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Python was developed by Guido van Rossum in the late eighties and early nineties at the National Research Institute for Mathematics and Computer Science in the Netherlands.

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Python History and Versions

  • Python laid its foundation in the late 1980s.
  • The implementation of Python was started in December 1989 by Guido Van Rossum at CWI in Netherland.
  • In February 1991, Guido Van Rossum published the code (labeled version 0.9.0) to alt.sources.
  • In 1994, Python 1.0 was released with new features like lambda, map, filter, and reduce.
  • Python 2.0 added new features such as list comprehensions, garbage collection systems.
  • On December 3, 2008, Python 3.0 (also called "Py3K") was released. It was designed to rectify the fundamental flaw of the language.
  • ABC programming language is said to be the predecessor of Python language, which was capable of Exception Handling and interfacing with the Amoeba Operating System.Python is a programming language that lets you work quickly and integrate systems more efficiently.
  • The following programming languages influence Python:
    • ABC language.
    • Modula-3

Python First Release (1.x)

Python is a widely-used, interpreted, object-oriented, and high-level programming language with dynamic semantics, used for general-purpose programming. It was created by Guido van Rossum, and first released on February 20, 1991.

The first release (0.9.0) had features such as classes, exception handling, functions, and the core datatypes like list, dict, str, and so on. It was heavily inspired by ABC, a language that Guido spent some time implementing at CWI. While creating Python, his goal was to take the good parts of ABC while fixing the rest.

Python version 1.0 was released in January 1994. The major new features included in this release were the functional programming tools lambda, map, filter and reduce, which Guido Van Rossum never liked.

Six and a half years later in October 2000, Python 2.0 was introduced.This version of was more of an open-source project from members of the National Research Institute of Mathematics and Computer Science. This version of Python included list comprehensions, a full garbage collector, and it supported Unicode.

Python 2.x and Python 3.0

Starting from 2000, core developers started thinking about Python 3.0. They wanted to streamline the language, cutting unnecessary language constructs and functions that Python had accrued in its almost 20 years of existence. As the Zen of Python says: “There should be one—and preferably only one—obvious way to do it.”

Their efforts resulted in Python 3.0, a backward-incompatible version of the Python language that was released in December 2008. Unfortunately, the release brought some complications.

The developers hadn’t realized how much Python was used and how much of the Python code out in the wild depended on other Python libraries. Therefore, while it was easy to move one's scripts to Python 3, it was much harder to move programs that relied on third-party libraries since they didn’t upgrade that fast.

Future of Python

(1) Artificial Intelligence (AI)

Python programming language is undoubtedly dominating the other languages when future technologies like Artificial Intelligence(AI) comes into the play.

There are plenty of python frameworks, libraries, and tools that are specifically developed to direct Artificial Intelligence to reduce human efforts with increased accuracy and efficiency for various development purposes.

It is only the Artificial Intelligence that has made it possible to develop speech recognition system, autonomous cars, interpreting data like images, videos etc.

We have shown below some of the python libraries and tools used in various Artificial Intelligence branches.

  • Machine Learning- PyML, PyBrain, scikit-learn, MDP Toolkit, GraphLab Create, MIPy etc.
  • General AI- pyDatalog, AIMA, EasyAI, SimpleAI etc.
  • Neural Networks- PyAnn, pyrenn, ffnet, neurolab etc.
  • Natural Language & Text Processing- Quepy, NLTK, gensim

(2) Big Data

The future scope of python programming language can also be predicted by the way it has helped big data technology to grow. Python has been successfully contributing in analyzing a large number of data sets across computer clusters through its high-performance toolkits and libraries.

Let’s have a look at the python libraries and toolkits used for Data analysis and handling other big data issues.

  • Pandas
  • Scikit-Learn
  • NumPy
  • SciPy
  • GraphLab Create
  • IPython
  • Bokeh
  • Agate
  • PySpark
  • Dask

(3) Networking

Networking is another field in which python has a brighter scope in the future. Python programming language is used to read, write and configure routers and switches and perform other networking automation tasks in a cost-effective and secure manner.

For these purposes, there are many libraries and tools that are built on the top of the python language. Here we have listed some of these python libraries and tools especially used by network engineers for network automation.

  • Ansible
  • Netmiko
  • NAPALM(Network Automation and Programmability Abstraction Layer with Multivendor Support)
  • Pyeapi
  • Junos PyEZ
  • PySNMP
  • Paramiko SSH 
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