It is hard not to wonder how current technology would have altered the events surrounding the tragic death of John F. Kennedy. On the afternoon of November 22, 1963, shots rang out in Dallas, TX, taking the life of JFK, one of the most beloved Americans. Given the same circumstances today, surely the advances in IT alone, would have drastically changed the outcome of that horrible day. Would the government have recognized that there was a viable threat looming over JFK? Would local and government agencies have been more prepared for a possible assassination attempt? Would the assortment of everyday communication devices assisted in the prevention of the assassination, not to mention, provided greater resources into the investigation? With all that the IT world has to offer today, how would it have altered the JFK tragedy?

As many conspiracy theories have rocked the foundation of the official story presented by government agencies, realization of the expansive nature of technology provides equal consideration as to how the event would have been changed had this technology been available during the time of the shooting. There were T.V. cameras, home 8mm recorders, even single shot-hand held cameras snapping away as the car caravan approached. Yet, there remains little documentation of the shooting and even less information pertaining to the precautions taken by officials prior to JFK's arrival. Theorists consider these possibilities along with how the world would have turned out had the great John F. Kennedynever been assassinated on that day.

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?"

Java still has its place in the world of software development, but is it quickly becoming obsolete by the more dynamically enabled Python programming language? The issue is hotly contested by both sides of the debate. Java experts point out that Java is still being developed with more programmer friendly updates. Python users swear that Java can take up to ten times longer to develop. Managers that need to make the best decision for a company need concrete information so that an informed and rational decision can be made.

First, Java is a static typed language while Python is dynamically typed. Static typed languages require that each variable name must be tied to both a type and an object. Dynamically typed languages only require that a variable name only gets bound to an object. Immediately, this puts Python ahead of the game in terms of productivity since a static typed language requires several elements and can make errors in coding more likely.

Python uses a concise language while Java uses verbose language. Concise language, as the name suggests, gets straight to the point without extra words. Removing additional syntax can greatly reduce the amount of time required to program.  A simple call in Java, such as the ever notorious "Hello, World" requires three several lines of coding while Python requires a single sentence. Java requires the use of checked exceptions. If the exceptions are not caught or thrown out then the code fails to compile. In terms of language, Python certainly has surpassed Java in terms of brevity.

Additionally, while Java's string handling capabilities have improved they haven't yet matched the sophistication of Python's. Web applications rely upon fast load times and extraneous code can increase user wait time. Python optimizes code in ways that Java doesn't, and this can make Python a more efficient language. However, Java does run faster than Python and this can be a significant advantage for programmers using Java. When you factor in the need for a compiler for Java applications the speed factor cancels itself out leaving Python and Java at an impasse.

While a programmer will continue to argue for the language that makes it easiest based on the programmer's current level of knowledge, new software compiled with Python takes less time and provides a simplified coding language that reduces the chance for errors. When things go right, Java works well and there are no problems. However, when errors get introduced into the code, it can become extremely time consuming to locate and correct those errors. Python generally uses less code to begin with and makes it easier and more efficient to work with.

Ultimately, both languages have their own strengths and weaknesses. For creating simple applications, Python provides a simpler and more effective application. Larger applications can benefit from Java and the verbosity of the code actually makes it more compatible with future versions. Python code has been known to break with new releases. Ultimately, Python works best as a type of connecting language to conduct quick and dirty work that would be too intensive when using Java alone. In this sense, Java is a low-level implementation language. While both languages are continuing to develop, it's unlikely that one language will surpass the other for all programming needs in the near future.

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.