Outsourcing IT needs in the corporate world has become extremely popular because it is cost-efficient and it gives IT resources to companies that may not otherwise be able to afford them. Another positive side effect of IT outsourcing is that it has brought many technology jobs back to the United States.

As convenient and patriotic as IT outsourcing has become, it also have several limitations that have caused many companies to re-think the idea of funding their own internal IT group. It is important for a company to be completely familiar with these limitations before developing any kind of company policy in regards to IT.

The Customer Could Outgrow The Outsource Company

When an IT outsourcing company first takes on a new client, the relationship is beneficial to both sides. But things can start to get inconvenient for the client when the client's business starts to outgrow the capabilities of the IT outsource group.

An IT outsourcing company can become entrenched in the daily routine of its clients, which is great at first. But when the IT company can no longer keep pace with the growth of its clients, then the clients are stuck trying to find a new solution and keep track of its own IT assets at the same time.

The IT Outsourcing Company Lacks The Necessary Technical Expertise

IT outsourcing clients like to believe that their support company knows everything there is to know about computers and the Internet. But every IT support company has its areas of expertise and they have the technical areas where they are not quite as strong. If the client starts to experience needs from the areas where the IT outsourcing company is not so strong, then that can become a significant business issue.

This problem can be amplified if the client is a small business experiencing growth. An IT outsourcing company is not as likely to bring on new personnel for a smaller client, which leaves the client without a solution.

The Client Losses A Measure Of Control Over Its Data

No matter how large or successful an IT outsourcing company may be, there will always be the limitation of client security and the protection of critical customer data. All it takes is one rogue employee of the IT outsourcing company to compromise all of the client's critical data.

Some IT outsourcing companies have safeguards put in place to try and prevent client data compromise, but those safeguards are limited by how much access the IT company has to the client network. In most cases, that access has to be comprehensive for the IT outsourcing company to be able to do its job.

Outsourcing IT responsibilities can take a lot of stress off of a client and allow that client to operate his business by focusing on his core competencies. But there are limitations to IT outsourcing that could make it necessary for a client to do his own IT support and pay the extra costs.

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

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?"

Python and Ruby, each with roots going back into the 1990s, are two of the most popular interpreted programming languages today. Ruby is most widely known as the language in which the ubiquitous Ruby on Rails web application framework is written, but it also has legions of fans that use it for things that have nothing to do with the web. Python is a big hit in the numerical and scientific computing communities at the present time, rapidly displacing such longtime stalwarts as R when it comes to these applications. It too, however, is also put to a myriad of other uses, and the two languages probably vie for the title when it comes to how flexible their users find them.

A Matter of Personality...

That isn't to say that there aren't some major, immediately noticeable, differences between the two programming tongues. Ruby is famous for its flexibility and eagerness to please; it is seen by many as a cleaned-up continuation of Perl's "Do What I Mean" philosophy, whereby the interpreter does its best to figure out the meaning of evening non-canonical syntactic constructs. In fact, the language's creator, Yukihiro Matsumoto, chose his brainchild's name in homage to that earlier language's gemstone-inspired moniker.

Python, on the other hand, takes a very different tact. In a famous Python Enhancement Proposal called "The Zen of Python," longtime Pythonista Tim Peters declared it to be preferable that there should only be a single obvious way to do anything. Python enthusiasts and programmers, then, generally prize unanimity of style over syntactic flexibility compared to those who choose Ruby, and this shows in the code they create. Even Python's whitespace-sensitive parsing has a feel of lending clarity through syntactical enforcement that is very much at odds with the much fuzzier style of typical Ruby code.

For example, Python's much-admired list comprehension feature serves as the most obvious way to build up certain kinds of lists according to initial conditions:

a = [x**3 for x in range(10,20)]
b = [y for y in a if y % 2 == 0]

first builds up a list of the cubes of all of the numbers between 10 and 19 (yes, 19), assigning the result to 'a'. A second list of those elements in 'a' which are even is then stored in 'b'. One natural way to do this in Ruby is probably:

a = (10..19).map {|x| x ** 3}
b = a.select {|y| y.even?}

but there are a number of obvious alternatives, such as:

a = (10..19).collect do |x|
x ** 3
end

b = a.find_all do |y|
y % 2 == 0
end

It tends to be a little easier to come up with equally viable, but syntactically distinct, solutions in Ruby compared to Python, even for relatively simple tasks like the above. That is not to say that Ruby is a messy language, either; it is merely that it is somewhat freer and more forgiving than Python is, and many consider Python's relative purity in this regard a real advantage when it comes to writing clear, easily understandable code.

And Somewhat One of Performance