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Python is termed as a multi-paradigm language used for programming and can be perceived as a Swiss army knife in the field of coding.

It can support OOP, functional programming patterns, and structured programming, among several other things.

There is a saying popularly used in the community of Python, and it goes like this: Python is normally the 2nd best language for all purposes.

But this isn’t a knock for organizations faced with the dilemma of using the best of the breed solutions as they soon find themselves burdened with codebases that are unmaintainable and incompatible.

Python is capable of handling all the jobs from data mining to website building to the running of embedded systems.

For instance, in the case of ForecastWatch, Python was utilized for writing a parser for harvesting forecasts from other sites.

It is also used for an aggregate engine that compiles the data and the website code for displaying results.

It was PHP, which was originally utilized for building the website until the organization realized that it was a lot easier to deal with a single language for everything.

Facebook also selected Python for data analysis because it was being used a great deal for other portions of the organization.

The name Python is derived from the popular rock band Monty Python.

The creator of the Python programming language, Guido Van Possum, chose this name to suggest that its use would be fun.

You will find many obscure Monty Python sketches, which are referenced in the code samples used in Python and those used for documentation.

For these reasons, this is a cherished programming language among programmers.

The data scientists with scientific or engineering backgrounds might feel like a barber armed with an ax when they use the language for the first time for data analysis - out of place.

However, the inherent simplicity and readability of Python make it comparatively easy to pick, and the quantity of devoted analytical libraries available nowadays means that data scientists in all sectors can find packages tailored for their needs, easily available on the net for downloads.

Due to the general nature of Python and its extensibility, it was inevitable that as the popularity of the language went into an orbit, its use in the field of data science became a foregone conclusion.

As a matter of fact, Python is a - jack of all trades - program, and it isn’t particularly well-suited for statistical analysis.

However, several companies have invested in Python, realizing the advantages of using a standardized language and extending it for those purposes.

Similar to other programming languages, the main reason for the success of Python is the libraries.

There are around 72,000 of them available with the PyPi (Python Package Index), and the number is constantly rising.

Python is specifically designed to possess stripped-down and lightweight core, and its standard library is built by using tools that can be utilized in all programming tasks.

Python comes with a “batteries included” philosophy that allows its users to get down to the issue of finding solutions to problems quickly without having to go across many competing function libraries.

There Is Always Someone Available to Help in the Python Community

There are many great things about Python, and one of them is the broad and diverse base of millions of Python users all across the globe who are ready to offer suggestions and advice when you’re stuck on something.

There is a very good chance that someone else was stuck on the same problem before you.

These open source communities are extremely popular due to their open discussion attitude.

However, some of them are pretty fierce about not allowing newcomers to mix easily.

These Python experts are happy to aid you, both online and during the local meet-ups.

Chances are, you will stumble into several intricacies of learning a new programming language.

As Python plays such a vital role in the data science community, you can find several resources that are specific to the use of Python in the data sciences.

These meet-up groups of data scientists who use Python are prevalent all across the US, especially in places such as Los Angeles and Seattle.

In case you’re having trouble locating a meet-up group near you that has the right qualifications, there is a data science hack that uses Python for searching these meet-up groups to find the perfect match.