Python is an open-source high-level programming language catered to general-purpose programming. It is instrumental for both IT-related projects as well as the analytical ones. As far as, its history is concerned, Python was conceived in the year 1980 by Guido Rossum at Centrum Wiskunde and Informatica (CWI) institute as a worthy successor to the ABC language. ABC language was itself a powerful language during the 90s, capable of handling and interfacing with the Amoeba operating system. Python was initially executed in December 1989.

A Basic Overview

Python is a multi-paradigm programming language. It supports both, object-oriented programming as well as structured programming. Many of its operations support functional and aspect-oriented programming.

Rather than having all of its functionalities constructed into its core, Python was designed to be highly extensible. For starters, Van Rossum’s created Python with the vision of building a small startup language coupled with a large library of packages and easily extensible interpreter.

Python Versions

Python 2.0 was released in 2000 with many major changes, including a garbage collector and support for Unicode.

The next version, 3.0 was published in 2008. It was a major update of the language that was not completely backward-compatible during its application. Many of its major features were reverse-ported to Python 2.6 and 2.7 version series.

Currently, Python 3.7.1 is the latest stable platform which everyone from freshers to experienced professionals is using.


Python is an open-source language. Naturally, it has been ported to many platforms. All python programs will work on any of these platforms without requiring to make any major or minor changes in the code.
Python can be used on Linux, Windows, Macintosh, PlayStation and can also be operated on PocketPC!

  • Freely available and open source

Python is free of cost and is available for distribution in the open. One can make duplicates of this software, hack the software’s source code and make changes to it. This is one of the reasons why Python is so clean and consistent, it has been guarded and looked after by a community who just want to see a better and improved version of python daily!

  • Support

Due to its vast online community, python is marketed as the best programming language till date. Its constant support and regular updates for users and administrators make it dependable and easy to use over other languages.

  • Easy to master

Compared to Java or any other languages, it is easy to learn and grasp python at the beginners level. For those who want to build applications and deploy them on the cloud, python is the preferred language.

  • Dynamic

Python is a strong-dynamically typed language. Dynamically typed means the variables are taken care and won’t conflict with each other.

To keep things simple and interesting as the language itself, take a look at the Infographic on Python Programming Language.


Indentation Python utilizes whitespace indentation and not curly brackets or keywords to limit the code blocks. An increase in indentation will only occur after certain statements; a decrease in indentation indicates the end of the existing code block.

The program’s visual structure represents the semantic structure of the code. This feature is also sometimes called as the off-side rule.

Statements and control flow
We work with control flow and statements all the time for input for sort of algorithm. Let’s see the common ones.

If statement- It executes a block of code based on a condition, along with else and else-if

For statement- It creates an iteration over an iterable object, storing each element to a local variable for use by the predefined block.

While statement- It performs the block of code as long as the predetermined condition is true.

Try catch statement- It allows the exceptions to be raised in its code block to be caught and handled by except clauses.

Raise statement- It is used to raise a particular exception or raise it again to a catch an exception.


Addition, subtraction, multiplication, and division operations can be easily performed in python. The division is a bit different compared to others. It has two types. They are floor division and integer division.
Python uses the ** operator for exponentiation function.

From Python 3.5, the new ‘@’ operator was introduced. It is called infix. It is intended to be used by the libraries like NumPy for matrix multiplication in machine learning.

Python also incorporates the words and, or, not for its boolean operators rather than the symbols- &&, ||, ! which are used in Java and C.

We don’t require to declare integers after importing the libraries in python. Specifying the integer and its datatype is mandatory in C and Java.


Methods are like functions attached to the object’s class. The syntax is quite simple and is dignified by instance.method(argument) for normal methods and functions. Python has an inbuilt engine to access instance data, compared to the implicit self (or this) in some other object-oriented programming languages.


Python allows avid coders to define their own typing techniques and methods using classes, often instrumental for object-oriented programming. Different instances of classes can be created by calling the class, for instance, the SpamClass().
As python uses duck typing, it consists of both, typed objects and untyped variable names.


Tons of libraries are available for surplus functionality, to say the least. Pythons biggest strength lies in its toolkits and libraries. It includes libraries for creating graphical user interfaces(GUI), establishing a connection to relational databases, generating pseudo-random numbers, performing arithmetic operations with arbitrary precision decimals, changing regular expressions, plus unit testing.


There are some neat implementations in python
CPython is the primary implementation of Python. It is written in C, having the C89 standard and a few C99 features. It compiles all the python programs to an intermediate bytecode which is later executed by its virtual machine. Windows and most modern Unix-like systems support this implementation.

After going through the tiring process of understanding and decoding python, we can take a break from the technical stuff and focus on the good part! These are some of the factors why python is way ahead of its competition.

Applications Of Python in Data Science

Applications/Projects that are currently hot in python

  • Wine Quality Prediction

This is one of the most demanded datasets for data science beginners. It is divided into 2 datasets. Users have the liberty to perform both regression and classification on this data. It will test your understanding of basic machine learning, such as outlier detection and feature selection. There are 4898 rows and 12 columns in this dataset.

  • Loan Prediction

The insurance domain has one of the largest uses of analytics and data science methods in their field. This dataset will provide you with a basic understanding of working on data sets from insurance companies — what challenges these companies are currently facing, what game-changing strategies are been implemented, which outcome variables are suitable for their operation etc. It is a pure classification problem. The data has 615 rows and 13 columns.

  • Time Series Analysis

Time Series is one of the most commonly used phenomena in data science. It has a wide variety of applications ranging from weather forecasting, predicting sales to analyzing year on year trends, etc. This dataset is specific to the time series and the challenge here is to forecast traffic on a mode of transportation.

  • Twitter Classification

Scrapping and working with the Twitter data has become a mandatory part of sentiment analysis problems. If you want to understand the working of this kind of unstructured data, definitely you will have fun working on the challenge this dataset poses. The dataset is 3MB in size and has 31,962 tweets.

  • Recommendation Engine

In this practice problem, the users are introduced with the data of programmers and questions that they previously solved, along with the time that they took to solve those questions. As a data scientist, the model you build will help online judges to decide the next level of questions to recommend to a user.

  • Handwritten Recognition using MNIST dataset

This is used for natural language processing. Dataset consists of total 60,000 in training model and 10.000 in the test model. The algorithm recognizes the human language and demonstrated the same with the use of SVM and Tensorflow.

Apart from these, Python is used in other domains too. Take a look at other applications of Python Programming Language here.

In the meantime, if you are interested in understanding and learning Python then you can opt for a Superb Python course giving you the complete knowledge about all the concepts.

Originally published at on January 30, 2019.

Likes to read & write about technology