21.1 Introduction to Python

Introduction of Python Data Science

Python is an interpreted, object-oriented, high-level programming language with dynamic semantics. Its high-level built in data structures, combined with dynamic typing and dynamic binding, make it very attractive for Rapid Application Development, as well as for use as a scripting or glue language to connect existing components together. Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance. Python supports modules and packages, which encourages program modularity and code reuse. The Python interpreter and the extensive standard library are available in source or binary form without charge for all major platforms, and can be freely distributed.

Some benefits to using Python include:

• Free - Python is open source software and is therefore free to use without a licensing fee

• Data Science Libraries - The reason for growing success of Python is the availability of data science libraries for aspiring candidates. These libraries have been upgraded continuously. Some important libraries including Numpy, Pandas, Matplotlib, SciPy, and Scikit-learn

• Scalable - Python is a more scalable than R and is faster to use than Matlab and Stata. Skilled data scientists in various industries use this language to develop various types of applications successfully.

• Easy to Learn - Compared to other languages Python is easy to learn even for non-programmers. It makes an ideal first language due to its ample learning resources, readable code and large community.

• It's a Programming Language - As a general purpose programming language, Python is universal. It is a quick, but powerful tool with plenty of capabilities. It gives you an opportunity to build your machine learning models, web applications, and anything else you need in one language.