Data Mining and Financial Data Analysis

Data Mining and Financial Data Analysis

Introduction:

Most marketers understand the value of collecting financial data, but also realize the challenges of leveraging this knowledge to create intelligent, proactive pathways back to the customer. Data mining - technologies and techniques for recognizing and tracking patterns within data - helps businesses sift through layers of seemingly unrelated data for meaningful relationships, where they can anticipate, rather than simply react to, customer needs as well as financial need. In this accessible introduction, we provides a business and technological overview of data mining and outlines how, along with sound business processes and complementary technologies, data mining can reinforce and redefine for financial analysis.

Objective:

1. The main objective of mining techniques is to discuss how customized data mining tools should be developed for financial data analysis.

2. Usage pattern, in terms of the purpose can be categories as per the need for financial analysis.

3. Develop a tool for financial analysis through data mining techniques.

Data mining:

Data mining is the procedure for extracting or mining knowledge for the large quantity of data or we can say data mining is "knowledge mining for data" or also we can say Knowledge Discovery in Database (KDD). Means data mining is : data collection , database creation, data management, data analysis and understanding.

There are some steps in the process of knowledge discovery in database, such as

1. Data cleaning. (To remove nose and inconsistent data)

2. Data integration. (Where multiple data source may be combined.)

3. Data selection. (Where data relevant to the analysis task are retrieved from the database.)

4. Data transformation. (Where data are transformed or consolidated into forms appropriate for mining by performing summary or aggregation operations, for instance)

5. Data mining. (An essential process where intelligent methods are applied in order to extract data patterns.)

6. Pattern evaluation. (To identify the truly interesting patterns representing knowledge based on some interesting measures.)

7. Knowledge presentation.(Where visualization and knowledge representation techniques are used to present the mined knowledge to the user.)

Data Warehouse:

A data warehouse is a repository of information collected from multiple sources, stored under a unified schema and which usually resides at a single site.

Text:

Most of the banks and financial institutions offer a wide verity of banking services such as checking, savings, business and individual customer transactions, credit and investment services like mutual funds etc. Some also offer insurance services and stock investment services.

There are different types of analysis available, but in this case we want to give one analysis known as "Evolution Analysis".

Data evolution analysis is used for the object whose behavior changes over time. Although this may include characterization, discrimination, association, classification, or clustering of time related data, means we can say this evolution analysis is done through the time series data analysis, sequence or periodicity pattern matching and similarity based data analysis.

Data collect from banking and financial sectors are often relatively complete, reliable and high quality, which gives the facility for analysis and data mining. Here we discuss few cases such as,

Eg, 1. Suppose we have stock market data of the last few years available. And we would like to invest in shares of best companies. A data mining study of stock exchange data may identify stock evolution regularities for overall stocks and for the stocks of particular companies. Such regularities may help predict future trends in stock market prices, contributing our decision making regarding stock investments.

Eg, 2. One may like to view the debt and revenue change by month, by region and by other factors along with minimum, maximum, total, average, and other statistical information. Data ware houses, give the facility for comparative analysis and outlier analysis all are play important roles in financial data analysis and mining.

Eg, 3. Loan payment prediction and customer credit analysis are critical to the business of the bank. There are many factors can strongly influence loan payment performance and customer credit rating. Data mining may help identify important factors and eliminate irrelevant one.

Factors related to the risk of loan payments like term of the loan, debt ratio, payment to income ratio, credit history and many more. The banks than decide whose profile shows relatively low risks according to the critical factor analysis.

We can perform the task faster and create a more sophisticated presentation with financial analysis software. These products condense complex data analyses into easy-to-understand graphic presentations. And there's a bonus: Such software can vault our practice to a more advanced business consulting level and help we attract new clients.

To help us find a program that best fits our needs-and our budget-we examined some of the leading packages that represent, by vendors' estimates, more than 90% of the market. Although all the packages are marketed as financial analysis software, they don't all perform every function needed for full-spectrum analyses. It should allow us to provide a unique service to clients.

The Products:

ACCPAC CFO (Comprehensive Financial Optimizer) is designed for small and medium-size enterprises and can help make business-planning decisions by modeling the impact of various options. This is accomplished by demonstrating the what-if outcomes of small changes. A roll forward feature prepares budgets or forecast reports in minutes. The program also generates a financial scorecard of key financial information and indicators.

Customized Financial Analysis by BizBench provides financial benchmarking to determine how a company compares to others in its industry by using the Risk Management Association (RMA) database. It also highlights key ratios that need improvement and year-to-year trend analysis. A unique function, Back Calculation, calculates the profit targets or the appropriate asset base to support existing sales and profitability. Its DuPont Model Analysis demonstrates how each ratio affects return on equity.

Financial Analysis CS reviews and compares a client's financial position with business peers or industry standards. It also can compare multiple locations of a single business to determine which are most profitable. Users who subscribe to the RMA option can integrate with Financial Analysis CS, which then lets them provide aggregated financial indicators of peers or industry standards, showing clients how their businesses compare.

iLumen regularly collects a client's financial information to provide ongoing analysis. It also provides benchmarking information, comparing the client's financial performance with industry peers. The system is Web-based and can monitor a client's performance on a monthly, quarterly and annual basis. The network can upload a trial balance file directly from any accounting software program and provide charts, graphs and ratios that demonstrate a company's performance for the period. Analysis tools are viewed through customized dashboards.

PlanGuru by New Horizon Technologies can generate client-ready integrated balance sheets, income statements and cash-flow statements. The program includes tools for analyzing data, making projections, forecasting and budgeting. It also supports multiple resulting scenarios. The system can calculate up to 21 financial ratios as well as the breakeven point. PlanGuru uses a spreadsheet-style interface and wizards that guide users through data entry. It can import from Excel, QuickBooks, Peachtree and plain text files. It comes in professional and consultant editions. An add-on, called the Business Analyzer, calculates benchmarks.

ProfitCents by Sageworks is Web-based, so it requires no software or updates. It integrates with QuickBooks, CCH, Caseware, Creative Solutions and Best Software applications. It also provides a wide variety of businesses analyses for nonprofits and sole proprietorships. The company offers free consulting, training and customer support. It's also available in Spanish.

ProfitSystem fx Profit Driver by CCH Tax and Accounting provides a wide range of financial diagnostics and analytics. It provides data in spreadsheet form and can calculate benchmarking against industry standards. The program can track up to 40 periods.

Source : http://ezinearticles.com/?Data-Mining-and-Financial-Data-Analysis&id=2752017

One of the Main Differences Between Statistical Analysis and Data Mining

One of the Main Differences Between Statistical Analysis and Data Mining

Two methods of analyzing data that are common in both academic and commercial fields are statistical analysis and data mining. While statistical analysis has a long scientific history, data mining is a more recent method of data analysis that has arisen from Computer Science. In this article I want to give an introduction to these methods and outline what I believe is one of the main differences between the two fields of analysis.

Statistical analysis commonly involves an analyst formulating a hypothesis and then testing the validity of this hypothesis by running statistical tests on data that may have been collected for the purpose. For example, if an analyst was studying the relationship between income level and the ability to get a loan, the analyst may hypothesis that there will be a correlation between income level and the amount of credit someone may qualify for.

The analyst could then test this hypothesis with the use of a data set that contains a number of people along with their income levels and the credit available to them. A test could be run that indicates for example that there may be a high degree of confidence that there is indeed a correlation between income and available credit. The main point here is that the analyst has formulated a hypothesis and then used a statistical test along with a data set to provide evidence in support or against that hypothesis.

Data mining is another area of data analysis that has arisen more recently from computer science that has a number of differences to traditional statistical analysis. Firstly, many data mining techniques are designed to be applied to very large data sets, while statistical analysis techniques are often designed to form evidence in support or against a hypothesis from a more limited set of data.

Probably the mist significant difference here, however, is that data mining techniques are not used so much to form confidence in a hypothesis, but rather extract unknown relationships may be present in the data set. This is probably best illustrated with an example. Rather than in the above case where a statistician may form a hypothesis between income levels and an applicants ability to get a loan, in data mining, there is not typically an initial hypothesis. A data mining analyst may have a large data set on loans that have been given to people along with demographic information of these people such as their income level, their age, any existing debts they have and if they have ever defaulted on a loan before.

A data mining technique may then search through this large data set and extract a previously unknown relationship between income levels, peoples existing debt and their ability to get a loan.

While there are quite a few differences between statistical analysis and data mining, I believe this difference is at the heart of the issue. A lot of statistical analysis is about analyzing data to either form confidence for or against a stated hypothesis while data mining is often more about applying an algorithm to a data set to extract previously unforeseen relationships.

Source:http://ezinearticles.com/?One-of-the-Main-Differences-Between-Statistical-Analysis-and-Data-Mining&id=4578250

Data Extraction Services For Better Outputs in Your Business

Data Extraction Services For Better Outputs in Your Business

Data Extraction can be defined as the process of retrieving data from an unstructured source in order to process it further or store it. It is very useful for large organizations who deal with large amount of data on a daily basis that need to be processed into meaningful information and stored for later use. The data extraction is a systematic way to extract and structure data from scattered and semi-structured electronic documents, as found on the web and in various data warehouses.

In today's highly competitive business world, vital business information such as customer statistics, competitor's operational figures and inter-company sales figures play an important role in making strategic decisions. By signing on this service provider, you will be get access to critivcal data from various sources like websites, databases, images and documents.

It can help you take strategic business decisions that can shape your business' goals. Whether you need customer information, nuggets into your competitor's operations and figure out your organization's performance, it is highly critical to have data at your fingertips as and when you want it. Your company may be crippled with tons of data and it may prove a headache to control and convert the data into useful information. Data extraction services enable you get data quickly and in the right format.

Few areas where Data Extraction can help you are:

    Capturing financial data
    Generating better sales leads
    Conducting market research, survey and analysis
    Conducting product research and analysis
    Track, extract and harvest product pricing data
    Searching for specific job postings
    Duplicating an online database
    Acquiring real estate data
    Processing auction information
    Searching online newspapers for latest pricing information
    Extracting and summarize news stories from online news sources

Outsourcing companies provide custom made data extraction services to the client's requirements. The different types of data extraction services;

    Web extraction
    Database extraction

Outsourcing is the beneficial option for large organizations seeking to manage large information. Outsourcing this services helps businesses in managing their data effectively, which in turn enables business to experience an increase in profits. By outsourcing, you can certainly increase your competitive edge and save costs too!

This article is courtesy of Web Scraping Expert - an executive at Outsourcing Web Research offer high quality and time bound comprehensive range of data extraction services at affordable rates. For more info please visit us at: http://www.webscrapingexpert.com/ or directly send your requirements at: info@webscrapingexpert.com

Source:http://ezinearticles.com/?Data-Extraction-Services-For-Better-Outputs-in-Your-Business&id=2760257

Increasing Accessibility by Scraping Information From PDF

Increasing Accessibility by Scraping Information From PDF

You may have heard about data scraping which is a method that is being used by computer programs in extracting data from an output that comes from another program. To put it simply, this is a process which involves the automatic sorting of information that can be found on different resources including the internet which is inside an html file, PDF or any other documents. In addition to that, there is the collection of pertinent information. These pieces of information will be contained into the databases or spreadsheets so that the users can retrieve them later.

Most of the websites today have text that can be accessed and written easily in the source code. However, there are now other businesses nowadays that choose to make use of Adobe PDF files or Portable Document Format. This is a type of file that can be viewed by simply using the free software known as the Adobe Acrobat. Almost any operating system supports the said software. There are many advantages when you choose to utilize PDF files. Among them is that the document that you have looks exactly the same even if you put it in another computer so that you can view it. Therefore, this makes it ideal for business documents or even specification sheets. Of course there are disadvantages as well. One of which is that the text that is contained in the file is converted into an image. In this case, it is often that you may have problems with this when it comes to the copying and pasting.

This is why there are some that start scraping information from PDF. This is often called PDF scraping in which this is the process that is just like data scraping only that you will be getting information that is contained in your PDF files. In order for you to begin scraping information from PDF, you must choose and exploit a tool that is specifically designed for this process. However, you will find that it is not easy to locate the right tool that will enable you to perform PDF scraping effectively. This is because most of the tools today have problems in obtaining exactly the same data that you want without personalizing them.

Nevertheless, if you search well enough, you will be able to encounter the program that you are looking for. There is no need for you to have programming language knowledge in order for you to use them. You can easily specify your own preferences and the software will do the rest of the work for you. There are also companies out there that you can contact and they will perform the task since they have the right tools that they can use. If you choose to do things manually, you will find that this is indeed tedious and complicated whereas if you compare this to having professionals do the job for you, they will be able to finish it in no time at all. Scraping information from PDF is a process where you collect the information that can be found on the internet and this does not infringe copyright laws.

Source:http://ezinearticles.com/?Increasing-Accessibility-by-Scraping-Information-From-PDF&id=4593863

Collecting Data With Web Scrapers

Collecting Data With Web Scrapers

There is a large amount of data available only through websites. However, as many people have found out, trying to copy data into a usable database or spreadsheet directly out of a website can be a tiring process. Data entry from internet sources can quickly become cost prohibitive as the required hours add up. Clearly, an automated method for collating information from HTML-based sites can offer huge management cost savings.

Web scrapers are programs that are able to aggregate information from the internet. They are capable of navigating the web, assessing the contents of a site, and then pulling data points and placing them into a structured, working database or spreadsheet. Many companies and services will use programs to web scrape, such as comparing prices, performing online research, or tracking changes to online content.

Let's take a look at how web scrapers can aid data collection and management for a variety of purposes.

Improving On Manual Entry Methods

Using a computer's copy and paste function or simply typing text from a site is extremely inefficient and costly. Web scrapers are able to navigate through a series of websites, make decisions on what is important data, and then copy the info into a structured database, spreadsheet, or other program. Software packages include the ability to record macros by having a user perform a routine once and then have the computer remember and automate those actions. Every user can effectively act as their own programmer to expand the capabilities to process websites. These applications can also interface with databases in order to automatically manage information as it is pulled from a website.

Aggregating Information

There are a number of instances where material stored in websites can be manipulated and stored. For example, a clothing company that is looking to bring their line of apparel to retailers can go online for the contact information of retailers in their area and then present that information to sales personnel to generate leads. Many businesses can perform market research on prices and product availability by analyzing online catalogues.

Data Management

Managing figures and numbers is best done through spreadsheets and databases; however, information on a website formatted with HTML is not readily accessible for such purposes. While websites are excellent for displaying facts and figures, they fall short when they need to be analyzed, sorted, or otherwise manipulated. Ultimately, web scrapers are able to take the output that is intended for display to a person and change it to numbers that can be used by a computer. Furthermore, by automating this process with software applications and macros, entry costs are severely reduced.

This type of data management is also effective at merging different information sources. If a company were to purchase research or statistical information, it could be scraped in order to format the information into a database. This is also highly effective at taking a legacy system's contents and incorporating them into today's systems.

Overall, a web scraper is a cost effective user tool for data manipulation and management.

source: http://ezinearticles.com/?Collecting-Data-With-Web-Scrapers&id=4223877

How Web Data Extraction Services Will Save Your Time and Money by Automatic Data Collection

How Web Data Extraction Services Will Save Your Time and Money by Automatic Data Collection

Data scrape is the process of extracting data from web by using software program from proven website only. Extracted data any one can use for any purposes as per the desires in various industries as the web having every important data of the world. We provide best of the web data extracting software. We have the expertise and one of kind knowledge in web data extraction, image scrapping, screen scrapping, email extract services, data mining, web grabbing.

Who can use Data Scraping Services?

Data scraping and extraction services can be used by any organization, company, or any firm who would like to have a data from particular industry, data of targeted customer, particular company, or anything which is available on net like data of email id, website name, search term or anything which is available on web. Most of time a marketing company like to use data scraping and data extraction services to do marketing for a particular product in certain industry and to reach the targeted customer for example if X company like to contact a restaurant of California city, so our software can extract the data of restaurant of California city and a marketing company can use this data to market their restaurant kind of product. MLM and Network marketing company also use data extraction and data scrapping services to to find a new customer by extracting data of certain prospective customer and can contact customer by telephone, sending a postcard, email marketing, and this way they build their huge network and build large group for their own product and company.

We helped many companies to find particular data as per their need for example.

Web Data Extraction

Web pages are built using text-based mark-up languages (HTML and XHTML), and frequently contain a wealth of useful data in text form. However, most web pages are designed for human end-users and not for ease of automated use. Because of this, tool kits that scrape web content were created. A web scraper is an API to extract data from a web site. We help you to create a kind of API which helps you to scrape data as per your need. We provide quality and affordable web Data Extraction application

Data Collection

Normally, data transfer between programs is accomplished using info structures suited for automated processing by computers, not people. Such interchange formats and protocols are typically rigidly structured, well-documented, easily parsed, and keep ambiguity to a minimum. Very often, these transmissions are not human-readable at all. That's why the key element that distinguishes data scraping from regular parsing is that the output being scraped was intended for display to an end-user.

Email Extractor

A tool which helps you to extract the email ids from any reliable sources automatically that is called a email extractor. It basically services the function of collecting business contacts from various web pages, HTML files, text files or any other format without duplicates email ids.

Screen scrapping

Screen scraping referred to the practice of reading text information from a computer display terminal's screen and collecting visual data from a source, instead of parsing data as in web scraping.

Data Mining Services

Data Mining Services is the process of extracting patterns from information. Datamining is becoming an increasingly important tool to transform the data into information. Any format including MS excels, CSV, HTML and many such formats according to your requirements.

Web spider

A Web spider is a computer program that browses the World Wide Web in a methodical, automated manner or in an orderly fashion. Many sites, in particular search engines, use spidering as a means of providing up-to-date data.

Web Grabber

Web grabber is just a other name of the data scraping or data extraction.

Web Bot

Web Bot is software program that is claimed to be able to predict future events by tracking keywords entered on the Internet. Web bot software is the best program to pull out articles, blog, relevant website content and many such website related data We have worked with many clients for data extracting, data scrapping and data mining they are really happy with our services we provide very quality services and make your work data work very easy and automatic.

Source: http://ezinearticles.com/?How-Web-Data-Extraction-Services-Will-Save-Your-Time-and-Money-by-Automatic-Data-Collection&id=5159023

How Xpath Plays Vital Role In Web Scraping

How Xpath Plays Vital Role In Web Scraping

XPath is a language for finding information in structured documents like XML or HTML. You can say that XPath is (sort of) SQL for XML or HTML files. XPath is used to navigate through elements and attributes in an XML or HTML document.

To understand XPath we must be clear about elements and nodes which are the building blocks of XML and HTML. Let’s talk about them. Here is an example element in an HTML document:

   <a class=”hyperlink” href=http://www.google.com>google</a>

Copy the above text to a file, name it as sample.html and open it in a browser. This will end up as a text link displaying the words “google” and it will take you to www.google.com. For each element there are three main parts: The type, the attributes, andthe text. They are listed below:

 a                                 Type
class,  href                Attributes
google                       Text

Let’s grab some XPath developer tools. I am on Firebug for Firefox or you can use Chrome’s developer tools. We will now form some XPath expressions to extract data from the above element. We will also verify the XPath by using Firebug Console.

For extracting the text “google”:

   //a[@href]/text()   

   //a[@class=”hyperlink”]/text()
 
For extracting the hyperlink i.e. ”www.google.com” :

   //a/@href
//a[@class=”hyperlink”]/@href

That’s all with a single element but in reality, you need to deal with more complex forms.

Let’s proceed to the idea of nodes, and its familial relationship of HTML elements. Look at this example code:

 <div title=”Section1″>

   <table id=”Search”>

       <tr class=”Yahoo”>Yahoo Search</tr>

       <tr class=”Google”>Google Search</tr>

   </table>

</div>

 Notice the </div> at the bottom? That means the table and tr elements are contained within the div. These other elements are considered descendants of the div. The table is a child, and the tr is a grandchild (and so on and so forth). The two tr elements are considered siblings each other. This is vital, as XPath uses these relationships to find your element.

So suppose you want to find the Google item. Any of the following expressions will work:

   //tr[@class=’Google’]
   //div/table/tr[2]
  //div[@title=”Section1″]//tr

So let’s analyze the expressions. We start at the top element (also known as a node). The // means to search all descendants, / means to just look at the current element’s children. So //div means look through all descendants for a div element. The brackets [] specify something about that element. So we can look for an attribute with the @ symbol, or look for text with the text() function. We can chain as many of these together as we can.

Here is a quick reference:

   //             Search all descendant elements
   /              Search all child elements
   []             The predicate (specifies something about the element you are looking for)
   @           Specifies an element attribute. (For example, @title)
   
   .               Specifies the current node (useful when you want to look for an element’s children in the predicate)
   ..              Specifies the parent node
  text()       Gets the text of the element.
   
In the context of web scraping, XPath is a nice tool to have in your belt, as it allows you to write specifications of document locations more flexibly than CSS selectors.

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Source: http://blog.datahut.co/how-xpath-plays-vital-role-in-web-scraping/

Web Scraping with Python: A Beginner’s Guide

Web Scraping with Python: A Beginner’s Guide

In the Big Data world, Web Scraping or Data extraction services are the primary requisites for Big Data Analytics. Pulling up data from the web has become almost inevitable for companies to stay in business. Next question that comes up is how to go about web scraping as a beginner.

Data can be extracted or scraped from a web source using a number of methods. Popular websites like Google, Facebook, or Twitter offer APIs to view and extract the available data in a structured manner.  This prevents the use of other methods that may not be preferred by the API provider. However, the demand to scrape a website arises when the information is not readily offered by the website. Python, an open source programming language is often used for Web Scraping due to its simple and rich ecosystem. It contains a library called “BeautifulSoup” which carries on this task. Let’s take a deeper look into web scraping using python.

Setting up a Python Environment:

To carry out web scraping using Python, you will first have to install the Python Environment, which enables to run code written in the python language. The libraries perform data scraping;

Beautiful Soup is a convenient-to-use python library. It is one of the finest tools for extracting information from a webpage. Professionals can scrape information from web pages in the form of tables, lists, or paragraphs. Urllib2 is another library that can be used in combination with the BeautifulSoup library for fetching the web pages. Filters can be added to extract specific information from web pages. Urllib2 is a Python module that can fetch URLs.

For MAC OSX :

To install Python libraries on MAC OSX, users need to open a terminal win and type in the following commands, single command at a time:

sudoeasy_install pip

pip install BeautifulSoup4

pip install lxml

For Windows 7 & 8 users:

Windows 7 & 8 users need to ensure that the python environment gets installed first. Once, the environment is installed, open the command prompt and find the way to root C:/ directory and type in the following commands:

easy_install BeautifulSoup4

easy_installlxml

Once the libraries are installed, it is time to write data scraping code.

Running Python:

Data scraping must be done for a distinct objective such as to scrape current stock of a retail store. First, a web browser is required to navigate the website that contains this data. After identifying the table, right click anywhere on it and then select inspect element from the dropdown menu list. This will cause a window to pop-up on the bottom or side of your screen displaying the website’s html code. The rankings appear in a table. You might need to scan through the HTML data until you find the line of code that highlights the table on the webpage.

Python offers some other alternatives for HTML scraping apart from BeautifulSoup. They include:

    Scrapy
    Scrapemark
    Mechanize

 Web scraping converts unstructured data from HTML code into structured form such as tabular data in an Excel worksheet. Web scraping can be done in many ways ranging from the use of Google Docs to programming languages. For people who do not have any programming knowledge or technical competencies, it is possible to acquire web data by using web scraping services that provide ready to use data from websites of your preference.

HTML Tags:

To perform web scraping, users must have a sound knowledge of HTML tags. It might help a lot to know that HTML links are defined using anchor tag i.e. <a> tag, “<a href=“http://…”>The link needs to be here </a>”. An HTML list comprises <ul> (unordered) and <ol> (ordered) list. The item of list starts with <li>.

HTML tables are defined with<Table>, row as <tr> and columns are divided into data as <td>;

    <!DOCTYPE html> : A HTML document starts with a document type declaration
    The main part of the HTML document in unformatted, plain text is defined by <body> and </body> tags
    The headings in HTML are defined using the heading tags from <h1> to <h5>
    Paragraphs are defined with the <p> tag in HTML
    An entire HTML document is contained between <html> and </html>

Using BeautifulSoup in Scraping:

While scraping a webpage using BeautifulSoup, the main concern is to identify the final objective. For instance, if you would like to extract a list from webpage, a step wise approach is required:

    First and foremost step is to import the required libraries:

 #import the library used to query a website

import urllib2

#specify the url wiki = “https://”

#Query the website and return the html to the variable ‘page’

page = urllib2.urlopen(wiki)

#import the Beautiful soup functions to parse the data returned from the website

from bs4 import BeautifulSoup

#Parse the html in the ‘page’ variable, and store it in Beautiful Soup format

soup = BeautifulSoup(page)

    Use function “prettify” to visualize nested structure of HTML page
    Working with Soup tags:

Soup<tag> is used for returning content between opening and closing tag including tag.

    In[30]:soup.title

 Out[30]:<title>List of Presidents in India till 2010 – Wikipedia, the free encyclopedia</title>

    soup.<tag>.string: Return string within given tag
    In [38]:soup.title.string
    Out[38]:u ‘List of Presidents in India and Brazil till 2010 in India – Wikipedia, the free encyclopedia’
    Find all the links within page’s <a> tags: Tag a link using tag “<a>”. So, go with option soup.a and it should return the links available in the web page. Let’s do it.
    In [40]:soup.a

Out[40]:<a id=”top”></a>

    Find the right table:

As a table to pull up information about Presidents in India and Brazil till 2010 is being searched for, identifying the right table first is important. Here’s a command to scrape information enclosed in all table tags.

all_tables= soup.find_all(‘table’)

Identify the right table by using attribute “class” of table needs to filter the right table. Thereafter, inspect the class name by right clicking on the required table of web page as follows:

    Inspect element
    Copy the class name or find the class name of right table from the last command’s output.

 right_table=soup.find(‘table’, class_=’wikitable sortable plainrowheaders’)

right_table

That’s how we can identify the right table.

    Extract the information to DataFrame: There is a need to iterate through each row (tr) and then assign each element of tr (td) to a variable and add it to a list. Let’s analyse the Table’s HTML structure of the table. (extract information for table heading <th>)

To access value of each element, there is a need to use “find(text=True)” option with each element.  Finally, there is data in dataframe.

There are various other ways to scrape data using “BeautifulSoup” that reduce manual efforts to collect data from web pages. Code written in BeautifulSoup is considered to be more robust than the regular expressions. The web scraping method we discussed use “BeautifulSoup” and “urllib2” libraries in Python. That was a brief beginner’s guide to start using Python for web scraping.

Source: https://www.promptcloud.com/blog/web-scraping-python-guide

Scraping Yelp Business Data With Python Scraping Script

Scraping Yelp Business Data With Python Scraping Script

Yelp is a great source of business contact information with details like address, postal code, contact information; website addresses etc. that other site like Google Maps just does not. Yelp also provides reviews about the particular business. The yelp business database can be useful for telemarketing, email marketing and lead generation.

Are you looking for yelp business details database? Are you looking for scraping data from yelp website/business directory? Are you looking for yelp screen scraping software? Are you looking for scraping the business contact information from the online Yelp? Then you are at the right place.

Here I am going to discuss how to scrape yelp data for lead generation and email marketing. I have made a simple and straight forward yelp data scraping script in python that can scrape data from yelp website. You can use this yelp scraper script absolutely free.

I have used urllib, BeautifulSoup packages. Urllib package to make http request and parsed the HTML using BeautifulSoup, used Threads to make the scraping faster.
Yelp Scraping Python Script

import urllib
from bs4 import BeautifulSoup
import re
from threading import Thread

#List of yelp urls to scrape
url=['http://www.yelp.com/biz/liman-fisch-restaurant-hamburg','http://www.yelp.com/biz/casa-franco-caramba-hamburg','http://www.yelp.com/biz/o-ren-ishii-hamburg','http://www.yelp.com/biz/gastwerk-hotel-hamburg-hamburg-2','http://www.yelp.com/biz/superbude-hamburg-2','http://www.yelp.com/biz/hotel-hafen-hamburg-hamburg','http://www.yelp.com/biz/hamburg-marriott-hotel-hamburg','http://www.yelp.com/biz/yoho-hamburg']

i=0
#function that will do actual scraping job
def scrape(ur):

          html = urllib.urlopen(ur).read()
          soup = BeautifulSoup(html)

      title = soup.find('h1',itemprop="name")
          saddress = soup.find('span',itemprop="streetAddress")
          postalcode = soup.find('span',itemprop="postalCode")
          print title.text
          print saddress.text
          print postalcode.text
          print "-------------------"

threadlist = []

#making threads
while i<len(url):
          t = Thread(target=scrape,args=(url[i],))
          t.start()
          threadlist.append(t)
          i=i+1

for b in threadlist:
          b.join()

Recently I had worked for one German company and did yelp scraping project for them and delivered data as per their requirement. If you looking for scraping data from business directories like yelp then send me your requirement and I will get back to you with sample.

Source: http://webdata-scraping.com/scraping-yelp-business-data-python-scraping-script/

Run Code Template – New Feature Added to Fminer Web Scraping Tool

Run Code Template – New Feature Added to Fminer Web Scraping Tool

Fminer is one of the powerful web scraping software, I already given brief of all the Fminer features in previous post. In this post I am going to introduce one of the interesting feature of fminer which is Run Code Template that is recently added to Fminer, this feature is similar to “Fminer Run Code” action but it’s different in a way you can use it. The Run Code Action you can use inside the data scraping flow and python code get executed when scraper start running.

While Run Code Templates are the saved python code snippets that you can run on the data tables after scraping completes. Assume if you get white space in scraped data then you can easily trim this left and right spaces by just executing “strip_column” template, see the code of that template below.

'''Strip all data of a column in data table
Remove the blank of data in the head and the tail.
'''

tabName = '[%table1|data table%]'
colName = '[%table1.column1|table column for strip%]'

tab = tables[tabName]
for i, row in enumerate(tab):
    row[colName] = row[colName].strip()   
    tab.edit_row(i, row)

This template comes with Fminer and few other template like “merge_tables_with_same_columns”.  Below are the steps how you can execute template python code on scraped data.

Step 1: Click on second icon from right that says “Run Code” under the Data section

Step 2: One popup will appear, you need to click on “Templates” icon and choose the template you want to execute and then click on Ok.

Step 3: Now the window will appear for configuration that will ask you to choose the table and column under that table on which you want to execute the code. Now click on Ok again.

Step 4: Now you can see the code of that template, now you can click on execute icon and script will start running, based on number of records it will take time to finish execution.

In many web scraping projects I found this template code very handy for cleaning data and making life easy. Templates are stored at following path so you can create your own template with customized code.

C:\Program Files (x86)\FMiner\templates

I have created one template which I use to remove HTML code that comes while scraping badly organized HTML pages. Below is the code of template for stripping html:

'''Strip HTML will remove all html tags of a column in data table.
'''
import re
tabName = '[%table1|data table%]'
colName = '[%table1.column1|table column for substring%]'
colNew = '[%table1.column1|table column to add new data%]'
tab = tables[tabName]
for i, row in enumerate(tab):
    cleanr =re.compile('<.*?>')
    cleantext = re.sub(cleanr,'', row[colName])
    row[colNew] = cleantext 
    tab.edit_row(i, row)

Stay connected as I am going to post more code templates that will make your web scraping life easy and manipulate data on fly.

Source: http://webdata-scraping.com/run-code-template-new-feature-added-fminer-web-scraping-tool/

How Web Scraping for Brand Monitoring is used in Retail Sector

How Web Scraping for Brand Monitoring is used in Retail Sector

Structured or unstructured, business data always plays an instrumental part in driving growth, development, and innovation for your dream venture. Irrespective of industrial sectors or verticals, big data, seems to be of paramount significance for every business or enterprise.

The unsurpassed popularity and increasing importance of big data gave birth to the concept of web scraping, thus enhancing growth opportunities for startups. Large or small, every business establishment will now achieve successful website monitoring and tracking.
How web scraping serves your branding need?

Web scraping helps in extracting unorganized data and ordering it into organized and manageable formats. So if your brand is being talked about in multiple ways (on social media, on expert forums, in comments etc.), you can set the scraping tool algorithm to fetch only data that contains reference about the brand. As an outcome, marketers and business owners around the brand can gauge brand sentiment and tweak their launch marketing campaign to enhance visibility.

Look around and you will discover numerous web scraping solutions ranging from manual to fully automated systems. From Reputation Tracking to Website monitoring, your web scraper can help create amazing insights from seemingly random bits of data (both in structured as well as unstructured format).
Using web scraping

The concept of web scraping revolutionizes the use of big data for business. With its availability across sectors, retailers are on cloud nine. Here’s how the retail market is utilizing the power of Web Scraping for brand monitoring.

Determining pricing strategy

The retail market is filled with competition. Whether it is products or pricing strategies, every retailer competes hard to stay ahead of the growth curve. Web scraping techniques will help you crawl price comparison sites’ pricing data, product descriptions, as well as images to receive data for comparison, affiliation, or analytics.

As a result, retailers will have the opportunity to trade their products at competitive prices, thus increasing profit margins by a whopping 10%.

Tracking online presence

Current trends in ecommerce herald the need for a strong online presence. Web scraping takes cue from this particular aspect, thus scraping reviews and profiles on websites. By providing you a crystal clear picture of product performance, customer behavior, and interactions, web scraping will help you achieve Online Brand Intelligence and monitoring.
Detection of fraudulent reviews

Present-day purchasers have this unique habit of referring to reviews, before finalizing their purchase decisions. Web scraping helps in the identification of opinion-spamming, thus figuring out fake reviews. It will further extend support in detecting, reviewing, streamlining, or blocking reviews, according to your business needs.
Online reputation management

Web data scraping helps in figuring out avenues to take your ORM objectives forward. With the help of the scraped data, you learn about both the impactful as well as vulnerable areas for online reputation management. You will have the web crawler identifying demographic opinions such as age group, gender, sentiments, and GEO location.

Social media analytics

Since social media happens to be one of the most crucial factors for retailers, it will be imperative to Scrape Social Media websites and extract data from Twitter. The web scraping technology will help you watch your brand in Social Media along with fetching Data for social media analytics. With social media channels such as Twitter monitoring services, you will strengthen your firm’s’ branding even more than before.
Advantages of BM

As a business, you might want to monitor your brand in social media to gain deep insights about your brand’s popularity and the current consumer behavior. Brand monitoring companies will watch your brand in social media and come up with crucial data for social media analytics. This process has immense benefits for your business, these are summarized over here –

Locate Infringers

Leading brands often face the challenge thrown by infringers. When brand monitoring companies keep a close look at products available in the market, there is less probability of a copyright infringement. The biggest infringement happens in the packaging, naming and presentation of products. With constant monitoring and legal support provided by the Trademark Law, businesses could remain protected from unethical competitors and illicit business practices.

Manage Consumer Reaction and Competitor’s Challenges

A good business keeps a check on the current consumer sentiment in the targeted demographic and positively manages the same in the interest of their brand. The feedback from your consumers could be affirmative or negative but if you have a hold on the social media channels, web platforms and forums, you, as a brand will be able to propagate trust at all times.

When competitor brands indulge in backbiting or false publicity about your brand, you can easily tame their negative comments by throwing in a positive image in front of your target audience. So, brand monitoring and its active implementation do help in positive image building and management for businesses.
Why Web scraping for BM?

Web scraping for brand monitoring gives you a second pair of eyes to look at your brand as a general consumer. Considering the flowing consumer sentiment in the market during a specific business season, you could correct or simply innovate better ways to mold the target audience in your brand’s favor. Through a systematic approach towards online brand intelligence and monitoring, future business strategies and possible brand responses could be designed, keeping your business actively prepared for both types of scenarios.

For effective web scraping, businesses extract data from Twitter that helps them understand ‘what’s trending’ in their business domain. They also come closer to reality in terms of brand perception, user interaction and brand visibility in the notions of their clientele. Web scraping professionals or companies scrape social media websites to gather relevant data related to your brand or your competitor’s that has the potential to affect your growth as a business. Management and organization of this data is done to extract out significant and reference building facts. Future strategy for your brand is designed by brand monitoring professionals keeping in mind the facts accumulated through web scraping. The data obtained through web scraping helps in –

Knowing the actual brand potential,
Expanding brand coverage,
Devising brand penetration,
Analyzing scope and possibilities for a brand and
Design thoughtful and insightful brand strategies.

In simple words, web scraping provides a business enough base of information that could be used to devise future plans and to make suggestive changes in the current business strategy.

Advantages of Web scraping for BM

Web scraping has made things seamless for businesses involved in managing their brands and active brand monitoring. There is no doubt, that web scraping for brand monitoring comes with immense benefits, some of these are –

Improved customer insight

When you have in hand and factual knowledge about your consumer base through social media channels, you are in a strong position to portray your positive image as a brand. With more realistic data on your hands, you could develop strategies more effectively and make realistic goals for your brand’s improvement. Social media insights also allows marketers to create highly targeted and custom marketing messages – thus leading to better likelihood of sales conversion.

Monitoring your Competition

Web scraping helps you realize where your brand stands in the market among the competition. The actual penetration of your brand in the targeted segment helps in getting a clear picture of your present business scenario. Through careful removal of competition in your concerned business category, you could strengthen your brand image.

Staying Informed

When your brand monitoring team is keeping track of all social media channels, it becomes easier for you to stay informed about latest comments about your business on sites like Facebook, Twitter and social forums etc. You could have deep knowledge about the consumer behavior related to your brand and your competitors on these web destinations.

Improved Consumer Satisfaction and Sales

Reputation tracking done through web scraping helps in generating planned response at times of crisis. It also mends the communication gap between consumer and the brand, hence improving the consumer satisfaction. This automatically translates into trust building and brand loyalty improving your brand’s sales.

To sign off

By granting opportunities to monitor your social media data, web scraping is undoubtedly helping retail businesses take a significant step towards perfect branding. If you are one of the key players in this sector, there’s reason for celebration ahead!

Source: https://www.promptcloud.com/blog/How-Web-Scraping-for-Brand-Monitoring-is-used-in-Retail-Sector

How Web Scraping can Help you Detect Weak spots in your Business

How Web Scraping can Help you Detect Weak spots in your Business

Business intelligence is not a new term. Businesses have always been employing experts for analysing the progress, market and industry trends to keep their growth graph going up. Now that we have big data and the tool to gather this data – Web scraping, business intelligence has become even more fruitful. In fact, business intelligence has become a necessary thing to survive now that the competition is fierce in every industry. This is the reason why most enterprises depend on web scraping solutions to gather the data relevant to their businesses. This data is highly insightful and dependable enough to make critical business decisions. Business intelligence from web scraping is definitely a game changer for companies as it can supply relevant and actionable data with minimal effort.

Most businesses have weak spots that are being overlooked or hidden from the plain sight. These weak spots, if left unnoticed can gradually result in the downfall of your company. Here is how you can use data acquired through web scraping to detect weak spots in your business and strengthen them.

Competitor analysis

Many a times, you can find out the flaws in your business by keeping a close watch on your competitors. Competitor analysis is something that we owe to web scraping as the level of competitive intelligence that you can derive from web scraping has never been achievable in the past. With crawling forums and social media sites where your target audience is, you can easily find out if your competitor is leveraging something you have overlooked. Competitor analysis is all about staying updated to each and every action by your competitors, so that you can always be prepared for their next strategic move. If your competitors are doing better than you, this data can be used to make a comparison between your business and theirs which would give you insights on where you lack.

Brand monitoring on Social media

With social media platforms acting like platforms where businesses and customers can interact with each other, the data available on these sites are increasingly becoming relevant to businesses. Any issues in your business operations will also reflect on your customer sentiments. Social media is a goldmine of sentiment data that can help you detect issues within your company. By analysing the posts that mention your brand or product on social media sites, you can identify what department of your company is functioning well and what isn’t.

For example, if you are an Ecommerce portal and many users are complaining about delivery issues from your company on social media, you might want to switch to a better logistics partner who does a better job. The ability to identify such issues at the earliest is extremely important and that’s where web scraping becomes a life saver. With social media scraping, monitoring your brand on social media is easy like never before and the chances of minor issues escalating to bigger ones is almost non-existent. Brand monitoring is extremely crucial if you are a business operating in the online space. Social media scraping solutions are provided by many leading web scraping companies, which totally eliminates the technical complications associated with the process for you.

Finding untapped opportunities

There are always new and untapped markets and opportunities that are relevant to your business. Finding them is not going to be an easy task with manual and outdated methods of research. Web scraping can fill this gap and help you find opportunities that your company can make use of to leverage your reach and progress. Sometimes, targeting the right audience makes all the difference that you’ve been trying to make. By using web crawling to find mentions of your relevant keywords on the web, you can easily stay updated on your niche and fill in to any new untapped markets. Web crawling for keywords is better explained in our previous blog.

Bottom line

It is not a cakewalk to stay ahead in the competition considering how competitive every industry has become in this digital age. It is crucial to find the weak spots and untapped opportunities of your business before someone else does. Of course, you can always use some help from the technology when you need it. Web scraping is clearly the best way to find and gather data that would help you figure these out. With web crawling solutions that can completely take care of this niche process, nothing is stopping you from using the data and insights that the web has in stock for your business.

Source: https://www.promptcloud.com/blog/web-scraping-detect-weak-spots-business

How Web Data Extraction Services Will Save Your Time and Money by Automatic Data Collection

How Web Data Extraction Services Will Save Your Time and Money by Automatic Data Collection

Data scrape is the process of extracting data from web by using software program from proven website only. Extracted data any one can use for any purposes as per the desires in various industries as the web having every important data of the world. We provide best of the web data extracting software. We have the expertise and one of kind knowledge in web data extraction, image scrapping, screen scrapping, email extract services, data mining, web grabbing.

Who can use Data Scraping Services?

Data scraping and extraction services can be used by any organization, company, or any firm who would like to have a data from particular industry, data of targeted customer, particular company, or anything which is available on net like data of email id, website name, search term or anything which is available on web. Most of time a marketing company like to use data scraping and data extraction services to do marketing for a particular product in certain industry and to reach the targeted customer for example if X company like to contact a restaurant of California city, so our software can extract the data of restaurant of California city and a marketing company can use this data to market their restaurant kind of product. MLM and Network marketing company also use data extraction and data scrapping services to to find a new customer by extracting data of certain prospective customer and can contact customer by telephone, sending a postcard, email marketing, and this way they build their huge network and build large group for their own product and company.

We helped many companies to find particular data as per their need for example.

Web Data Extraction

Web pages are built using text-based mark-up languages (HTML and XHTML), and frequently contain a wealth of useful data in text form. However, most web pages are designed for human end-users and not for ease of automated use. Because of this, tool kits that scrape web content were created. A web scraper is an API to extract data from a web site. We help you to create a kind of API which helps you to scrape data as per your need. We provide quality and affordable web Data Extraction application

Data Collection

Normally, data transfer between programs is accomplished using info structures suited for automated processing by computers, not people. Such interchange formats and protocols are typically rigidly structured, well-documented, easily parsed, and keep ambiguity to a minimum. Very often, these transmissions are not human-readable at all. That's why the key element that distinguishes data scraping from regular parsing is that the output being scraped was intended for display to an end-user.

Email Extractor

A tool which helps you to extract the email ids from any reliable sources automatically that is called a email extractor. It basically services the function of collecting business contacts from various web pages, HTML files, text files or any other format without duplicates email ids.

Screen scrapping

Screen scraping referred to the practice of reading text information from a computer display terminal's screen and collecting visual data from a source, instead of parsing data as in web scraping.

Data Mining Services

Data Mining Services is the process of extracting patterns from information. Datamining is becoming an increasingly important tool to transform the data into information. Any format including MS excels, CSV, HTML and many such formats according to your requirements.

Web spider

A Web spider is a computer program that browses the World Wide Web in a methodical, automated manner or in an orderly fashion. Many sites, in particular search engines, use spidering as a means of providing up-to-date data.

Web Grabber

Web grabber is just a other name of the data scraping or data extraction.

Web Bot

Web Bot is software program that is claimed to be able to predict future events by tracking keywords entered on the Internet. Web bot software is the best program to pull out articles, blog, relevant website content and many such website related data We have worked with many clients for data extracting, data scrapping and data mining they are really happy with our services we provide very quality services and make your work data work very easy and automatic.

Source: http://ezinearticles.com/?How-Web-Data-Extraction-Services-Will-Save-Your-Time-and-Money-by-Automatic-Data-Collection&id=5159023

Are You Screen Scraping or Data Mining?

Are You Screen Scraping or Data Mining?

Many of us seem to use these terms interchangeably but let’s make sure we are clear about the differences that make each of these approaches different from the other.

Basically, screen scraping is a process where you use a computer program or software to extract information from a website.  This is different than crawling, searching or mining a site because you are not indexing everything on the page – a screen scraper simply extracts precise information selected by the user.  Screen scraping is a useful application when you want to do real-time, price and product comparisons, archive web pages, or acquire data sets that you want to evaluate or filter.

When you perform screen scraping, you are able to scrape data more directly and, you can automate the process if you are using the right solution. Different types of screen scraping services and solutions offer different ways of obtaining information. Some look directly at the html code of the webpage to grab the data while others use more advanced, visual abstraction techniques that can often avoid “breakage” errors when the web source experiences a programming or code change.

On the other hand, data mining is basically the process of automatically searching large amounts of information and data for patterns. This means that you already have the information and what you really need to do is analyze the contents to find the useful things you need. This is very different from screen scraping as screen scraping requires you to look for the data, collect it and then you can analyze it.

Data mining also involves a lot of complicated algorithms often based on various statistical methods. This process has nothing to do with how you obtain the data. All it cares about is analyzing what is available for evaluation.

Screen scraping is often mistaken for data mining when, in fact, these are two different things. Today, there are online services that offer screen scraping. Depending on what you need, you can have it custom tailored to meet your specific needs and perform precisely the tasks you want. But screen scraping does not guarantee any kind of analysis of the data.

Source: http://www.connotate.com/are-you-screen-scraping-or-data-mining/

Scraping LinkedIn Public Profiles for Fun and Profit

Scraping LinkedIn Public Profiles for Fun and Profit

Reconnaissance and Information Gathering is a part of almost every penetration testing engagement. Often, the tester will only perform network reconnaissance in an attempt to disclose and learn the company's network infrastructure (i.e. IP addresses, domain names, and etc), but there are other types of reconnaissance to conduct, and no, I'm not talking about dumpster diving. Thanks to social networks like LinkedIn, OSINT/WEBINT is now yielding more information. This information can then be used to help the tester test anything from social engineering to weak passwords.

In this blog post I will show you how to use Pythonect to easily generate potential passwords from LinkedIn public profiles. If you haven't heard about Pythonect yet, it is a new, experimental, general-purpose dataflow programming language based on the Python programming language. Pythonect is most suitable for creating applications that are themselves focused on the "flow" of the data. An application that generates passwords from the employees public LinkedIn profiles of a given company - have a coherence and clear dataflow:

(1) Find all the employees public LinkedIn profiles → (2) Scrap all the employees public LinkedIn profiles → (3) Crunch all the data into potential passwords

Now that we have the general concept and high-level overview out of the way, let's dive in to the details.

Finding all the employees public LinkedIn profiles will be done via Google Custom Search Engine, a free service by Google that allows anyone to create their own search engine by themselves. The idea is to create a search engine that when searching for a given company name - will return all the employees public LinkedIn profiles. How? When creating a Google Custom Search Engine it's possible to refine the search results to a specific site (i.e. 'Sites to search'), and we're going to limit ours to: linkedin.com. It's also possible to fine-tune the search results even further, e.g. uk.linkedin.com to find only employees from United Kingdom.

The access to the newly created Google Custom Search Engine will be made using a free API key obtained from Google API Console. Why go through the Google API? because it allows automation (No CAPTCHA's), and it also means that the search-result pages will be returned as JSON (as oppose to HTML). The only catch with using the free API key is that it's limited to 100 queries per day, but it's possible to buy an API key that will not be limited.

Scraping the profiles is a matter of iterating all over the hCards in all the search-result pages, and extracting the employee name from each hCard. Whats is a hCard? hCard is a micro format for publishing the contact details of people, companies, organizations, and places. hCard is also supported by social networks such as Facebook, Google+, LinkedIn and etc. for exporting public profiles. Google (when indexing) parses hCard, and when relevant, uses them in search-result pages. In other words, when search-result pages include LinkedIn public profiles, it will appear as hCards, and could be easily parsed.

Let's see the implementation of the above:

#!/usr/bin/python
#
# Copyright (C) 2012 Itzik Kotler
#
# scraper.py is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation, either version 3 of the License, or
# (at your option) any later version.
#
# scraper.py is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with scraper.py.  If not, see <http://www.gnu.org/licenses/>.

"""Simple LinkedIn public profiles scraper that uses Google Custom Search"""

import urllib
import simplejson


BASE_URL = "https://www.googleapis.com/customsearch/v1?key=<YOUR GOOGLE API KEY>&cx=<YOUR GOOGLE SEARCH ENGINE CX>"


def __get_all_hcards_from_query(query, index=0, hcards={}):

    url = query

    if index != 0:

        url = url + '&start=%d' % (index)

    json = simplejson.loads(urllib.urlopen(url).read())

    if json.has_key('error'):

        print "Stopping at %s due to Error!" % (url)

        print json

    else:

        for item in json['items']:

            try:

                hcards[item['pagemap']['hcard'][0]['fn']] = item['pagemap']['hcard'][0]['title']

            except KeyError as e:

                pass

        if json['queries'].has_key('nextPage'):

            return __get_all_hcards_from_query(query, json['queries']['nextPage'][0]['startIndex'], hcards)

    return hcards


def get_all_employees_by_company_via_linkedin(company):

    queries = ['"at %s" inurl:"in"', '"at %s" inurl:"pub"']

    result = {}

    for query in queries:

        _query = query % company

        result.update(__get_all_hcards_from_query(BASE_URL + '&q=' + _query))

    return list(result)

Replace <YOUR GOOGLE API KEY> and <YOUR GOOGLE SEARCH ENGINE CX> in the code above with your Google API Key and Google Search Engine CX respectively, save it to a file called scraper.py, and you're ready!

To kick-start, here is a simple program in Pythonect (that utilizes the scraper module) that searchs and prints all the Pythonect company employees full names:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin -> print

The output should be:

Itzik Kotler

In my LinkedIn Profile, I have listed Pythonect as a company that I work for, and since no one else is working there, when searching for all the employees of Pythonect company - only my LinkedIn profile comes up.
For demonstration purposes I will keep using this example (i.e. "Pythonect" company, and "Itzik Kotler" employee), but go ahead and replace Pythonect with other, more popular, companies names and see the results.

Now that we have a working skeleton, let's take its output and start crunching it. Keep in mind that every "password generation forumla" is merely a guess. The examples below are only a sampling of what can be done. There are, obviously many more possibilities and you are encouraged to experiment. But first, let's normalize the output - this way it's going to be consistent before operations are performed on it:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin -> string.lower(''.join(_.split()))

The normalization procedure is short and simple: convert the string to lowercase and remove any spaces, and so the output should be now:

itzikkotler

As for data manipulation, out of the box (Thanks to The Python Standard Library) we've got itertools and it's combinatoric generators. Let's start by applying itertools.product:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin -> string.lower(''.join(_.split())) -> itertools.product(_, repeat=4) -> print

The code above will generate and print every 4 characters password from the letters: i, t, z, k, o, t, l , e, r. However, it won't cover passwords with uppercase letters in it. And so, here's a simple and straightforward implementation of a cycle_uppercase function that cycles the input letters yields a copy of the input with letter in uppercase:

def cycle_uppercase(i):
    s = ''.join(i)
    for idx in xrange(0, len(s)):
        yield s[:idx] + s[idx].upper() + s[idx+1:]

To use it, save it to a file called itertools2.py, and then simply add it to the Pythonect program after the itertools.product(_, repeat=4) block, as follows:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin \
    -> string.lower(''.join(_.split())) \
        -> itertools.product(_, repeat=4) \
            -> itertools2.cycle_uppercase \
                -> print

Now, the program will also cover passwords that include a single uppercase letter in it. Moving on with the data manipulation, sometimes the password might contain symbols that are not found within the scrapped data. In this case, it is necessary to build a generator that will take the input and add symbols to it. Here is a short and simple generator implemented as a Generator Expression:

[_ + postfix for postfix in ['123','!','$']]

To use it, simply add it to the Pythonect program after the itertools2.cycle_uppercase block, as follows:

"Pythonect" -> scraper.get_all_employees_by_company_via_linkedin \
    -> string.lower(''.join(_.split())) \
        -> itertools.product(_, repeat=4) \
            -> itertools2.cycle_uppercase \
                -> [_ + postfix for postfix in ['123','!','$']] \
                    -> print

The result is that now the program adds the strings: '123', '!', and '$' to every generated password, which increases the chances of guessing the user's right password, or not, depends on the password :)

To summarize, it's possible to take OSINT/WEBINT data on a given person or company and use it to generate potential passwords, and it's easy to do with Pythonect. There are, of course, many different ways to manipulate the data into passwords and many programs and filters that can be used. In this aspect, Pythonect being a flow-oriented language makes it easy to experiment and research with different modules and programs in a "plug and play" manner.

Source:http://blog.ikotler.org/2012/12/scraping-linkedin-public-profiles-for.html