The interpreted programming language Python has surged in popularity in recent years. Long beloved by system administrators and others who had good use for the way it made routine tasks easy to automate, it has gained traction in other sectors as well. In particular, it has become one of the most-used tools in the discipline of numerical computing and analysis. Being put to use for such heavy lifting has endowed the language with a great selection of powerful libraries and other tools that make it even more flexible. One upshot of this development has been that sophisticated business analysts have also come to see the language as a valuable tool for those own data analysis needs.

Greatly appreciated for its simplicity and elegance of syntax, Python makes an excellent first programming language for previously non-technical people. Many business analysts, in fact, have had success growing their skill sets in this way thanks to the language's tractability. Long beloved by specialized data scientists, the iPython interactive computing environment has also attracted great attention within the business analyst’s community. Its instant feedback and visualization options have made it easy for many analysts to become skilled Python programmers while doing valuable work along the way.

Using iPython and appropriate notebooks for it, for example, business analysts can easily make interactive use of such tools as cohort analysis and pivot tables. iPython makes it easy to benefit from real-time, interactive researches which produce immediately visible results, including charts and graphs suitable for use in other contexts. Through becoming familiar with this powerful interactive application, business analysts are also exposing themselves in a natural and productive way to the Python programming language itself.

Gaining proficiency with this language opens up further possibilities. While interactive analytic techniques are of great use to many business analysts, being able to create fully functioning, independent programs is of similar value. Becoming comfortable with Python allows analysts to tackle and plumb even larger data sets than would be possible through an interactive approach, as results can be allowed to accumulate over hours and days of processing time.

This ability can sometime allow business analysts to address the so-called "Big Data" questions that can otherwise seem the sole province of specialized data scientists. More important than this higher level of independence, perhaps, is the fact that this increased facility with data analysis and handling allows analysts to communicate more effectively with such stakeholders. Through learning a programming language which allows them to begin making independent inroads into such areas, business analysts gain a better perspective on these specialized domains, and this allows them to function as even more effective intermediaries.

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Who Are the Main Players in Big Data?

Much of success is about performance. It’s about what we do and what we are able to inspire others to do. There are some simple performance principles I have learned in my life, and I want to share them with you.  They really bring success, and what it takes to be successful, into sharp focus. They are also the basis for developing and maintaining an expectation of success.

The Five Principles of Performance

1. We generally get from ourselves and others what we expect. It is a huge fact that you will either live up or down to your own expectations. If you expect to lose, you will. If you expect to be average, you will be average. If you expect to feel bad, you probably will. If you expect to feel great, nothing will slow you down. And what is true for you is true for others. Your expectations for others will become what they deliver and achieve. As Gandhi said, “Be the change you wish to see in the world.”

2. The difference between good and excellent companies is training. The only thing worse than training employees and losing them is to not train them and keep them! A football team would not be very successful if they did not train, practice, and prepare for their opponents. When you think of training as practice and preparation, it makes you wonder how businesses survive that do not make significant training investments in their people.

Actually, companies that do not train their people and invest in their ability don’t last. They operate from a competitive disadvantage and are eventually gobbled up and defeated in the marketplace. If you want to improve and move from good to excellent, a good training strategy will be the key to success.

Writing Python in Java syntax is possible with a semi-automatic tool. Programming code translation tools pick up about 75% of dynamically typed language. Conversion of Python to a statically typed language like Java requires some manual translation. The modern Java IDE can be used to infer local variable type definitions for each class attribute and local variable.


Translation of Syntax
Both Python and Java are OO imperative languages with sizable syntax constructs. Python is larger, and more competent for functional programming concepts. Using the source translator tool, parsing of the original Python source language will allow for construction of an Abstract Source Tree (AST), followed by conversion of the AST to Java.

Python will parse itself. This capability is exhibited in the ast module, which includes skeleton classes. The latter can be expanded to parse and source each node of an AST. Extension of the ast.NodeVisitor class enables python syntax constructs to be customized using translate.py and parser.py coding structure.

The Concrete Syntax Tree (CST) for Java is based on visit to the AST. Java string templates can be output at AST nodes with visitor.py code. Comment blocks are not retained by the Python ast Parser. Conversion of Python to multi-line string constructs with the translator reduces time to script.


Scripting Python Type Inference in Java
Programmers using Python source know that the language does not contain type information. The fact that Python is a dynamic type language means object type is determined at run time. Python is also not enforced at compile time, as the source is not specified. Runtime type information of an object can be determined by inspecting the __class__.__name__ attribute.

Python’s inspect module is used for constructing profilers and debugging.
Implementation of def traceit (frame, event, arg) method in Python, and connecting it to the interpreter with sys.settrace (traceit) allows for integration of multiple events during application runtime.

Method call events prompt inspect and indexing of runtime type. Inspection of all method arguments can be conducted. By running the application profiler and exercising the code, captured trace files for each source file can be modified with the translator. Generating method syntax can be done with the translator by search and addition of type information. Results in set or returned variables disseminate the dynamic code in static taxonomy.

The final step in the Python to Java scrip integration is to administer unsupported concepts such as value object creation. There is also the task of porting library client code, for reproduction in Java equivalents. Java API stubs can be created to account for Python APIs. Once converted to Java the final clean-up of the script is far easier.

Related:

 What Are The 10 Most Famous Software Programs Written in Python?

Python, a Zen Poem

When eCommerce companies want to optimize information security, password management tools enable users to create strong passwords for every login.

Better than a Master Pass
A two-factor authentication, a security process in which the user provides two means of identification, one of which is typically a physical token, such as a card, and the other of which is typically something memorized, such as a security code can drastically reduce online fraud such as identity theft . A common example of two-factor authenticationis a bank card: the card itself is the physical item and the personal identification number (PIN) is the data that goes with it.

LastPass 3.0 Premium and RoboForm, security downloads offer fingerprint-based authentication features that can be configured to any computer PC or mobile application.  Both are supported by the Google Authenticator mobile app for smart phone and device integration.

LastPass 3.0 is most powerful on-demand password manager on the market. LastPass 3.0 Premium includes mobile support and more features. Dashlane 2.0 is is not as robust, but includes a user-friendly interface. F-Secure Key is a free, one-device version of these top competitors. F-Secure Key is for exclusive use on an installed device, so password safe retention is dependent on proprietary use of the device itself. The application can be upgraded for a small annual fee.

Password Manager App Cross-Portability
F-Secure Key syncs with Mac, PC Android, and iOS devices simultaneously. A transient code is generated on mobile devices, in addition to the two-factor authentication default of the F-Secure Key master password security product.

Password capture and replay in case of lost credentials is made possible with a password manager. Integration of a password manager app with a browser allows a user to capture login credentials, and replay on revisit to a site. Dashlane, LastPass, Norton Identity Safe, Password Genie 4.0 offer continuous detection and management of password change events, automatically capturing credentials each time a new Web-based, service registration sign up is completed.

Other applications like F-Secure Key, KeePass, and My1login replay passwords via a bookmarklet, supported by any Java-equipped browser. KeePass ups the ante for would be keyloggers, with a unique replay technology.

Personal Data and Auto-Fill Forms
Most password managers fill username and password credentials into login forms automatically. Password managers also retain personal data for form fill interfaces with applications, and other HTML forms online. The RoboForm app is one of the most popular for its flexibility in multi-form password and personal data management, but the others also capture and reuse at least a portion of what has been entered in a form manually.

The 1Password app for Windows stores the most types of personal data for use to fill out forms. Dashlane, LastPass, and Password Genie store the various types of ID data used for form fill-in, like passport and driver's license numbers and other key details to HTML acknowledgement of discretionary password and personal information.

The Cost of Protection
LastPass Premium and Password Box are the lowest monthly password manager plans on the market, going for $1 a month. Annual plans offered by other password manager sources vary according to internal plan: Dashlane $20, F-Secure Key $16, and Password Genie, $15.
All password manager companies and their products may not be alike in the end.

Security checks on security products like password managers have become more sophisticated in response to product cross-portability and open source app interface volatility. Norton, RoboForm, KeePass, generate strong, random passwords on-demand. Some security procedures now require three-factor authentication, which involves possession of a physical token and a password, used in conjunction with biometricdata, such as finger-scanningor a voiceprint.

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