Web Data Scraping Services At Lowest Rate For Business Directory

We are the world's most trusted provider directory, your business data scrape, and scrape email scraping and sending the data needed. We scour the entire directory database or doctors, lawyers, brokers, financial advisers, etc. As the scraping of a particular industry category wise database scraping or data that can be adapted.

We are pioneers in the worldwide web scraping and data services. We must understand the value of our customer database, we email id with the greatest effort to collect data. We are lawyers, doctors, brokers, realtors, schools, students, universities, IT managers, pubs, bars, nightclubs, dance clubs, financial advisers, liquor stores, Face book, Twitter, pharmaceutical companies, mortgage broker scraped data, accounting firms, car dealers , artists, shop health and job portals.

Our business database development services to try and get real quality at the lowest possible industry. Example worked. We have a quick turnaround time can be a business mailing database. Our business database development services to try and get real quality at the lowest possible industry. Example worked. We have a quick turnaround time can be a business mailing database.

We are the world's most trusted provider directory, your business data scrape, and scrape email scraping and sending the data needed. We scour the entire directory database or doctors, lawyers, brokers, financial advisers, etc., as the scraping of a particular industry category wise database scraping or data that can be adapted.

We are pioneers in the worldwide web scraping and data services. We must understand the value of our customer database, we email id with the greatest effort to collect data. We are lawyers, doctors, brokers, realtors, schools, students, universities, IT managers, pubs, bars, nightclubs, dance clubs, financial advisers, liquor stores, Face book, Twitter, pharmaceutical companies, mortgage broker scraped data, accounting firms, car dealers , artists, shop health and job portals.

What a great resource for specific information or content with little success to gather and have tried to organize themselves in a folder? You no longer need to worry, and data processing services through our website search are the best solution for your problem.

We currently have an "information explosion" phase of the walk, where there is so much information and content information for an event or a small group of channels.

Order without the benefit of you and your customers a little truth to that information. You use information and material is easy to organize in a way that is needed. Something other than a small business guide, simply create a separate folder in less than an hour.

Our technology-specific Web database for you to a similar configuration and database development to use. In addition, we finished our services can help you through the data to identify the sources of information for web pages to follow. This is a cost effective way to create a database.

We offer directory database, company name, address, the state, country, phone, email and website URL to take. In recent projects we have completed. We have a quick turnaround time can be a business mailing database. Our business database development services to try and get real quality at the lowest possible industry.

Source:http://www.articlesbase.com/outsourcing-articles/web-data-scraping-services-at-lowest-rate-for-business-directory-5757029.html

So What Exactly Is A Private Data Scraping Services To Use You?

If your computer connects to the Internet or resources on the request for this information, and queries to different servers. If you have a website to introduce to the site server recognizes your computer's IP address and displays the data and much more. Many e - commerce sites use to log your IP address, and the browsing patterns for marketing purposes.

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The  Scraping server you connect to your destination or to process your information and make a filter. For example, IP address or protocol filtering traffic through a  Scraping service. As you might guess, there are many types of  Scraping services. including the ability to a high demand for the software. Email messages are quickly sent to businesses and companies to help you search for contacts.

Although there are Sanding free  Scraping IP addresses in this way can work, the use of payment services, and automatic user interface (plug and play) are easy to give.  Scraping web information services, thus offering a variety of relevant sources of data.  Scraping information service organizations are generally used where large amounts of data every day. It is possible for you to receive efficient, high precision is also affordable.

Information on the various strategies that companies,  Scraping excellent information services, and use the structure planned out and has led to the introduction of more rapid relief of the Earth.

In addition, the application software that has flexibility as a priority. In addition, there is a software that can be tailored to the needs of customers, and satisfy various customer requirements play a major role. Particular software, allows businesses to sell, a customer provides the features necessary to provide the best experience.

If you do not use a private Data Scraping Services suggest that you immediately start your Internet marketing. It is an inexpensive but vital to your marketing company. To choose how to set up a private  Scraping service, visit my blog for more information. Data Scraping Services software as the activity data and provides a large amount of information, Sorting. In this way, the company reduced the cost and time savings and greater return on investment will be a concept.

Without the steady stream of data from these sites to get stopped? Scraping HTML page requests sent by argument on the web server, depending on changes in production, it is very likely to break their staff. 

Data Scraping Services is common in the respective outsourcing company. Many companies outsource  Data Scraping Services service companies are increasingly outsourcing these services, and generally dealing with the Internet business-related activities, in particular a lot of money, can earn.

Web  Data Scraping Services, pull information from a structured plan format. Informal or semi-structured data source from the source.They are there to just work on your own server to extract data to execute. IP blocking is not a problem for them when they switch servers in minutes and back on track, scraping exercise. Try this service and you'll see what I mean.

It is an inexpensive but vital to your marketing company. To choose how to set up a private  Scraping service, visit my blog for more information. Data Scraping Services software as the activity data and provides a large amount of information, Sorting. In this way, the company reduced the cost and time savings and greater return on investment will be a concept.

Source:http://www.articlesbase.com/outsourcing-articles/so-what-exactly-is-a-private-data-scraping-services-to-use-you-5587140.html

Choose Mining Wear Parts Wisely

It is important to choose a reputable supplier of mining wear parts; one that has been acknowledged as a leader in mining expertise. You will want to research and seek out a company that specializes in the engineering, manufacturing, procurement and design of mining wear parts and who has access to a multitude of patterns and templates to choose from.

It is vital to find a company that invites you to put them to the test; a company that is committed to selling more than just a product, standing behind the parts that they design and manufacture with an unprecedented industry guarantee. Some companies are so confident in their products that each wear part is stamped with their logo, identifying it as a superior product.

You will also want to find a company that takes pride in establishing strong customer relationships and who employs people who are as equally committed to providing outstanding service with customer satisfaction a priority. Your research will help you find a mining wear parts company that guarantees that if they do not have the part available, that they will find it for you or are capable of custom designing products to your exact specifications.

If you stop to consider the ramifications of an equipment malfunction or breakdown on production quotas, the significance of reliable parts becomes readily apparent. The impact can be far reaching if it halts production while the necessary repairs are completed. The ugly reality is that downtime incurs financial losses.

While the cost of aftermarket replacement mining wear parts is one factor, the installation of the part is equally as important. It is vital that aftermarket parts are built to a rugged standard to endure the rigorous industrial demands placed on them. Mining wear parts are routinely subjected to high stress abrasion and impact. The fabricated parts need to have the structural strength to be wear resistant with extended usage. Hardened manganese is the preferred material of choice to impart added strength and avoid premature breakage and replacement. Using inferior quality parts may result in the necessity of replacing them prematurely if they do not withstand the wear and tear that they are subjected to daily. While a few dollars may be saved initially by purchasing inferior mining wear parts, production costs can dramatically increase if frequent breakdowns occur and manpower hours are wasted in the field. Efficient use of manpower is an important budget consideration. Reliability is an absolute necessity w
hen you have production deadlines to meet and operations can quickly grind to a standstill when production is halted.

Quality assurance management monitors the consistency of the parts, demanding that they are machined within precise measurements. In addition, they focus on striving to improve the quality of parts as new technology becomes available. Using precision made, high quality wear parts can make your business more competitive, giving you an advantage and improving your bottom line.

Source:http://ezinearticles.com/?Choose-Mining-Wear-Parts-Wisely&id=6691631

Scraping table from html web with CloudStat

You need to use the data from internet, but don’t type, you can just extract or scrape them if you know the web URL.

Thanks to XML package from R. It provides amazing readHTMLtable() function.

For a study case,

I want to scrape data:

    US Airline Customer Score.
    World Top Chess Players (Men).

A. Scraping US Airline Customer Score table from

http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines

Code:

airline = ‘http://www.theacsi.org/index.php?option=com_content&view=article&id=147&catid=&Itemid=212&i=Airlines’

airline.table = readHTMLTable(airline, header=T, which=1,stringsAsFactors=F)

Result:

B. Scraping World Top Chess players (Men) table from http://ratings.fide.com/top.phtml?list=men

Code:

chess = ‘http://ratings.fide.com/top.phtml?list=men’

chess.table = readHTMLTable(chess, header=T, which=5,stringsAsFactors=F)

Result:

Done. You had successfully scraping data from any web page with CloudStat.

You can get the full version of this study case (code and result) at Scraping table from html web.

Then, you can analyze as usual! Great! No more retype the data. Enjoy!

Source:http://www.r-bloggers.com/scraping-table-from-html-web-with-cloudstat/

Extracting Wisdom Teeth Tips

It is believed that due to evolution, our jaws are now smaller than our ancient ancestors'. For this reason, our mouths often do not have adequate room to accommodate the third molars, making them basically useless and in some cases detrimental. Even if they are not impacted, wisdom teeth may be hard to clean, and therefore require removal to reduce the probability of caries and infection.

As part of your routine dental visits, your dentist will likely take X-rays to monitor the development of your third molars. Your dentist will likely recommend removing them as soon as possible to avoid any complications. The extraction of wisdom teeth can sometimes be a costly and daunting procedure; for these reasons many patients delay having them extracted. However, if the impacted teeth become infected, it is important to see your dental professional at once. Symptoms of infection due to impacted wisdom teeth include;

•    Pain in the gums and surrounding areas
•    Red or inflamed gums
•    Tender or bleeding gums
•    Inflammation around the face and jaw
•    Bad breath (halitosis)
•    Frequent headaches

If a single molar needs to be extracted, local anesthetic will be used. In the case where several or all the teeth need extraction, the patient will usually be "put under" using a general anesthetic. If you have an infection or medical complications that put you at a higher than normal risk, the surgery may be performed at a hospital. Extraction of the wisdom teeth is a day surgery, and patients are usually able to return to normal activities in a day or so. You may be prescribed antibiotics prior to the surgery, and you will likely be asked not to eat or drink the night before the surgery.

During the surgery, your dentist makes an incision in the gum tissue covering the tooth. Once the tooth is exposed, the dentist may cut the tooth into smaller pieces to make extraction easier. After the extraction you will be given stitches to mend the gum tissue. You may need to return a few days later to have the stitches removed. You will be monitored after the surgery to ensure that you are not bleeding excessively.

The best time for extraction is when the patient is in their late teens to avoid unnecessary complications. Wisdom teeth extractions performed later in life are still beneficial, but the removal may be more difficult and healing may take longer. Therefore it is wise to have a conversation with your dentist regarding your wisdom teeth as early as possible.

Most people will experience the emergence of their wisdom teeth at some point in their life, and extraction is sometimes necessary as a preventative measure or to fix an actual problem or to prevent problem. It is best to deal with any problems regarding your wisdom teeth as soon as possible to avoid unnecessary difficulties.

Source:http://ezinearticles.com/?Extracting-Wisdom-Teeth-Tips&id=7788863

Online Data Entry and Data Mining Services

Data entry job involves transcribing a particular type of data into some other form. It can be either online or offline. The input data may include printed documents like Application forms, survey forms, registration forms, handwritten documents etc.

Data entry process is an inevitable part of the job to any organization. One way or other each organization demands data entry. Data entry skills vary depends upon the nature of the job requirement, in some cases data to be entered from a hard copy formats and in some other cases data to be entered directly into a web portal. Online data entry job generally requires the data to be entered in to any online data base.

For a super market, data associate might be required to enter the goods which have sold in a particular day and the new goods received in a particular day to maintain the stock well in order. Also, by doing this the concerned authorities will get an idea about the sale particulars of each commodity as they requires. In another example, an office the account executive might be required to input the day to day expenses in to the online accounting database in order to keep the account well in order.

The aim of the data mining process is to collect the information from reliable online sources as per the requirement of the customer and convert it to a structured format for the further use. The major source of data mining is any of the internet search engine like Google, Yahoo, Bing, AOL, MSN etc. Many search engines such as Google and Bing provide customized results based on the user's activity history. Based on our keyword search, the search engine lists the details of the websites from where we can gather the details as per our requirement.

Collect the data from the online sources such as Company Name, Contact Person, Profile of the Company, Contact Phone Number of Email ID Etc. are doing for the marketing activities. Once the data is gathered from the online sources into a structured format, the marketing authorities will start their marketing promotions by calling or emailing the concerned persons, which may result to create a new customer. So basically data mining is playing a vital role in today's business expansions. By outsourcing the data entry and its related works, you can save the cost that would be incurred in setting up the necessary infrastructure and employee cost.

Source:http://ezinearticles.com/?Online-Data-Entry-and-Data-Mining-Services&id=7713395

Autoscraping casts a wider net

We have recently started letting more users into the private beta for our Autoscraping service. We’re receiving a lot of applications following the shutdown of Needlebase and we’re increasing our capacity to accommodate these users.

Natalia made a screencast to help our new users get started:

It’s also a great introduction to what this service can do.

We released slybot as an open source integration of the scrapely extraction library and the scrapy framework. This is the core technology behind the autoscraping service and we will make it easy to export autoscraping spiders from Scrapinghub  and run them completely with slybot – allowing our users to have the flexibility and freedom provided by open source.

Source:http://blog.scrapinghub.com/2012/02/27/autoscraping-casts-a-wider-net/

Handling exceptions in scrapers

When requesting and parsing data from a source with unknown properties and random behavior (in other words, scraping), I expect all kinds of bizarrities to occur. Managing exceptions is particularly helpful in such cases.

Here is some ways that an exception might be raised.
[][0] #The list has no zeroth element, so this raises an IndexError
{}['foo'] #The dictionary has no foo element, so this raises a KeyError

Catching the exception is sometimes cleaner than preventing it from happening in the first place. Here are some examples handling bizarre exceptions in scrapers.

Example 1: Inconsistant date formats

Let’s say we’re parsing dates.
import datetime
This doesn’t raise an error.
datetime.datetime.strptime('2012-04-19', '%Y-%m-%d')
But this does.
datetime.datetime.strptime('April 19, 2012', '%Y-%m-%d')

It raises a ValueError because the date formats don’t match. So what do we do if we’re scraping a data source with multiple date formats?

Ignoring unexpected date formats

A simple thing is to ignore the date formats that we didn’t expect.

import lxml.html
import datetime
def parse_date1(source):
    rawdate = lxml.html.fromstring(source).get_element_by_id('date').text
    try:
         cleandate = datetime.datetime.strptime(rawdate, '%Y-%m-%d')
    except ValueError:
         cleandate = None
    return cleandate

print parse_date1('<div id="date">2012-04-19</div>')

If we make a clean date column in a database and put this in there, we’ll have some rows with dates and some rows with nulls. If there are only a few nulls, we might just parse those by hand.

Trying multiple date formats

Maybe we have determined that this particular data source uses three different date formats. We can try all three.

import lxml.html
import datetime

def parse_date2(source):

    rawdate = lxml.html.fromstring(source).get_element_by_id('date').text

    for date_format in ['%Y-%m-%d', '%B %d, %Y', '%d %B, %Y']:

        try:
             cleandate = datetime.datetime.strptime(rawdate, date_format)
             return cleandate
        except ValueError:
             pass
    return None

print parse_date2('<div id="date">19 April, 2012</div>')

This loops through three different date formats and returns the first one that doesn’t raise the error.

Example 2: Unreliable HTTP connection

If you’re scraping an unreliable website or you are behind an unreliable internet connection, you may sometimes get HTTPErrors or URLErrors for valid URLs. Trying again later might help.

import urllib2
def load(url):
    retries = 3
    for i in range(retries):
        try:
            handle = urllib2.urlopen(url)
            return handle.read()
        except urllib2.URLError:
            if i + 1 == retries:
                raise
            else:
                time.sleep(42)
    # never get here

print load('http://thomaslevine.com')

This function tries to download the page thee times. On the first two fails, it waits 42 seconds and tries again. On the third failure, it raises the error. On a success, it returs the content of the page.

Example 3: Logging errors rather than raising them

For more complicated parses, you might find loads of errors popping up in weird places, so you might want to go through all of the documents before deciding which to fix first or whether to do some of them manually.

import scraperwiki
for document_name in document_names:
    try:
        parse_document(document_name)
    except Exception as e:
        scraperwiki.sqlite.save([], {
            'documentName': document_name,
            'exceptionType': str(type(e)),
            'exceptionMessage': str(e)
        }, 'errors')

This catches any exception raised by a particular document, stores it in the database and then continues with the next document. Looking at the database afterwards, you might notice some trends in the errors that you can easily fix and some others where you might hard-code the correct parse.

Example 4: Exiting gracefully

When I’m scraping over 9000 pages and my script fails on page 8765, I like to be able to resume where I left off. I can often figure out where I left off based on the previous row that I saved to a database or file, but sometimes I can’t, particularly when I don’t have a unique index.


for bar in bars:
    try:
        foo(bar)
    except:
        print('Failure at bar = "%s"' % bar)
        raise

This will tell me which bar I left off on. It’s fancier if I save the information to the database, so here is how I might do that with ScraperWiki.

import scraperwiki
resume_index = scraperwiki.sqlite.get_var('resume_index', 0)
for i, bar in enumerate(bars[resume_index:]):
    try:
        foo(bar)
    except:
        scraperwiki.sqlite.save_var('resume_index', i)
        raise
scraperwiki.sqlite.save_var('resume_index', 0)

ScraperWiki has a limit on CPU time, so an error that often concerns me is the scraperwiki.CPUTimeExceededError. This error is raised after the script has used 80 seconds of CPU time; if you catch the exception, you have two CPU seconds to clean up. You might want to handle this error differently from other errors.

import scraperwiki
resume_index = scraperwiki.sqlite.get_var('resume_index', 0)
for i, bar in enumerate(bars[resume_index:]):
    try:
        foo(bar)
    except scraperwiki.CPUTimeExceededError:
        scraperwiki.sqlite.save_var('resume_index', i)
    except Exception as e:
        scraperwiki.sqlite.save_var('resume_index', i)
        scraperwiki.sqlite.save([], {
            'bar': bar,
            'exceptionType': str(type(e)),
            'exceptionMessage': str(e)
        }, 'errors')
scraperwiki.sqlite.save_var('resume_index', 0)

tl;dr

Expect exceptions to occur when you are scraping a randomly unreliable website with randomly inconsistent content, and consider handling them in ways that allow the script to keep running when one document of interest is bizarrely formatted or not available.

Source: https://blog.scraperwiki.com/2012/05/handling-exceptions-in-scrapers/

Seven tools for web scraping – To use for data journalism & creating insightful content

I’ve been creating a lot of (data driven) creative content lately and one of the things I like to do is gathering as much data as I can from public sources. I even have some cases it is costing to much time to create and run database queries and my personal build PHP scraper is faster so I just wanted to share some tools that could be helpful. Just a short disclaimer: use these tools on your own risk! Scraping websites could generate high numbers of pageviews and with that, using bandwidth from the website you are scraping.

1. Scraper (Chrome plugin)

    Scraper is a simple data mining extension for Google Chrome™ that is useful for online research when you need to quickly analyze data in spreadsheet form.

You can select a specific data point, a price, a rating etc and then use your browser menu: click Scrape Similar and you will get multiple options to export or copy your data to Excel or Google Docs. This plugin is really basic but does the job it is build for: fast and easy screen scraping.

2. Simple PHP Scraper

PHP has a DOMXpath function. I’m not going to explain how this function works, but with the script below you can easily scrape a list of URLs. Since it is PHP, use a cronjob to hourly, daily or weekly scrape the desired data. If you are not used to creating Xpath references, use the Scraper for Chrome plugin by selecting the data point and see the Xpath reference directly.

scraper-example

– Click here to download the example script.

3. Kimono Labs
Kimono has two easy ways to scrape specific URLs: just paste the URL into their website or use their bookmark. Once you have pointed out the data you need, you can set how often and when you want the data to be collected. The data is saved in their database. I like the facts that their learning curve is not that steep and it doesn’t look like you need a PHD in engineering to use their software. The disadvantage of this tool is the fact you can’t upload multiple URLs at once.

4. Import.io
Import.io is a browser based web scraping tool. By following their easy step-by-step plan you select the data you want to scrape and the tool does the rest. It is a more sophisticated tool compared to Kimono. I like it because of the fact it shows a clear overview of all the scrapers you have active and you can scrape multiple URLs at once.

5. Outwit Hub

I will start with the two biggest differences compared to the previous tool: it is a softwarepackage to use on your PC or laptop and to use its full potential it will cost you 75 USD. The free version can only scrape 100 rows of data. What I do like is the number of preprogrammed options to scrape which makes it easy to start and learn about web scraping.

6. ScraperWiki

This tool is really for people wanting to scrape on a massive scale. You can code your own scrapers (in PHP, Ruby & Python) and pricing is really cheap looking to what you can get: 29USD / month for 100 datasets. You are completely free in using libraries and timers. And if your programming skills are not good enough, they can help you out (paid service though). Compared to other tools, this is the most advanced tool that offers the basics of web scraping.

7. Fminer.com

This tool made it possible to finally scrape all the data inside Google Webmaster Tools since it can deal with JavaScript and AJAX interfaces. Read my extensive review on this page: Scraping Webmaster Tools with FMiner!

But on the end, building your individual project scrapers will always be more effective than using predefined scrapers. Am I missing any tools in this sum up of tools?

Source: http://www.notprovided.eu/7-tools-web-scraping-use-data-journalism-creating-insightful-content/

Web scraping tutorial

There are three ways to access a website data. One is through a browser, the other is using a API (if the site provides one) and the last by parsing the web pages through code. The last one also known as Web Scraping is a technique of extracting information from websites using specially coded programs.

In this post we will take a quick look at writing a simple scraperusing the simplehtmldom library. But before we continue a word of caution:

Writing screen scrapers and spiders that consume large amounts of bandwidth, guess passwords, grab information from a site and use it somewhere else may well be a violation of someone’s rights and will eventually land you in trouble. Before writing  a screen scraper first see if the website offers an RSS feed or an API for the data you are looking. If not and you have to use a scraper, first check the websites policies regarding automated tools before proceeding.

Now that we have got all the legalities out of the way, lets start with the examples.

1. Installing simplehtmldom.

Simplehtmldom is a PHP library that facilitates the process of creating web scrapers. It is a HTML DOM parser written in PHP5 that let you manipulate HTML in a quick and easy way. It is a wonderful library that does away with the messy details of regular expressions and uses CSS selector style DOM access like those found in jQuery.

First download the library from sourceforge.  Unzip the library in you PHP includes directory or a directory where you will be testing the code.

Writing our first scraper.

Now that we are ready with the tools, lets write our first web scraper. For our initial idea let us see how to grab the sponsored links section from a google search page.

There are three ways to access a website data. One is through a browser, the other is using a API (if the site provides one) and the last by parsing the web pages through code. The last one also known as Web Scraping is a technique of extracting information from websites using specially coded programs.

In this post we will take a quick look at writing a simple scraperusing the simplehtmldom library. But before we continue a word of caution:

Writing screen scrapers and spiders that consume large amounts of bandwidth, guess passwords, grab information from a site and use it somewhere else may well be a violation of someone’s rights and will eventually land you in trouble. Before writing  a screen scraper first see if the website offers an RSS feed or an API for the data you are looking. If not and you have to use a scraper, first check the websites policies regarding automated tools before proceeding.

Source: http://www.codediesel.com/php/web-scraping-in-php-tutorial/

The Roots of Web Scraping and the Wisdom behind It

You may be wondering how data mining came into existence. This effective and innovative trend in business and research is indeed something commendable and the genius behind it is worth great reward. To have a clear view of the origin of web scraping, the following important factors that contribute to the creation of this phenomenon called data collection or web scraping are considered.

Foundations

Unlike any other innovation, no specific date can be clearly pointed out as the birthdate of data mining. It has come into existence as a result of several problem solving processes in major data gathering and handling situations. It appears that cyber technology has opened a Pandora box of “anything can happen” experiences. Moreover, the shift from physical to virtual data collection has resulted in a bulk of database that needed to be organized, analyzed and utilized.

Source: http://www.loginworks.com/blogs/web-scraping-blogs/roots-web-scraping-wisdom-behind/

Webscraping using readLines and RCurl

There is a massive amount of data available on the web. Some of it is in the form of precompiled, downloadable datasets which are easy to access. But the majority of online data exists as web content such as blogs, news stories and cooking recipes. With precompiled files, accessing the data is fairly straightforward; just download the file, unzip if necessary, and import into R. For “wild” data however, getting the data into an analyzeable format is more difficult. Accessing online data of this sort is sometimes reffered to as “webscraping”. Two R facilities, readLines() from the base package and getURL() from the RCurl package make this task possible.

readLines

For basic webscraping tasks the readLines() function will usually suffice. readLines() allows simple access to webpage source data on non-secure servers. In its simplest form, readLines() takes a single argument – the URL of the web page to be read:

web_page <- readLines("http://www.interestingwebsite.com")

As an example of a (somewhat) practical use of webscraping, imagine a scenario in which we wanted to know the 10 most frequent posters to the R-help listserve for January 2009. Because the listserve is on a secure site (e.g. it has https:// rather than http:// in the URL) we can't easily access the live version with readLines(). So for this example, I've posted a local copy of the list archives on the this site.

One note, by itself readLines() can only acquire the data. You'll need to use grep(), gsub() or equivalents to parse the data and keep what you need.

# Get the page's source
web_page <- readLines("http://www.programmingr.com/jan09rlist.html")
# Pull out the appropriate line
author_lines <- web_page[grep("<I>", web_page)]
# Delete unwanted characters in the lines we pulled out
authors <- gsub("<I>", "", author_lines, fixed = TRUE)
# Present only the ten most frequent posters
author_counts <- sort(table(authors), decreasing = TRUE)
author_counts[1:10]
[webscrape results]


We can see that Gabor Grothendieck was the most frequent poster to R-help in January 2009.

The RCurl package

To get more advanced http features such as POST capabilities and https access, you'll need to use the RCurl package. To do webscraping tasks with the RCurl package use the getURL() function. After the data has been acquired via getURL(), it needs to be restructured and parsed. The htmlTreeParse() function from the XML package is tailored for just this task. Using getURL() we can access a secure site so we can use the live site as an example this time.

# Install the RCurl package if necessary
install.packages("RCurl", dependencies = TRUE)
library("RCurl")
# Install the XML package if necessary
install.packages("XML", dependencies = TRUE)
library("XML")
# Get first quarter archives
jan09 <- getURL("https://stat.ethz.ch/pipermail/r-help/2009-January/date.html", ssl.verifypeer = FALSE)
jan09_parsed <- htmlTreeParse(jan09)
# Continue on similar to above
...

For basic webscraping tasks readLines() will be enough and avoids over complicating the task. For more difficult procedures or for tasks requiring other http features getURL() or other functions from the RCurl package may be required. For more information on cURL visit the project page here.

Source: http://www.r-bloggers.com/webscraping-using-readlines-and-rcurl-2/

Screen scrapers: To program or to purchase?

Companies today use screen scraping tools for a variety of purposes, including collecting competitive information, capturing product specs, moving data between legacy and new systems, and keeping inventory or price lists accurate.

Because of their popularity and reputation as being extremely efficient tools for quickly gathering applicable display data, screen scraping tools or browser add-ons are a dime a dozen: some free, some low cost, and some part of a larger solution. Alternatively, you can build your own if you are (or know) a programming whiz. Each tool has its potential pros and cons, however, to keep in mind as you determine which type of tool would best fit your business need.

Program-your-own screen scraper

Pros:

    Using in-house resources doesn't require additional budget

Cons:

    Properly creating scripts to automate screen scraping can take a significant amount of time initially, and continues to take time in order to maintain the process. If, for instance, objects from which you're gathering data move on a web page, the entire process will either need to be re-automated, or someone with programming acumen will have to edit the script every time there is a change.

    It's questionable whether or not this method actually saves time and resources

Free or cheap scrapers

Pros:

    Here again, budget doesn't ever enter the picture, and you can drive the process yourself.

    Some tools take care of at least some of the programming heavy lifting required to screen scrape effectively

Cons:

    Many inexpensive screen scrapers require that you get up to speed on their programming language—a time-consuming process that negates the idea of efficiency that prompted the purchase.

Screen scraping as part of a full automation solution

Pros:

    In the amount of time it takes to perform one data extraction task, you have a completely composed script that the system writes for you

    It's the easiest to use out of all of the options

    Screen scraping is only part of the package; you can leverage automation software to automate nearly any task or process including tasks in Windows, Excel automation, IT processes like uploads, backups, and integrations, and business processes like invoice processing.

    You're likely to get buy-in for other automation projects (and visibility for the efficiency you're introducing to the organization) if you pick a solution with a clear and scalable business purpose, not simply a tool to accomplish a single task.

Cons:

    This option has the highest price tag because of its comprehensive capabilities.

Looking for more information?

Here are some options to dig deeper into screen scraping, and deciding on the right tool for you:

 Watch a couple demos of what screen scraping looks like with an automation solution driving the process.

 Read our web data extraction guide for a complete overview.

 Try screen scraping today by downloading a free trial.

Source: https://www.automationanywhere.com/screen-scrapers

Data Mining Outsourcing in a Better and Unique Approach

Data mining outsourcing services are ideal for clarity in various decision making processes.  It is the ultimate goal of any organization and business to increase on its profits as well as strengthen the bond with its customers. Equipping the business in such a way that it’s very easy to detect frauds and manage risks in a convenient manner is equally important. Volumes of data that are irrelevant or cannot be used when raw needs to be converted to a more useful form.  The data mining outsourcing services can greatly help you to analyze and interpret data in a more diligent way.

This service to reliable, experienced and qualified hands is very important. Your research project or engineering project can be easily and conveniently handled by experienced staff who guarantees you an accuracy level of about 98% and a massive reduction in operating costs. The quality of work is unsurpassed and the presentation is done in a format that is easy and simple for you. The project is done in a very short time alleviating you delays as well as ensuring on-time completion of your projects. To enjoy a successful outsourcing experience, you need to bank on a famous and reliable expertise.

The only time to rely with data mining outsourcing services is when you do not have a reliable, experienced expertise in your business.  Statistics indicate that it’s very easy to lose business intelligence or expose the privacy of the customers through this process. However companies which offer secure outsourcing process are on the increase as a result of massive competition. It’s an opportunity to develop your potential of sourced data and improve your business in all fields. 

Data mining potential applications are infinite. However major applications are in the marketing research and scientific projects. It’s done both on large and small quantities of data by experienced staff well known for their best analytical procedures to guarantee you accurate and easy to use information. Data mining outsourcing services are the only perfect way to profitability.

Source:http://www.e-edge.biz/Data_Mining_Outsourcing_in_a_Better_and_Unique_Approach.html

Online Data Entry & Web Scraping Services

To operate any type of organization smoothly, it is essential to have precise data that is accurate and reliable. When your business expands, data entry on an ongoing basis is a tedious job. It’s a very time consuming task that can often distract employees focusing on core business areas.

Webpop offers all forms of online data entry services that are quick and accurate. We provide data entry services across all verticals that can be completely customized to your business requirements.

Database Population Services

Database population involves content collection from various database sources. This requires a lot of attention to detail, dedication and awareness and can prove a formidable task, especially for websites that largeley depend on it.

Webpop offer a quick and efficient database population service that helps relieve the stress from an extremely laborius task and leaves you more time to focus on more important aspects of your business. By investing just a fraction of the cost, you can outsource your database population tasks to us.

Web Scraping Services
Webpop have been assisting clients in searching, extracting and collecting data from the web for the past 5 years using the latest techniques in web scraping techology. We can scrape all types of information from a variety of sources such as websites, blogs, online directories, e-commerce websites and podcasts to name a few. We use a varied selection of automated and manual web scraping technologies to extract, gather and collect all of the required data you require from any chosen website(s) on the World Wide Web.

We can simplify the whole process from collection to population, converting your scraped data in to structured formats that are applicable to your website. This can be offered as a one time service or an ongoing basis that will assist you in constantly keeping your website’s content fresh and up to date. We can crawl competitors websites, gather sales leads, product details, pricing methodologies and also creat custom campaigns to suit your project’s requirements.

Over the years Webpop has grown from strength-to-strength by providing all types of data entry, database population and web scraping services. All of our data entry services are performed with care, due dilligence and attention to detail. We enjoy a challenge and pride ourselves on delivering results whilst working on precarious projects that require precision and total commitment.

Source:http://www.webpopdesign.com/services/data-entry/

Kimono Is A Smarter Web Scraper That Lets You “API-ify” The Web, No Code Required

A new Y Combinator-backed startup called Kimono wants to make it easier to access data from the unstructured web with a point-and-click tool that can extract information from webpages that don’t have an API available. And for non-developers, Kimono plans to eventually allow anyone track data without needing to understand APIs at all.

This sort of smarter “web scraper” idea has been tried before, and has always struggled to find more than a niche audience. Previous attempts with similar services like Dapper or Needlebase, for example, folded. Yahoo Pipes still chugs along, but it’s fair to say that the service has long since been a priority for its parent company.

But Kimono’s founders believe that the issue at hand is largely timing.

“Companies more and more are realizing there’s a lot of value in opening up some of their data sets via APIs to allow developers to build these ecosystems of interesting apps and visualizations that people will share and drive up awareness of the company,” says Kimono co-founder Pratap Ranade. (He also delves into this subject deeper in a Forbes piece here). But often, companies don’t know how to begin in terms of what data to open up, or how. Kimono could inform them.

Plus, adds Ranade, Kimono is materially different from earlier efforts like Dapper or Needlebase, because it’s outputting to APIs and is starting off by focusing on the developer user base, with an expansion to non-technical users planned for the future. (Meanwhile, older competitors were often the other way around).

The company itself is only a month old, and was built by former Columbia grad school companions Ranade and Ryan Rowe. Both left grad school to work elsewhere, with Rowe off to Frog Design and Ranade at McKinsey. But over the nearly half-dozen or so years they continued their careers paths separately, the two stayed in touch and worked on various small projects together.

One of those was Airpapa.com, a website that told you which movies were showing on your flights. This ended up giving them the idea for Kimono, as it turned out. To get the data they needed for the site, they had to scrape data from several publicly available websites.

“The whole process of cleaning that [data] up, extracting it on a schedule…it was kind of a painful process,” explains Rowe. “We spent most of our time doing that, and very little time building the website itself,” he says. At the same time, while Rowe was at Frog, he realized that the company had a lot of non-technical designers who needed access to data to make interesting design decisions, but who weren’t equipped to go out and get the data for themselves.

With Kimono, the end goal is to simplify data extraction so that anyone can manage it. After signing up, you install a bookmarklet in your browser, which, when clicked, puts the website into a special state that allows you to point to the items you want to track. For example, if you were trying to track movie times, you might click on the movie titles and showtimes. Then Kimono’s learning algorithm will build a data model involving the items you’ve selected.

Screen Shot 2014-02-18 at 4.29.05 PM

Screen Shot 2014-02-18 at 4.29.27 PM

That data can be tracked in real time and extracted in a variety of ways, including to Excel as a .CSV file, to RSS in the form of email alerts, or for developers as a RESTful API that returns JSON. Kimono also offers “Kimonoblocks,” which lets you drop the data as an embed on a webpage, and it offers a simple mobile app builder, which lets you turn the data into a mobile web application.

Screen Shot 2014-02-18 at 4.29.50 PM

For developer users, the company is currently working on an API editor, which would allow you to combine multiple APIs into one.

So far, the team says, they’ve been “very pleasantly surprised” by the number of sign-ups, which have reached ten thousand*. And even though only a month old, they’ve seen active users in the thousands.

Initially, they’ve found traction with hardware hackers who have done fun things like making an airhorn blow every time someone funds their Kickstarter campaign, for instance, as well as with those who have used Kimono for visualization purposes, or monitoring the exchange rates of various cryptocurrencies like Bitcoin and dogecoin. Others still are monitoring data that’s later spit back out as a Twitter bot.

Kimono APIs are now making over 100,000 calls every week, and usage is growing by over 50 percent per week. The company also put out an unofficial “Sochi Olympics API” to showcase what the platform can do.

The current business model is freemium based, with pricing that kicks in for higher-frequency usage at scale.

The Mountain View-based company is a team of just the two founders for now, and has initial investment from YC, YC VC and SV Angel.

Source:http://techcrunch.com/2014/02/18/kimono-is-a-smarter-web-scraper-that-lets-you-api-ify-the-web-no-code-required/

A Web Scraper’s Guide to Kimono

Being a frequent reader of Hacker News, I noticed an item on the front page earlier this year which read, “Kimono – Never write a web scraper again.” Although it got a great number of upvotes, the tech junta was quick to note issues, especially if you are a developer who knows how to write scrapers. The biggest concern was a non-intuitive UX, followed by the inability of the first beta version to extract data items from websites as smoothly as the demo video suggested.

I decided to give it a few months before I tested it out, and I finally got the chance to do so recently.

Kimono is a Y-Combinator backed startup trying to do something in a field where others have failed. Kimono is focused on creating APIs for websites which don’t have one, another term would be web scraping. Imagine you have a website which shows some data you would like to dynamically process in your website or application. If the website doesn’t have an API, you can create one using Kimono by extracting the data items from the website.

Is it Legal?

Kimono provides an FAQ section, which says that web scraping from public websites “is 100% legal” as long as you check the robots.txt file to see which URL patterns they have disallowed. However, I would advise you to proceed with caution because some websites can pose a problem.

A robots.txt is a file that gives directions to crawlers (usually of search engines) visiting the website. If a webmaster wants a page to be available on search engines like Google, he would not disallow robots in the robots.txt file. If they’d prefer no one scrapes their content, they’d specifically mention it in their Terms of Service. You should always look at the terms before creating an API through Kimono.

An example of this is Medium. Their robots.txt file doesn’t mention anything about their public posts, but the following quote from their TOS page shows you shouldn’t scrape them (since it involves extracting data from their HTML/CSS).

    For the remainder of the site, you may not duplicate, copy, or reuse any portion of the HTML/CSS, JavaScipt, logos, or visual design elements without express written permission from Medium unless otherwise permitted by law.

If you check the #BuiltWithKimono section of their website, you’d notice a few straightforward applications. For instance, there is a price comparison API, which is built by extracting the prices from product pages on different websites.

Let us move on and see how we can use this service.

What are we about to do?

Let’s try to accomplish a task, while exploring Kimono. The Blog Bowl is a blog directory where you can share and discover blogs. The posts that have been shared by users are available on the feeds page. Let us try to get a list of blog posts from the page.

The simple thought process when scraping the data is parsing the HTML (or searching through it, in simpler terms) and extracting the information we require. In this case, let’s try to get the title of the post, its link, and the blogger’s name and profile page.

Source: http://www.sitepoint.com/web-scrapers-guide-kimono/

Scraping Data: Site-specific Extractors vs. Generic Extractors

Scraping is becoming a rather mundane job with every other organization getting its feet wet with it for their own data gathering needs. There have been enough number of crawlers built – some open-sourced and others internal to organizations for in-house utilities. Although crawling might seem like a simple technique at the onset, doing this at a large-scale is the real deal. You need to have a distributed stack set up to take care of handling huge volumes of data, to provide data in a low-latency model and also to deal with fail-overs. This still is achievable after crossing the initial tech barrier and via continuous optimizations. (P.S. Not under-estimating this part because it still needs a team of Engineers monitoring the stats and scratching their heads at times).

Social Media Scraping

Focused crawls on a predefined list of sites

However, you bump into a completely new land if your goal is to generate clean and usable data sets from these crawls i.e. “extract” data in a format that your DB can process and aid in generating insights. There are 2 ways of tackling this:

a. site-specific extractors which give desired results

b. generic extractors that result in few surprises

Assuming you still do focused crawls on a predefined list of sites, let’s go over specific scenarios when you have to pick between the two-

1. Mass-scale crawls; high-level meta data - Use generic extractors when you have a large-scale crawling requirement on a continuous basis. Large-scale would mean having to crawl sites in the range of hundreds of thousands. Since the web is a jungle and no two sites share the same template, it would be impossible to write an extractor for each. However, you have to settle in with just the document-level information from such crawls like the URL, meta keywords, blog or news titles, author, date and article content which is still enough information to be happy with if your requirement is analyzing sentiment of the data.

cb1c0_one-size

A generic extractor case

Generic extractors don’t yield accurate results and often mess up the datasets deeming it unusable. Reason being

programatically distinguishing relevant data from irrelevant datasets is a challenge. For example, how would the extractor know to skip pages that have a list of blogs and only extract the ones with the complete article. Or delineating article content from the title on a blog page is not easy either.

To summarize, below is what to expect of a generic extractor.

Pros-

minimal manual intervention

low on effort and time

can work on any scale

Cons-

Data quality compromised

inaccurate and incomplete datasets

lesser details suited only for high-level analyses

Suited for gathering- blogs, forums, news

Uses- Sentiment Analysis, Brand Monitoring, Competitor Analysis, Social Media Monitoring.

2. Low/Mid scale crawls; detailed datasets - If precise extraction is the mandate, there’s no going away from site-specific extractors. But realistically this is do-able only if your scope of work is limited i.e. few hundred sites or less. Using site-specific extractors, you could extract as many number of fields from any nook or corner of the web pages. Most of the times, most pages on a website share similar templates. If not, they can still be accommodated for using site-specific extractors.

cutlery

Designing extractor for each website

Pros-

High data quality

Better data coverage on the site

Cons-

High on effort and time

Site structures keep changing from time to time and maintaining these requires a lot of monitoring and manual intervention

Only for limited scale

Suited for gathering - any data from any domain on any site be it product specifications and price details, reviews, blogs, forums, directories, ticket inventories, etc.

Uses- Data Analytics for E-commerce, Business Intelligence, Market Research, Sentiment Analysis

Conclusion

Quite obviously you need both such extractors handy to take care of various use cases. The only way generic extractors can work for detailed datasets is if everyone employs standard data formats on the web (Read our post on standard data formats here). However, given the internet penetration to the masses and the variety of things folks like to do on the web, this is being overly futuristic.

So while site-specific extractors are going to be around for quite some time, the challenge now is to tweak the generic ones to work better. At PromptCloud, we have added ML components to make them smarter and they have been working well for us so far.

What have your challenges been? Do drop in your comments.

Source: https://www.promptcloud.com/blog/scraping-data-site-specific-extractors-vs-generic-extractors/

3 Reasons to Up Your Web Scraping Game

If you aren’t using a machine-learning-driven intelligent Web scraping solution yet, here are three reasons why you might want to abandon that entry-level Web-scraping software or cut your high-cost script-writing approach.

    You need to keep an eye on a large number of web sources that get updated frequently.
    Understanding what’s changed is at least as critical as the data itself.
    You don’t want maintenance and scheduling to drag you down.

Here’s what an intelligent Web-scraping solution can deliver – and why:

1. Better data monitoring of an ever-shifting Web

If you need to keep a watch over hundreds, thousands or even tens of thousands of sites, an intelligent Web scraper is a must, because:

    It can scale – easily adding new websites, coordinating extraction routines, and automating the normalization of data across different websites.

    It can navigate and extract data from websites efficiently. Script-based approaches typically only can view a Web page in isolation, making it difficult to optimize navigation across unique pages of a targeted site. More intelligent approaches can be trained to bypass unnecessary links and leave a lighter footprint on the sites you need to access. And, they can monitor millions of precise Web data points quickly. This means you can monitor more pages on more sites with more frequent updates.

2. Critical alerts to Web data changes

A key sales executive suddenly drops off of the management page of your main competitor. That can mean big shakeup in the entire organization, which your sales team can jump on.

An intelligent Web scraper can alert you to this personnel shift because it can be set to monitor for just the changes; less powerful technologies or script-based approaches can’t. Whether you’re tracking price shifts, people moves, or product changes (or more) intelligent Web scraping delivers more profound insights.

3. Maintenance may become your biggest nightmare

You’ve purchased an entry-level tool and built out scrapers for a few hundred sites.  At first, everything seems fine. But, within weeks you begin to notice that your data is incomplete and not being updated as you’d expected. Why did your data deliveries disappear?

Reality is that these low-cost tools are simply not designed for mission-critical business applications – on the surface they look helpful and easy to use, but underneath the surface they are script-based and highly dependent upon the HTML of a website. But websites change, and entry-level web scraping tools are simply not engineered to adapt to those changes.

And, most of these tools are simply not designed for enterprise use. They have limited reporting, if any, so the only way to know whether they’re successfully completing their tasks is by finding gaps in the data – often when it’s too late.

An intelligent web scraping approach doesn’t rely upon the HTML of a web page. It uses machine learning algorithms which view the web the same way a user might. A typical reader doesn’t get confused when a font or color is changed on a website, and neither do these algorithms. But simple approaches to web scraping are highly dependent on the specific HTML to help it understand the content of a page. So, when websites have design changes (on average once every 18 months), the software fails.

While entry-level web scraping software can be an easy solution for simple, one-time web scraping projects, the scripts they generate are fragile and the resources required for tracking and maintenance can become overwhelming when you need to regularly extract data from multiple sites.

Case in point: Shopzilla assimilates data five times faster than outsourced Web scrapers

To demonstrate the power of intelligent Web scraping, here’s a real-life example from Shopzilla.  Shopzilla manages a premier portfolio of online shopping brands in the United States and Europe, connecting more than 40 million shoppers each month with millions of products from retailers worldwide. With the explosive growth of retail data on the Web, Shopzilla’s outsourced, custom-built approach, based on scripting, could not add the product lines of new retailers to its site in a timely fashion. It was taking up to two weeks to write the scripts needed to make a single site accessible.

By deploying Connotate’s intelligent web scraping platform on site, Shopzilla gained the ability to harness Web data’s rapid growth and keep up to date. Today, new sources are added in days, not weeks.  The platform continually monitors Web content from thousands of sites, delivering high volumes of data every day in a structured format. The result: 500 percent more data from new retailers. An added bonus: the company has reduced IT maintenance costs and its dependence on outsourced development timetables. Case in point: Deep competitor intelligence in two languages

A global manufacturer needed to monitor competitors’ technology improvements in a field where market leadership hinges on an ability to quickly leverage these advances. That meant accessing scholarly journals and niche sites in multiple languages. Using the Connotate solution, it was able to access highly-targeted, keyword-driven university and industry research journals and blogs in German and English that are hard to reach because they do not support RSS feeds. Our solution also incorporated semantic analysis to tag and categorize data and help identify new technologies and products not currently in the keyword list. The firm enhanced its competitive edge with the up-to-the-minute, precise data it needed.

Is your Web scraping intelligent enough?

See what intelligent agents through an automated Web data extraction and monitoring solution can bring to your business. Contact us and speak with one of experts.

Source:http://www.connotate.com/3-reasons-web-scraping-game-6579#.VGMjH2f4EuQ

Web Scraping Enters Politics

Web scraping is becoming an essential tool in gaining an edge over everything about just anything. This is proven by international news on US political campaigns, specifically by identifying wealthy donors. As is commonly known, election campaigns should follow a rule regarding the use of a certain limited amount of money for the expenses of each candidate. Being so, much of the campaign activities must be paid by supporters and sponsors.

It is not a surprise then that even politics is lured to make use of the dynamic and ever growing data mining processes. Once again, web mining has proven to be an essential component of almost all levels of human existence, the society, and the world as a whole. It proves its extraordinary capacity to dig precious information to reach the much aspired for goals of every individual.

Mining for personal information

The CBC News online very recently disclosed that the US Republican presidential candidate Mitt Romney has used data mining in order to identify rich donors. It is reported that the act of getting personal information such as the buying history and church attendance were vital in this incident. Through this information, the party was able to identify prospective rich donors and indeed tap them. As a businessman himself, Romney knows exactly how to fish and where the fat fish are. Moreover, what is unique about the identified donors is that they have never been donating before.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/web-scraping-enters-politics/

Web Scraping Popularity Soars

The world is stirred because of the ever-growing web scraping success in almost all of its services. Success stories pertaining to the benefits of online data collection in business, research, politics, health, and almost all aspects of human life are endless. With this popularity surge, it has become a hot issue and many are questioning its legality and reliability.

Looking back, this simple harvesting of pertinent data from competitors and the global market in general like anything else started as a non-threatening and advanced form of web research. Eventually, when the benefits begin to manifest and the system improves, many are lured into it that it has become one of the strongest and fastest growing business in the world.

Simple Beginnings

As naturally as a law of life that great things come from small beginnings, data mining was conceived as a process in gaining information, mostly in research. This act of collecting information through the internet was never imagined to be what it has become nowadays.

Source:http://www.loginworks.com/blogs/web-scraping-blogs/web-scraping-popularity-soars/

Scraping webdata from a website that loads data in a streaming fashion

I'm trying to scrape some data off of the FEC.gov website using python for a project of mine. Normally I use python

mechanize and beautifulsoup to do the scraping.

I've been able to figure out most of the issues but can't seem to get around a problem. It seems like the data is

streamed into the table and mechanize.Browser() just stops listening.

So here's the issue: If you visit http://query.nictusa.com/cgi-bin/can_ind/2011_P80003338/1/A ... you get the first 500

contributors whose last name starts with A and have given money to candidate P80003338 ... however, if you use

browser.open() at that url all you get is the first ~5 rows.

I'm guessing its because mechanize isn't letting the page fully load before the .read() is executed. I tried putting a

time.sleep(10) between the .open() and .read() but that didn't make much difference.

And I checked, there's no javascript or AJAX in the website (or at least none are visible when you use the 'view-

source'). SO I don't think its a javascript issue.

Any thoughts or suggestions? I could use selenium or something similar but that's something that I'm trying to avoid.

-Will

2 Answers

Why not use an html parser like lxml with xpath expressions.

I tried

>>> import lxml.html as lh
>>> data = lh.parse('http://query.nictusa.com/cgi-bin/can_ind/2011_P80003338/1/A')
>>> name = data.xpath('/html/body/table[2]/tr[5]/td[1]/a/text()')
>>> name
[' AABY, TRYGVE']
>>> name = data.xpath('//table[2]/*/td[1]/a/text()')
>>> len(name)
500
>>> name[499]
' AHMED, ASHFAQ'
>>>



Similarly, you can create xpath expression of your choice to work with.


Source: http://stackoverflow.com/questions/9435512/scraping-webdata-from-a-website-that-loads-data-in-a-streaming-

fashion

How can I circumvent page view limits when scraping web data using Python?

I am using Python to scrape US postal code population data from http:/www.city-data.com, through this directory: http://www.city-data.com/zipDir.html. The specific pages I am trying to scrape are individual postal code pages with URLs like this: http://www.city-data.com/zips/01001.html. All of the individual zip code pages I need to access have this same URL Format, so my script simply does the following for postal_code in range:

    Creates URL given postal code
    Tries to get response from URL
    If (2), Check the HTTP of that URL
    If HTTP is 200, retrieves the HTML and scrapes the data into a list
    If HTTP is not 200, pass and count error (not a valid postal code/URL)
    If no response from URL because of error, pass that postal code and count error
    At end of script, print counter variables and timestamp

The problem is that I run the script and it works fine for ~500 postal codes, then suddenly stops working and returns repeated timeout errors. My suspicion is that the site's server is limiting the page views coming from my IP address, preventing me from completing the amount of scraping that I need to do (all 100,000 potential postal codes).

My question is as follows: Is there a way to confuse the site's server, for example using a proxy of some kind, so that it will not limit my page views and I can scrape all of the data I need?

Thanks for the help! Here is the code:

##POSTAL CODE POPULATION SCRAPER##

import requests

import re

import datetime

def zip_population_scrape():

    """
    This script will scrape population data for postal codes in range
    from city-data.com.
    """
    postal_code_data = [['zip','population']] #list for storing scraped data

    #Counters for keeping track:
    total_scraped = 0
    total_invalid = 0
    errors = 0


    for postal_code in range(1001,5000):

        #This if statement is necessary because the postal code can't start
        #with 0 in order for the for statement to interate successfully
        if postal_code <10000:
            postal_code_string = str(0)+str(postal_code)
        else:
            postal_code_string = str(postal_code)

        #all postal code URLs have the same format on this site
        url = 'http://www.city-data.com/zips/' + postal_code_string + '.html'

        #try to get current URL
        try:
            response = requests.get(url, timeout = 5)
            http = response.status_code

            #print current for logging purposes
            print url +" - HTTP:  " + str(http)

            #if valid webpage:
            if http == 200:

                #save html as text
                html = response.text

                #extra print statement for status updates
                print "HTML ready"

                #try to find two substrings in HTML text
                #add the substring in between them to list w/ postal code
                try:           

                    found = re.search('population in 2011:</b> (.*)<br>', html).group(1)

                    #add to # scraped counter
                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])

                    #print statement for logging
                    print postal_code_string + ": " + str(found) + ". Data scrape successful. " + str(total_scraped) + " total zips scraped."
                #if substrings not found, try searching for others
                #and doing the same as above   
                except AttributeError:
                    found = re.search('population in 2010:</b> (.*)<br>', html).group(1)

                    total_scraped +=1

                    postal_code_data.append([postal_code_string,found])
                    print postal_code_string + ": " + str(found) + ". Data scrape successful. " + str(total_scraped) + " total zips scraped."

            #if http =404, zip is not valid. Add to counter and print log        
            elif http == 404:
                total_invalid +=1

                print postal_code_string + ": Not a valid zip code. " + str(total_invalid) + " total invalid zips."

            #other http codes: add to error counter and print log
            else:
                errors +=1

                print postal_code_string + ": HTTP Code Error. " + str(errors) + " total errors."

        #if get url fails by connnection error, add to error count & pass
        except requests.exceptions.ConnectionError:
            errors +=1
            print postal_code_string + ": Connection Error. " + str(errors) + " total errors."
            pass

        #if get url fails by timeout error, add to error count & pass
        except requests.exceptions.Timeout:
            errors +=1
            print postal_code_string + ": Timeout Error. " + str(errors) + " total errors."
            pass


    #print final log/counter data, along with timestamp finished
    now= datetime.datetime.now()
    print now.strftime("%Y-%m-%d %H:%M")
    print str(total_scraped) + " total zips scraped."
    print str(total_invalid) + " total unavailable zips."
    print str(errors) + " total errors."



Source: http://stackoverflow.com/questions/25452798/how-can-i-circumvent-page-view-limits-when-scraping-web-data-using-python

Web data scraping (online news comments) with Scrapy (Python)

Since you seem like the try-first ask-question later type (that's a very good thing), I won't give you an answer, but a (very detailed) guide on how to find the answer.

The thing is, unless you are a yahoo developer, you probably don't have access to the source code you're trying to scrape. That is to say, you don't know exactly how the site is built and how your requests to it as a user are being processed on the server-side. You can, however, investigate the client-side and try to emulate it. I like using Chrome Developer Tools for this, but you can use others such as FF firebug.

So first off we need to figure out what's going on. So the way it works, is you click on the 'show comments' it loads the first ten, then you need to keep clicking for the next ten comments each time. Notice, however, that all this clicking isn't taking you to a different link, but lively fetches the comments, which is a very neat UI but for our case requires a bit more work. I can tell two things right away:

    They're using javascript to load the comments (because I'm staying on the same page).
    They load them dynamically with AJAX calls each time you click (meaning instead of loading the comments with the page and just showing them to you, with each click it does another request to the database).

Now let's right-click and inspect element on that button. It's actually just a simple span with text:

<span>View Comments (2077)</span>

By looking at that we still don't know how that's generated or what it does when clicked. Fine. Now, keeping the devtools window open, let's click on it. This opened up the first ten. But in fact, a request was being made for us to fetch them. A request that chrome devtools recorded. We look in the network tab of the devtools and see a lot of confusing data. Wait, here's one that makes sense:

http://news.yahoo.com/_xhr/contentcomments/get_comments/?content_id=42f7f6e0-7bae-33d3-aa1d-3dfc7fb5cdfc&_device=full&count=10&sortBy=highestRated&isNext=true&offset=20&pageNumber=2&_media.modules.content_comments.switches._enable_view_others=1&_media.modules.content_comments.switches._enable_mutecommenter=1&enable_collapsed_comment=1

See? _xhr and then get_comments. That makes a lot of sense. Going to that link in the browser gave me a JSON object (looks like a python dictionary) containing all the ten comments which that request fetched. Now that's the request you need to emulate, because that's the one that gives you what you want. First let's translate this to some normal reqest that a human can read:

go to this url: http://news.yahoo.com/_xhr/contentcomments/get_comments/
include these parameters: {'_device': 'full',
          '_media.modules.content_comments.switches._enable_mutecommenter': '1',
          '_media.modules.content_comments.switches._enable_view_others': '1',
          'content_id': '42f7f6e0-7bae-33d3-aa1d-3dfc7fb5cdfc',
          'count': '10',
          'enable_collapsed_comment': '1',
          'isNext': 'true',
          'offset': '20',
          'pageNumber': '2',
          'sortBy': 'highestRated'}

Now it's just a matter of trial-and-error. However, a few things to note here:

    Obviously the count is what decides how many comments you're getting. I tried changing it to 100 to see what happens and got a bad request. And it was nice enough to tell me why - "Offset should be multiple of total rows". So now we understand how to use offset

    The content_id is probably something that identifies the article you are reading. Meaning you need to fetch that from the original page somehow. Try digging around a little, you'll find it.

    Also, you obviously don't want to fetch 10 comments at a time, so it's probably a good idea to find a way to fetch the number of total comments somehow (either find out how the page gets it, or just fetch it from within the article itself)

    Using the devtools you have access to all client-side scripts. So by digging you can find that that link to /get_comments/ is kept within a javascript object named YUI. You can then try to understand how it is making the request, and try to emulate that (though you can probably figure it out yourself)

    You might need to overcome some security measures. For example, you might need a session-key from the original article before you can access the comments. This is used to prevent direct access to some parts of the sites. I won't trouble you with the details, because it doesn't seem like a problem in this case, but you do need to be aware of it in case it shows up.

    Finally, you'll have to parse the JSON object (python has excellent built-in tools for that) and then parse the html comments you are getting (for which you might want to check out BeautifulSoup).

As you can see, this will require some work, but despite all I've written, it's not an extremely complicated task either.

So don't panic.

It's just a matter of digging and digging until you find gold (also, having some basic WEB knowledge doesn't hurt). Then, if you face a roadblock and really can't go any further, come back here to SO, and ask again. Someone will help you.


Source: http://stackoverflow.com/questions/20218855/web-data-scraping-online-news-comments-with-scrapy-python

A good web data extraction/screen scraper program?

I need to capture product data from a site on a regular basis and wondered if any one knows of a good software program? I've trialed Mozenda but its a monthly subscription and pricey in the long term. Obviously something thats free would be best but I don't mind paying either. Just need a decent program thats reliable and doesn't require much programming knowledge.

You can try ScraperWiki.com if you know python.

I've experimented with Screen-Scraper and found it easy to use. The application comes in multiple versions: basic (which is free), professional, and enterprise. Also, multiple platforms are supported.

Hire a programmer to do it so that there is only a one off cost. I often see similar projects on freelancing websites like Elance and oDesk.

I really like iMacros. You can give it a test drive to see if it meets your needs with the totally free Firefox extension (there's also IE versions), but there are also more full featured application and "server" versions that have more features and ability to do thing in an unattended manner.

Here are some other alternatives to consider:

    License the data from the provider. Call em up and ask 'em.

    Use Amazon Mechanical Turk to get humans to copy and paste and format it for ya. They are cheap.

    For automation, it depends on how complicated the HTML is and how often it changes. You could use Excel's Web Data Import if it's really simple.


You can use irobot from IRobotSoft, which is totally free, and provides more functionalityies than other paid software. Watch demos here http://irobotsoft.com/help/ for how simple it is.

Questions on their forum were answered very quickly.


Source: http://stackoverflow.com/questions/2334164/a-good-web-data-extraction-screen-scraper-program

How to login to website and extract data using PHP [closed]

I have installed the tiny tiny rss on to my computer (Windows) and also have Xampp installed (localhost).

I want to be able to use PHP to extract data from the Tiny tiny RSS webpage.

I have tried this it which just opens the front page:

<?php
$homepage = file_get_contents('my install tiny tiny rss url');
echo $homepage;
?>

But how do I login and extract the data.

You can use cURL to send post data and headers. To login you need to replicate the exact data exchange between the client and the server.


SOurce: http://stackoverflow.com/questions/20611918/how-to-login-to-website-and-extract-data-using-php

Is it ok to scrape data from Google results?

I'd like to fetch results from Google using curl to detect potential duplicate content. Is there a high risk of being banned by Google?

Google will eventually block your IP when you exceed a certain amount of requests.



Google disallows automated access in their TOS, so if you accept their terms you would break them.

That said, I know of no lawsuit from Google against a scraper. Even Microsoft scraped Google, they powered their search engine Bing with it. They got caught in 2011 red handed :)

There are two options to scrape Google results:

1) Use their API

    You can issue around 40 requests per hour You are limited to what they give you, it's not really useful if you want to track ranking positions or what a real user would see. That's something you are not allowed to gather.

    If you want a higher amount of API requests you need to pay.
    60 requests per hour cost 2000 USD per year, more queries require a custom deal.

2) Scrape the normal result pages

    Here comes the tricky part. It is possible to scrape the normal result pages. Google does not allow it.
    If you scrape at a rate higher than 15 keyword requests per hour you risk detection, higher than 20/h will get you blocked from my experience.
    By using multiple IPs you can up the rate, so with 100 IP addresses you can scrape up to 2000 requests per hour. (50k a day)
    There is an open source search engine scraper written in PHP at http://scraping.compunect.com It allows to reliable scrape Google, parses the results properly and manages IP addresses, delays, etc. So if you can use PHP it's a nice kickstart, otherwise the code will still be useful to learn how it is done.


Source: http://stackoverflow.com/questions/22657548/is-it-ok-to-scrape-data-from-google-results

Data Scraping from PDF and Excel

I am doing a little data scraping, There are 3 types of file from which i am scraping data.

1- HTML
2- PDF
3- Excel(xls)

For HTML i am comfortable, i am using HTML Agility for that.

For PDF and excel i need suggestions from anyone.



Concerning Excel. If you are in a MS environment you can either do Office Automation or use OLEDB. In a Java environment look at Apache POI.

EDIT: Concerning PDF in Java try Apache PDFBox . Can also work in .NET using IKVM

I can recommend Cogniview's PDF2XL, a reasonably inexpensive commercial product, to extract data from tables in PDF files into Excel. We have used it with great success.

HTML Agility is a library. Its good to use. But then, why do you need separate tools for different data extraction purposes? Use Automation Anywhere to extract data from any source. As far as I know, it would work for all the three sources you have specified. Google it.

Source: http://stackoverflow.com/questions/3147803/data-scraping-from-pdf-and-excel

Excel VBA Data Mining Real-Time Data from a Web Page that Refreshes Data

I want to capture real-time data that updates into a table on a webpage; I prefer capturing it into excel using VBA, but I will write it in .NET C# or VB if I that is easier.

the data updates about 1 or 2 seconds, and I want to just grab the latest data quotes and log it into my spreadsheet; the table names are the same, only the data refreshes, and it does so automatically on the web page.

I've done a lot of Excel VBA and I know how to download a URL to a file--this is NOT what I want; I want to gain access to my webpage that is active and grab the data updates after I've logged into my site and selected a webpage that I like.

Is there a simple way to access this data on the webpage from Excel or .Net? Because it refreshes no more than once every 1 or 2 seconds, it is easy to just keep checking it for updates, and I can compare the latest data to see if it actually refreshed.


In Excel 2003, use Data/Import External Data/New Web Query
Browse to your page and select the table you want to import.
After that you can either do a manual Refresh, or use a timer procedure to do something like:

Source: http://stackoverflow.com/questions/9855794/excel-vba-data-mining-real-time-data-from-a-web-page-that-refreshes-data

Need to pull data from a website…web query? macro?

I have a list of every DOT # (Dept. of Trans.) in the country. I want to find out insurance effective date for each one of these companies. If you go to http://li-public.fmcsa.dot.gov --> "continue" --> then from the dropdown select "carrier search" and hit "go" it'll take you to a search form (that is the only way to get to this screen).

From there, you can input a DOT # X (use 61222 as an example) and it'll bring you to another screen. Click "view report in HTML" and then down on the bottom you'll see "Active/Pending Insurance". I want to pull the "effective date" from that page and stick it in the spreadsheet next to the DOT # X that I already know.

Of the thousands of DOT #'s in my list, not all will have filings on this website, if that makes a difference.

Can this be done with a Macro or Excel Web Query? I know I probably sound like a total novice, but I'd appreciate any help I could get.

Can you do it? Frankly even if you could you'd lock up the spreadsheet while it's doing that processing. And in the end, how would you handle an error half-way through?

I'd not do this in a client-facing application. This sounds more like something to do in server-side app that can do the processing and gather the information in a more controlled environment. Then you Excel spreadsheet could query that app and get the information in one fell swoop. Error handling is much simpler and you don't end up sitting there staring at Excel why it works its way through thousands of web sites. It was not built to do that elegantly.

What do you write the web service I'm describing in? Well it depends on your preference. Me, I'd write it in Ruby on Rails since it can easily handle the scraping aspect of the task and can report the data out easily as well. But it really falls back to whatever you're most comfortable coding in.


Source: http://stackoverflow.com/questions/15286429/need-to-pull-data-from-a-website-web-query-macro

How to extract data from web 2.0 graphs using a scraper

I have recently come across a web page containing a graph object that displays the (x, y) values on the object as the mouse is rolled across it. Is there any way to automate the extraction of this data?

How is the graph data loaded? If embedded in the page source then you can extract it with xpath or regex. Else use Firebug to see how it is loaded.

You will need a solution that works inside the web browser, so the AJAX/Javascript is properly rendered.

I have used iMacros with good success for web scraping in the past. There are free/open-source and "PRO" paid editions (comparison table here).

Another option is always to custom code something with the Microsoft webbrowser control.


Source: http://stackoverflow.com/questions/3980774/how-to-extract-data-from-web-2-0-graphs-using-a-scraper

Legality of Web Scraping vs Normal Use

I know the topic of web scraping has been discussed before (example), and I understand it's a bit of a grey area

depending on a lot of factors (e.g. website's terms of use).

What I'd like to ask is: how is web scraping any different from (a) how we access the webpage via a web browser, and

(b) how web crawlers (e.g. Google) download and index webpages?

Without knowing the legal background, I can't help but think that they're all just HTTP requests. If web scraping is

illegal, then so should crawling and indexing (for instance be illegal).

Of course if your program is hitting the server so hard that it causes a denial of service, it's a different story

altogether... my point is simply accessing and using data that is already open to the public.



I know this is a dead thread, but it would be nice to place some legal implications here due to its ranking in my

Google Search. I cannot help but figure I am not the only one who searches like I do.

Legally, in the US, there are a few factors that seem to be important.

    Are you doing anything that is akin to hacking or gaining unauthorized access via the Computer Fraud and Abuse Act.

Exploiting vulnerabilities and passing SQL in the URL to open a database no matter how bad the idiot programming like

that was is illegal with a 15 year sentence (see the cases where an individual exploited security vulnerabilities in

Verizon). Also, add a time out even if you round robin or use proxies. DDoS attacks are attacks. 1000 requests per

second can shut down a lot of servers providing public information. The result here is up to 15 years in jail.

    Copyright Law: As mentioned, pure replication of data is illegal. Even 4% replication has been deemed a breach.

With the recent gutting of the DMCA, a person is even more vulnerable to civil and criminal penalties.

    Trespass and Chattels: The following from wikipedia says it all.

    U.S. courts have acknowledged that users of "scrapers" or "robots" may be held liable for committing trespass to

chattels,[5][6] which involves a computer system itself being considered personal property upon which the user of a

scraper is trespassing. The best known of these cases, eBay v. Bidder's Edge, resulted in an injunction ordering

Bidder's Edge to stop accessing, collecting, and indexing auctions from the eBay web site.

    Paywalls and Product: When going behind paywalls and breaching contract by clicking an agreement not to do

something and then doing it, you add fuel to the protection of negligence v. willingness [an issue for damages and

penalties not guilt] in civil and any criminal trials. (sorry originally wanted to say ignorance but it really isn't a

defense)

    International: EU law and other law is way more lax. Corporations with big budgets dominate our legal landscape.

They control the system in a very real way with their $$$.

Basically, get public information and information that is available without going behind a pay wall. Think like a user

of the internet and combine a bunch of sources into a unique product. Don't just 'steal' an entire site (it isn't

really stealing if it is a government site that offers public data especially for download but is if you download all

or even more than a couple of the listings on ebay). Read the terms and conditions to know who actually owns the

content.

Here are a few examples. Trulia owns its information but you could use it to go to an agents website and collect a

legal amount of information. The legal amount is determinable. However, a public MLS listing lookup site with no

agreement or terms and offering data to the public is fair game. The MLS numbers lists, however, are normally not fair

game.

If a researcher can get to data, so can you. If a researcher needs permission, so do you. A computer is like having a

million corporate researchers at your disposal.

AS for company policy, it is usually used internally to shield from liability and serves as a warning but is not

entirely enforceable. The legal parts letting you know about copyrights and such are and usually are supposed to be

known by everyone. Complete ignorance is not a legal protection. It does provide a ground set of rules. Be nice, or get

banned is that message as far as I know.

My personal strategy is to start with public data and embellish it within legal means.


Source: http://stackoverflow.com/questions/14735791/legality-of-web-scraping-vs-normal-use