Why Healthcare Companies should look towards Web Scraping

Why Healthcare Companies should look towards Web Scraping

The internet is a massive storehouse of information which is available in the form of text, media and other formats. To be competitive in this modern world, most businesses need access to this storehouse of information. But, all this information is not freely accessible as several websites do not allow you to save the data. This is where the process of Web Scraping comes in handy.

Web scraping is not new—it has been widely used by financial organizations, for detecting fraud; by marketers, for marketing and cross-selling; and by manufacturers for maintenance scheduling and quality control. Web scraping has endless uses for business and personal users. Every business or individual can have his or her own particular need for collecting data. You might want to access data belonging to a particular category from several websites. The different websites belonging to the particular category display information in non-uniform formats. Even if you are surfing a single website, you may not be able to access all the data at one place.

The data may be distributed across multiple pages under various heads. In a market that is vast and evolving rapidly, strategic decision-making demands accurate and thorough data to be analyzed, and on a periodic basis. The process of web scraping can help you mine data from several websites and store it in a single place so that it becomes convenient for you to a alyze the data and deliver results.

In the context of healthcare, web scraping is gaining foothold gradually but qualitatively. Several factors have led to the use of web scraping in healthcare. The voluminous amount of data produced by healthcare industry is too complex to be analyzed by traditional techniques. Web scraping along with data extraction can improve decision-making by determining trends and patterns in huge amounts of intricate data. Such intensive analyses are becoming progressively vital owing to financial pressures that have increased the need for healthcare organizations to arrive at conclusions based on the analysis of financial and clinical data. Furthermore, increasing cases of medical insurance fraud and abuse are encouraging healthcare insurers to resort to web scraping and data extraction techniques.

Healthcare is no longer a sector relying solely on person to person interaction. Healthcare has gone digital in its own way and different stakeholders of this industry such as doctors, nurses, patients and pharmacists are upping their ante technologically to remain in sync with the changing times. In the existing setup, where all choices are data-centric, web scraping in healthcare can impact lives, educate people, and create awareness. As people no more depend only on doctors and pharmacists, web scraping in healthcare can improve lives by offering rational solutions.

To be successful in the healthcare sector, it is important to come up with ways to gather and present information in innovative and informative ways to patients and customers. Web scraping offers a plethora of solutions for the healthcare industry. With web scraping and data extraction solutions, healthcare companies can monitor and gather information as well as track how their healthcare product is being received, used and implemented in different locales. It offers a safer and comprehensive access to data allowing healthcare experts to take the right decisions which ultimately lead to better clinical experience for the patients.

Web scraping not only gives healthcare professionals access to enterprise-wide information but also simplifies the process of data conversion for predictive analysis and reports. Analyzing user reviews in terms of precautions and symptoms for diseases that are incurable till date and are still undergoing medical research for effective treatments, can mitigate the fear in people. Data analysis can be based on data available with patients and is one way of creating awareness among people.

Hence, web scraping can increase the significance of data collection and help doctors make sense of the raw data. With web scraping and data extraction techniques, healthcare insurers can reduce the attempts of frauds, healthcare organizations can focus on better customer relationship management decisions, doctors can identify effective cure and best practices, and patients can get more affordable and better healthcare services.

Web scraping applications in healthcare can have remarkable utility and potential. However, the triumph of web scraping and data extraction techniques in healthcare sector depends on the accessibility to clean healthcare data. For this, it is imperative that the healthcare industry think about how data can be better recorded, stored, primed, and scraped. For instance, healthcare sector can consider standardizing clinical vocabulary and allow sharing of data across organizations to heighten the benefits from healthcare web scraping practices.

Healthcare sector is one of the top sectors where data is multiplying exponentially with time and requires a planned and structured storage of data. Continuous web scraping and data extraction is necessary to gain useful insights for renewing health insurance policies periodically as well as offer affordable and better public health solutions. Web scraping and data extraction together can process the mammoth mounds of healthcare data and transform it into information useful for decision making.

To reduce the gap between various components of healthcare sector-patients, doctors, pharmacies and hospitals, healthcare organizations and websites will have to tap the technology to collect data in all formats and present in a usable form. The healthcare sector needs to overcome the lag in implementing effective web scraping and data extraction techniques as well as intensify their pace of technology adoption. Web scraping can contribute enormously to the healthcare industry and facilitate organizations to methodically collect data and process it to identify inadequacies and best practices that improve patient care and reduce costs.

Source: https://www.promptcloud.com/blog/why-health-care-companies-should-use-web-scraping

Business Intelligence & Data Warehousing in a Business Perspective

Business Intelligence & Data Warehousing in a Business Perspective

Business Intelligence

Business Intelligence has become a very important activity in the business arena irrespective of the domain due to the fact that managers need to analyze comprehensively in order to face the challenges.

Data sourcing, data analysing, extracting the correct information for a given criteria, assessing the risks and finally supporting the decision making process are the main components of BI.

In a business perspective, core stakeholders need to be well aware of all the above stages and be crystal clear on expectations. The person, who is being assigned with the role of Business Analyst (BA) for the BI initiative either from the BI solution providers' side or the company itself, needs to take the full responsibility on assuring that all the above steps are correctly being carried out, in a way that it would ultimately give the business the expected leverage. The management, who will be the users of the BI solution, and the business stakeholders, need to communicate with the BA correctly and elaborately on their expectations and help him throughout the process.

Data sourcing is an initial yet crucial step that would have a direct impact on the system where extracting information from multiple sources of data has to be carried out. The data may be on text documents such as memos, reports, email messages, and it may be on the formats such as photographs, images, sounds, and they can be on more computer oriented sources like databases, formatted tables, web pages and URL lists. The key to data sourcing is to obtain the information in electronic form. Therefore, typically scanners, digital cameras, database queries, web searches, computer file access etc, would play significant roles. In a business perspective, emphasis should be placed on the identification of the correct relevant data sources, the granularity of the data to be extracted, possibility of data being extracted from identified sources and the confirmation that only correct and accurate data is extracted and passed on to the data analysis stage of the BI process.

Business oriented stake holders guided by the BA need to put in lot of thought during the analyzing stage as well, which is the second phase. Synthesizing useful knowledge from collections of data should be done in an analytical way using the in-depth business knowledge whilst estimating current trends, integrating and summarizing disparate information, validating models of understanding, and predicting missing information or future trends. This process of data analysis is also called data mining or knowledge discovery. Probability theory, statistical analysis methods, operational research and artificial intelligence are the tools to be used within this stage. It is not expected that business oriented stake holders (including the BA) are experts of all the above theoretical concepts and application methodologies, but they need to be able to guide the relevant resources in order to achieve the ultimate expectations of BI, which they know best.

Identifying relevant criteria, conditions and parameters of report generation is solely based on business requirements, which need to be well communicated by the users and correctly captured by the BA. Ultimately, correct decision support will be facilitated through the BI initiative and it aims to provide warnings on important events, such as takeovers, market changes, and poor staff performance, so that preventative steps could be taken. It seeks to help analyze and make better business decisions, to improve sales or customer satisfaction or staff morale. It presents the information that manager's need, as and when they need it.

In a business sense, BI should go several steps forward bypassing the mere conventional reporting, which should explain "what has happened?" through baseline metrics. The value addition will be higher if it can produce descriptive metrics, which will explain "why has it happened?" and the value added to the business will be much higher if predictive metrics could be provided to explain "what will happen?" Therefore, when providing a BI solution, it is important to think in these additional value adding lines.

Data warehousing

In the context of BI, data warehousing (DW) is also a critical resource to be implemented to maximize the effectiveness of the BI process. BI and DW are two terminologies that go in line. It has come to a level where a true BI system is ineffective without a powerful DW, in order to understand the reality behind this statement, it's important to have an insight in to what DW really is.

A data warehouse is one large data store for the business in concern which has integrated, time variant, non volatile collection of data in support of management's decision making process. It will mainly have transactional data which would facilitate effective querying, analyzing and report generation, which in turn would give the management the required level of information for the decision making.

The reasons to have BI together with DW

At this point, it should be made clear why a BI tool is more effective with a powerful DW. To query, analyze and generate worthy reports, the systems should have information available. Importantly, transactional information such as sales data, human resources data etc. are available normally in different applications of the enterprise, which would obviously be physically held in different databases. Therefore, data is not at one particular place, hence making it very difficult to generate intelligent information.

The level of reports expected today, are not merely independent for each department, but managers today want to analyze data and relationships across the enterprise so that their BI process is effective. Therefore, having data coming from all the sources to one location in the form of a data warehouse is crucial for the success of the BI initiative. In a business viewpoint, this message should be passed and sold to the managements of enterprises so that they understand the value of the investment. Once invested, its gains could be achieved over several years, in turn marking a high ROI.

Investment costs for a DW in the short term may look quite high, but it's important to re-iterate that the gains are much higher and it will span over many years to come. It also reduces future development cost since with the DW any requested report or view could be easily facilitated. However, it is important to find the right business sponsor for the project. He or she needs to communicate regularly with executives to ensure that they understand the value of what's being built. Business sponsors need to be decisive, take an enterprise-wide perspective and have the authority to enforce their decisions.

Process

Implementation of a DW itself overlaps with some phases of the above explained BI process and it's important to note that in a process standpoint, DW falls in to the first few phases of the entire BI initiative. Gaining highly valuable information out of DW is the latter part of the BI process. This can be done in many ways. DW can be used as the data repository of application servers that run decision support systems, management Information Systems, Expert systems etc., through them, intelligent information could be achieved.

But one of the latest strategies is to build cubes out of the DW and allow users to analyze data in multiple dimensions, and also provide with powerful analytical supporting such as drill down information in to granular levels. Cube is a concept that is different to the traditional relational 2-dimensional tabular view, and it has multiple dimensions, allowing a manager to analyze data based on multiple factors, and not just two factors. On the other hand, it allows the user to select whatever the dimension he wish to choose for analyzing purposes and not be limited by one fixed view of data, which is called as slice & dice in DW terminology.

BI for a serious enterprise is not just a phase of a computerization process, but it is one of the major strategies behind the entire organizational drivers. Therefore management should sit down and build up a BI strategy for the company and identify the information they require in each business direction within the enterprise. Given this, BA needs to analyze the organizational data sources in order to build up the most effective DW which would help the strategized BI process.

High level Ideas on Implementation

At the heart of the data warehousing process is the extract, transform, and load (ETL) process. Implementation of this merely is a technical concern but it's a business concern to make sure it is designed in such a way that it ultimately helps to satisfy the business requirements. This process is responsible for connecting to and extracting data from one or more transactional systems (source systems), transforming it according to the business rules defined through the business objectives, and loading it into the all important data model. It is at this point where data quality should be gained. Of the many responsibilities of the data warehouse, the ETL process represents a significant portion of all the moving parts of the warehousing process.

Creation of a powerful DW depends on the correctness of data modeling, which is the responsibility of the database architect of the project, but BA needs to play a pivotal role providing him with correct data sources, data requirements and most importantly business dimensions. Business Dimensional modeling is a special method used for DW projects and this normally should be carried out by the BA and from there onwards technical experts should take up the work. Dimensions are perspectives specific to a business that could be used for analysis purposes. As an example, for a sales database, the dimensions could include Product, Time, Store, etc. Obviously these dimensions differ from one business to another and hence for each DW initiative those dimensions should be correctly identified and that could be very well done by a person who has experience in the DW domain and understands the business as well, making it apparent that DW BA is the person responsible.

Each of the identified dimensions would be turned in to a dimension table at the implementation phase, and the objective of the above explained ETL process is to fill up these dimension tables, which in turn will be taken to the level of the DW after performing some more database activities based on a strong underlying data model. Implementation details are not important for a business stakeholder but being aware of high level process to this level is important so that they are also on the same pitch as that of the developers and can confirm that developers are actually doing what they are supposed to do and would ultimately deliver what they are supposed to deliver.

Security is also vital in this regard, since this entire effort deals with highly sensitive information and identification of access right to specific people to specific information should be correctly identified and captured at the requirements analysis stage.

Advantages

There are so many advantages of BI system. More presentation of analytics directly to the customer or supply chain partner will be possible. Customer scores, customer campaigns and new product bundles can all be produced from analytic structures resulting in high customer retention and creation of unique products. More collaboration within information can be achieved from effective BI. Rather than middle managers getting great reports and making their own areas look good, information will be conveyed into other functions and rapidly shared to create collaborative decisions increasing the efficiency and accuracy. The return on human capital will be greatly increased.

Managers at all levels will save their time on data analysis, and hence saving money for the enterprise, as the time of managers is equal to money in a financial perspective. Since powerful BI would enable monitoring internal processes of the enterprises more closely and allow making them more efficient, the overall success of the organization would automatically grow. All these would help to derive a high ROI on BI together with a strong DW. It is a common experience to notice very high ROI figures on such implementations, and it is also important to note that there are many non-measurable gains whilst we consider most of the measurable gains for the ROI calculation. However, at a stage where it is intended to take the management buy-in for the BI initiative, it's important to convert all the non measurable gains in to monitory values as much as possible, for example, saving of managers time can be converted in to a monitory value using his compensation.

The author has knowledge in both Business and IT. Started career as a Software Engineer and moved to work in the business analysis area of a premier US based software company.

Source: http://ezinearticles.com/?Business-Intelligence-and-Data-Warehousing-in-a-Business-Perspective&id=35640

Web Scraping Best Practices

Web Scraping Best Practices

Extracting data from the World Wide Web has several challenges as more webmasters are working day and night to lower cases of scraping and crawling of their data in order to survive in the competitive world. There are various other problems you may face when web scraping and most of them can be avoided by adapting and implementing certain web scraping best practices as discussed in this article.

Have knowledge of the scraping tools

Acquiring adequate knowledge of hurdles that may be encountered during web scraping, you will be able to have a smooth web scraping experience and be on the safe side of the law. Conduct a thorough research on the types of tools you will use for scraping and crawling. Firsthand knowledge on these tools will help you find the data you need without being blocked.

Proper proxy software that acts as the middle party works well when you know how to work around HTTP and HTML protocols. Use tools that can change crawling patterns, URLs and data retrieved even when you are crawling on one domain. This will help you abide to the rules and regulations that come with web scraping activities and escaping any legal issues.
Conduct your scraping activities during off-peak hours

You may opt to extract data during times that less people have access for instance over the weekends, during late night hours, public holidays among others. Visiting a website on several instances to retrieve the same type of data is a waste of bandwidth. It is always advisable to download the entire site content to your computer and thereafter you can access it whenever need arises.
Hide your scrapping activities

There is a thin line between ethical and unethical crawling hence you should completely evade being on the top user list of a particular website. Cover up your track as best as you can by making use of proxy IPs to avoid any legal problems. You may also use multiple IP addresses or VPN services to conceal your scrapping activities and lower chances of landing on a website’s blacklist.

Website owners today are very protective of their data and any other information existing under their unique url. Be keen when going through the terms and conditions indicated by websites as they may consider crawling as an infringement of their privacy. Simple etiquette goes a long way. Your web scraping efforts will be fruitful if the site owner supports the idea of sharing data.
Keep record of your activities

Web scraping involves large amount of data.Due to this you may not always remember each and every piece of information you have acquired, gathering statistics will help you monitor your activities.
Load data in phases

Web scraping demands a lot of patience from you when using the crawlers to get needed information. Take the process in a slow manner by loading data one piece at a time. Several parallel request to the same domain can crush the entire site or retrace the scrapping attempts back to your local machine.

Loading data small bits will save you the hustle of scrapping afresh in case that your activity has been interrupted because you will have already stored part of the data required. You can reduce the loading data on an individual domain through various techniques such as caching pages that you have scrapped to escape redundancy occurrences. Use auto throttling mechanisms to increase the amount of traffic to the website and pause for breaks between requests to prevent getting banned.
Conclusion

Through these few mentioned web scraping best practices you will be able to work around website and gather the data required as per clients’ request without major hurdles along the way. The ultimate goal of every web scraper is to be able to access vital information and at the same time remain on the good side of the law.

Source: http://nocodewebscraping.com/web-scraping-best-practices/

Data Mining vs Screen-Scraping

Data Mining vs Screen-Scraping

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

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

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

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

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

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

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