Python Fundamentals

Python is a popular object-oriented programming language having the capabilities of a high-level programming language. It’s easy to learn syntax and portability capability makes it popular these days.

The followings facts give us the introduction to Python −

· Python was developed by Guido van Rossum at Stichting Mathematisch Centrum in the Netherlands.

· It was written as the successor of a programming language named ‘ABC’.

· Its first version was released in 1991.

· The name Python was picked by Guido van Rossum from a TV show named Monty Python’s Flying Circus.

· It is an open-source programming language which means that we can freely download it and use it to develop programs. It can be downloaded from www.python.org.

· Python programming language is having the features of Java and C both. It is having the elegant ‘C’ code and on the other hand, it is having classes and objects like Java for object-oriented programming.

It is an interpreted language, which means the source code of the Python program would be first converted into byte code and then executed by a Python virtual machine.

Why Python for Data Science?

Python is the fifth most important language as well as the most popular language for Machine learning and data science. The following are the features of Python that makes it the preferred choice of language for data science.

An extensive set of packages

Python has an extensive and powerful set of packages that are ready to be used in various domains. It also has packages like NumPy, scipy, pandas, scikit-learn, etc. which are required for machine learning and data science.

Easy prototyping

Another important feature of Python that makes it the choice of language for data science is the easy and fast prototyping. This feature is useful for developing a new algorithm.

Collaboration feature

The field of data science basically needs good collaboration and Python provides many useful tools that make this extremely.

One language for many domains

A typical data science project includes various domains like data extraction, data manipulation, data analysis, feature extraction, modeling, evaluation, deployment, and updating the solution. As Python is a multi-purpose language, it allows the data scientist to address all these domains from a common platform.