Wednesday, 30 November 2016

Assuring Scraping Success with Proxy Data Scraping

Assuring Scraping Success with Proxy Data Scraping

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

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

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

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

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

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


Saturday, 26 November 2016

How Xpath Plays Vital Role In Web Scraping Part 2

How Xpath Plays Vital Role In Web Scraping Part 2

Here is a piece of content on  Xpaths which is the follow up of How Xpath Plays Vital Role In Web Scraping

Let’s dive into a real-world example of scraping amazon website for getting information about deals of the day. Deals of the day in amazon can be found at this URL. So navigate to the amazon (deals of the day) in Firefox and find the XPath selectors. Right click on the deal you like and select “Inspect Element with Firebug”:

If you observe the image below keenly, there you can find the source of the image(deal) and the name of the deal in src, alt attribute’s respectively.

So now let’s write a generic XPath which gathers the name and image source of the product(deal).

  //img[@role=”img”]/@src  ## for image source
  //img[@role=”img”]/@alt   ## for product name

In this post, I’ll show you some tips we found valuable when using XPath in the trenches.

If you have an interest in Python and web scraping, you may have already played with the nice requests library to get the content of pages from the Web. Maybe you have toyed around using Scrapy selector or lxml to make the content extraction easier. Well, now I’m going to show you some tips I found valuable when using XPath in the trenches and we are going to use both lxml and Scrapy selector for HTML parsing.

Avoid using expressions which contains(.//text(), ‘search text’) in your XPath conditions. Use contains(., ‘search text’) instead.

Here is why: the expression .//text() yields a collection of text elements — a node-set(collection of nodes).and when a node-set is converted to a string, which happens when it is passed as argument to a string function like contains() or starts-with(), results in the text for the first element only.

from scrapy import Selector
html_code = “””<a href=”#”>Click here to go to the <strong>Next Page</strong></a>”””
sel = Selector(text=html_code)
xp = lambda x: sel.xpath(x).extract()           # Let’s type this only once
print xp(‘//a//text()’)                                       # Take a peek at the node-set
[u’Click here to go to the ‘, u’Next Page’]   # output of above command
print xp(‘string(//a//text())’)                           # convert it to a string
  [u’Click here to go to the ‘]                           # output of the above command

Let’s do the above one by using lxml then you can implement XPath by both lxml or Scrapy selector as XPath expression is same for both methods.

lxml code:

from lxml import html
html_code = “””<a href=”#”>Click here to go to the <strong>Next Page</strong></a>””” # Parse the text into a tree
parsed_body = html.fromstring(html_code)  # Perform xpaths on the tree
print parsed_body(‘//a//text()’)                      # take a peek at the node-set
[u’Click here to go to the ‘, u’Next Page’]   # output
print parsed_body(‘string(//a//text())’)              # convert it to a string
[u’Click here to go to the ‘]                    # output

A node converted to a string, however, puts together the text of itself plus of all its descendants:

>>> xp(‘//a[1]’)  # selects the first a node
[u'<a href=”#”>Click here to go to the <strong>Next Page</strong></a>’]

>>> xp(‘string(//a[1])’)  # converts it to string
[u’Click here to go to the Next Page’]

Beware of the difference between //node[1] and (//node)[1]//node[1] selects all the nodes occurring first under their respective parents and (//node)[1] selects all the nodes in the document, and then gets only the first of them.

from scrapy import Selector

html_code = “””<ul class=”list”>

<ul class=”list”>

sel = Selector(text=html_code)
xp = lambda x: sel.xpath(x).extract()

xp(“//li[1]”) # get all first LI elements under whatever it is its parent

[u'<li>1</li>’, u'<li>4</li>’]

xp(“(//li)[1]”) # get the first LI element in the whole document


xp(“//ul/li[1]”)  # get all first LI elements under an UL parent

[u'<li>1</li>’, u'<li>4</li>’]

xp(“(//ul/li)[1]”) # get the first LI element under an UL parent in the document



//a[starts-with(@href, ‘#’)][1] gets a collection of the local anchors that occur first under their respective parents and (//a[starts-with(@href, ‘#’)])[1] gets the first local anchor in the document.

When selecting by class, be as specific as necessary.

If you want to select elements by a CSS class, the XPath way to do the same job is the rather verbose:

*[contains(concat(‘ ‘, normalize-space(@class), ‘ ‘), ‘ someclass ‘)]

Let’s cook up some examples:

>>> sel = Selector(text='<p class=”content-author”>Someone</p><p class=”content text-wrap”>Some content</p>’)

>>> xp = lambda x: sel.xpath(x).extract()

BAD: because there are multiple classes in the attribute

>>> xp(“//*[@class=’content’]”)


BAD: gets more content than we need

 >>> xp(“//*[contains(@class,’content’)]”)

     [u'<p class=”content-author”>Someone</p>’,
     u'<p class=”content text-wrap”>Some content</p>’]


>>> xp(“//*[contains(concat(‘ ‘, normalize-space(@class), ‘ ‘), ‘ content ‘)]”)
[u'<p class=”content text-wrap”>Some content</p>’]

And many times, you can just use a CSS selector instead, and even combine the two of them if needed:


>>> sel.css(“.content”).extract()
[u'<p class=”content text-wrap”>Some content</p>’]

>>> sel.css(‘.content’).xpath(‘@class’).extract()
[u’content text-wrap’]

Learn to use all the different axes.

It is handy to know how to use the axes, you can follow through these examples.

In particular, you should note that following and following-sibling are not the same thing, this is a common source of confusion. The same goes for preceding and preceding-sibling, and also ancestor and parent.

Useful trick to get text content

Here is another XPath trick that you may use to get the interesting text contents: 

//*[not(self::script or self::style)]/text()[normalize-space(.)]

This excludes the content from the script and style tags and also skip whitespace-only text nodes.

Tools & Libraries Used:

Firefox inspect element with firebug
Scrapy : 1.1.1
Python : 2.7.12
Requests : 2.11.0

 Have questions? Comment below. Please share if you found this helpful.


Wednesday, 9 November 2016

Data Mining Process - Why Outsource Data Mining Service?

Data Mining Process - Why Outsource Data Mining Service?

Overview of Data Mining and Process:

Data mining is one of the unique techniques for investigating information to extract certain data patterns and decide to outcome of existing requirements. Data mining is widely use in client research, services analysis, market research and so on. It is totally based on mathematical algorithm and analytical skills to drive the desired results from the huge database collection.

Information mining is mostly used by financial analyzer, business and professional organization and also there are many growing area of business that are get maximum advantages of data extract with use of data warehouses in their small to large level of businesses.

Most of functionalities which are used in information collecting process define as under:

* Retrieving Data

* Analyzing Data

* Extracting Data

* Transforming Data

* Loading Data

* Managing Databases

Most of small, medium and large levels of businesses are collect huge amount of data or information for analysis and research to develop business. Such kind of large amount will help and makes it much important whenever information or data required.

Why Outsource Data Online Mining Service?

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Thus, data or information mining is very important part of the web research services and it is most useful process. By outsource data extraction and mining service; you can concentrate on your co relative business and growing fast as you desire.

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