Python Programming is one of the most popular languages and an old language in fact introduced before Java programming. After the advent of data science, Python programming has become the most popular language among all the other languages.

Python Programming is one of the popular languages in the current generation. Intial programming languages like C, C++ and JAVA is being replaced with Python now, in many educational courses. It is an interpreted and high level programming language.

Python is an object-oriented, open-source, adaptable and simple to learn. It has a rich arrangement of libraries and tools that makes the assignments simple for Data scientists. Python is simplified from the traditional programming to keep every syntax simple and understandable.

Python is a programming language that lets you work quickly and integrate systems more effectively. 

Python Programming Origin:

Python was introduced in the late 1980s by Guido van Rossum at Centrum Wiskunde & Informatica (CWI) in the Netherlands as a successor to ABC programming language, which was inspired by SETL (High-level programming language based on the mathematical theory of sets), capable of exception handling and interfacing with the Amoeba operating system. Its implementation began in December 1989.

Guido van Rossum

His idea was to write a lite programming which contains OOPS, which contains both language and script in the same concept. A language that behaves as scripting will surely have an upper hand.

Python Programming Versions:

Python 2.7 is the most popular version, which was started using from the earlier 2000's. Python 2.7 is still going strong. Later in 2015, python.org decided to release it's upcoming versions, to increase it's usability and eventually it made the users to get increased in using this language.

Difference between high level and low level programming:

Both high level and low level are, programming language types. The main difference between high level language and low level language is that, Programmers can easily understand or interpret or compile the high level language in comparison of machine. On the other hand, Machine can easily understand the low level language in comparison of human beings.

Difference between interpreter and compiler:

Basic difference is, you need to compile the natural language. High language to machine language. In this process, you will be converting the programming language into a machine level, understandable code. If you end up making mistakes, if the compiler fails to convert the code to low level, it throws an error which is normally called as compilation error. Basically occurs due to the compiler fails to continue with the written programming code.

Where as interpreter says, whatever you write on the terminal, I will directly transfer to the machine language. At every line it will convert and give the machine level code to the hardware(Machine). Normally interpreter doesn't follow the procedure that compiler follows. So by the above definitions, Python is flexible, convenient and can run on any platform effectively. It is adaptable and can be integrated with other third-party software effectively.

Why Python Programming?

Python in Data science has empowered the data scientists to accomplish more in less time. Python is an adaptable programming language that can be effectively understood and is exceptionally amazing as well.

Python is highly adaptable and can work in any environment effectively. Additionally, with negligible changes, it can run on any operating system and can be integrated with other programming languages. These qualities have settled on Python the top choice for developers & data scientists.

Available on various community base:

Python has a big community base of engineers and data scientists like Python.org, Fullstackpython.com, realpython.com, etc. Python developers can impart their issues and thoughts to the community. Python Package Index is an extraordinary place to explore the different skylines of the Python Programming language. Python developers are continually making enhancements in the language that is helping it to turn out to be better over time.

10 Best Reasons to use Python Programming Language:

1. Python has a lot of packages like Tensorflow, Keras, and Theano that is assisting data scientists with developing deep learning algorithms. Python gives superior help with regard to deep learning algorithms.
2. Deep learning algorithms were inspired by the human brain architecture. It manages to build artificial neural networks that reenact the conduct of the human mind. Deep learning neural networks give weight and biasing to different input parameters and give the desired output.
3. Python builds better analytics tools. Data analytics is a necessary part of data science. Data analytics tools give information about different frameworks that are important to assess the performance in any business.
4. Python not only lets you create software but also enables you to deal with the analysis, computing of numeric and logical data, and web development.
5. Indeed, Python has additionally become omnipresent on the web, controlling various prominent websites with Web development frameworks like TurboGears, Django, and Tornado.
6. Python is easy to use and has a fast learning curve. New data scientists can easily learn Python with its simple to utilize syntax and better comprehensibility.
7. Python is significant for data scientists since it has many useful and easy to use libraries like Pandas, Numpy, Scipy, Tensorflow, and many more.
8. Python, also gives a lot of data mining tools that help in better handling of the data. For example, Rapid Miner, Weka, Orange, and so on.
9. Data Scientists need to manage a large amount of data known as big data. With simple utilization and a huge arrangement of python libraries, Python has become a popular choice to deal with big data.
10. Data science consulting organizations are empowering their group of developers and data scientists to utilize Python as a programming language.

Major Features of Python Programming:

• Python is a progressively typed language, so the variables are defined automatically.
• Python is more readable and uses lesser code to play out a similar task when contrasted with other programming languages.
• Python is specifically typed. In this way, developers need to cast types manually.
• Python is an interpreted language. This implies the program need not have complied.
• Python is flexible, convenient and can run on any platform effectively. It is adaptable and can be integrated with other third-party software effectively.

Check  the next chapter to know how to install the Anaconda & popular IDE's to practice your Python Programming Language. Do comment your opinion and share more features about Python here.