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?"

As developers we are overwhelmed with the number of language choices made available to us. It wasn't so long ago that C and it's object oriented sibling C++ where the mainstay of any programmer. Now though we have languages which make certain tasks so easy and simple that we simply cannot afford to ignore them.

In this article we are going to look at the overall differences between Python, Perl and TCL. All formidable and worthy in their own right, but each one has been designed to suit a specific programming need.

1)– Perl is the most mature out of the three languages we are looking at in this article. It was originally designed for processing textual data, and it does so extremely well. Of course Perl has grown over time and can be used for a multitude of different programming scenarios.

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.

Although the index is an important itemized guide of what people are searching for on the internet, it’s arguable that certain languages getting recognition is a direct result of early adopters posting tutorials and filling up discussion boards on current trends. Additionally, popular tech blogs pick up on technological shifts and broadcast related versions of the same themes.

When does the popularity of a software language matter?

1. If you want marketable skills, knowing what employers are looking for is beneficial. As an example, languages such as Java and Objective C are highly coveted in the smart-phone apps businesses.
2. A consistently shrinking language in usage is an indicator not only that employers are apt to pass on those skills but fall in danger of being obsolete.
3. Focusing on languages that are compatible with other developers increases your chances to participate on projects that companies are working on.