Python is an open-source, interpreted, high-level language and provides a great approach for object-oriented programming. It is one of the best languages used by data scientists for various data science projects/applications. Python provides great functionality to deal with mathematics, statistics and scientific function. It provides great libraries to deal with data science applications.

One of the main reasons why Python is widely used in the scientific and research communities is because of its ease of use and simple syntax which makes it easy to adapt for people who do not have an engineering background. It is also more suited for quick prototyping.

Python is essential for Data Science because of the following reasons: –

• It’s Flexible

It’s ideal for developers who want to script applications and websites.

• It’s Easy to Learn

It boasts a gradual and relatively low learning curve. This ease of learning makes Python an ideal tool for beginning programmers. Python offers programmers the advantage of using fewer lines of code to accomplish tasks than one needs when using older languages.

• It’s Open Source

Python is open-source, which means it’s free and uses a community-based model for development. Python is designed to run on Windows and Linux environments. Also, it can easily be ported to multiple platforms. There are many open-source Python libraries such as Data manipulation, Data Visualization, Statistics, Mathematics, Machine Learning, and Natural Language Processing, to name just a few.

• It’s Well-Supported

Python has a large following and is heavily used in academic and industrial circles, which means that there are plenty of useful analytics libraries available. Python users needing help can always turn to Stack Overflow, mailing lists, and user-contributed code and documentation. And the more popular Python becomes, the more users will contribute information on their user experience, and that means more support material is available at no cost. This creates a self-perpetuating spiral of acceptance by a growing number of data analysts and data scientists.