Python programming language is general purpose open source programming language. One of its main features is flexibility and ease of use. Python has a variety of useful set of utilities and libraries for data processing and analytical tasks. Currently due to the rise in demand of big data processing python has grown in popularity because its features are easy to use which are core to the processing of huge chunks of information.

Guido Van Rossum, the pioneer of python, introduced python in the year 1980 and then implemented it in 1989. The intention behind the development of python was to make it open source language that can also be used for commercial projects. The fundamental principle of python is to write the code that is easy to use, highly readable and embrace writing fewer lines of code for achieving a particular task. One of the most popular standard libraries which have ready to use tools for performing a various work is Python Package Index. It was introduced in January 2016 and contains more than 72,000 packages for third-party software usage.

Python plays a critical role in linking data to customers. Recently python has found few entry barriers and many people have had access to have experienced the power of python in the past. So, what makes python the best language for big data analytics?

One of the reasons to choose python is that python ecosystem is very vibrant, the ratings at Redmonk are a proof of the strength python community. The Redmonk ranking is based on StackOverflow discussions and contribution made in Github to determine the popularity of programming language on the method used by users to ask questions about Python and the number of the open source projects contributions.

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.

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Python, a Zen Poem

A string in Python is enclosed in either single or double quotes.  Therefore, either one does the trick.  A common practice is to place single words with no characters that can be interpolated in single quotes and multi-word strings that contain interpolated characters in double quotes.  This may be a carry over from Perl where interpolated characters are in double quotes. 

If you do not want to interpolate a string, use a raw string ... r"\n".  With the exception of the last print statement, each of the print statements prints hello on a separate line from how are you?.  They are great for regular expressions.

Finally, triple double quotes """ some message about a function or class ... """ are used for docstrings.

print "hello \n how are you?"
print 'hello \n how are you?'
print r"hello \n how are you?"

The world of technology moves faster than the speed of light it seems. Devices are updated and software upgraded annually and sometimes more frequent than that.  Society wants to be able to function and be as productive as they can be as well as be entertained “now”.

Software companies must be ready to meet the demands of their loyal customers while increasing their market share among new customers. These companies are always looking to the ingenuity and creativity of their colleagues to keep them in the consumer’s focus. But, who are these “colleagues”? Are they required to be young, twenty-somethings that are fresh out of college with a host of ideas and energy about software and hardware that the consumer may enjoy? Or can they be more mature with a little more experience in the working world and may know a bit more about the consumer’s needs and some knowledge of today’s devices?

Older candidates for IT positions face many challenges when competing with their younger counterparts. The primary challenge that most will face is the ability to prove their knowledge of current hardware and the development and application of software used by consumers. Candidates will have to prove that although they may be older, their knowledge and experience is very current. They will have to make more of an effort to show that they are on pace with the younger candidates.

Another challenge will be marketing what should be considered prized assets; maturity and work experience. More mature candidates bring along a history of work experience and a level of maturity that can be utilized as a resource for most companies. They are more experienced with time management, organization and communication skills as well as balancing home and work. They can quickly become role models for younger colleagues within the company.

Unfortunately, some mature candidates can be seen as a threat to existing leadership, especially if that leadership is younger. Younger members of a leadership team may be concerned that the older candidate may be able to move them out of their position. If the candidate has a considerably robust technological background this will be a special concern and could cause the candidate to lose the opportunity.

Demonstrating that their knowledge or training is current, marketing their experience and maturity, and not being seen as a threat to existing leadership make job hunting an even more daunting task for the mature candidate. There are often times that they are overlooked for positions for these very reasons. But, software companies who know what they need and how to utilize talent will not pass up the opportunity to hire these jewels.

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