. (It only works on ScraperWiki, unfortunately.)

from scraperwiki import swimport

search = swimport('twitter_search').search

Then search for terms.

search(['picnic #tcamp12', 'from:TCampDC', '@TCampDC', '#tcamp12', '#viphack'])

A separate search will be run on each of these phrases. That’s it.

A more complete search

Searching for #tcamp12 and #viphack didn’t get me all of the tweets because I waited like a week to do this. In order to get a more complete list of the tweets, I looked at the tweets returned from that first search; I searched for tweets referencing the users who had tweeted those tweets.

from scraperwiki.sqlite import save, select

from time import sleep

# Search by user to get some more

users = [row['from_user'] + ' tcamp12' for row in \

select('distinct from_user from swdata where from_user where user > "%s"' \

% get_var('previous_from_user', ''))]

for user in users:

    search([user], num_pages = 2)

    save_var('previous_from_user', user)

    sleep(2)

By default, the search function retrieves 15 pages of results, which is the maximum. In order to save some time, I limited this second phase of searching to two pages, or 200 results; I doubted that there would be more than 200 relevant results mentioning a particular user.

The full script also counts how many tweets were made by each user.

Library

Remember, this is a library, so you can easily reuse it in your own scripts, like Max Richman did.

Source: https://scraperwiki.wordpress.com/2012/07/04/twitter-scraper-python-library/