Init Python

Lets Init Python 

With the rapid development in technology we are witnessing wide range of improvements in our day to day lives. It is surely a 100 % gift to everyone as the technology intends to enhance the research, quality and security of life in the universe. In this exponential evolution of technology, software programming languages plays a very crucial role in designing and developing various types of applications related to different sciences. Of all the popular programming languages, Python created by Guido van Rossum is one of the best for the past decade. So, Lets init python. 

In this article, we will go through the installation, interacting with python interpreter and various applications of the language.

Installation

Python is an open source and licensed under the PSF (Python Software Foundation) agreement. Most of the earlier versions, but not all, are also GPL-compatible.

Newer versions are dual licensed under PSF and the Zero-Clause BSD license.

Downloading and installing python is really easy, you can refer here to download the software.

In windows we can install using the python installer, where as in Linux systems we will get the software by default.

You can check the version using below command

python --version

If you want to install specific version in linux env you can do so using apt.

Alternate implementations like IronPython, Jython, MicroPython etc are available. The default installation will install the traditional implementation of python i.e CPython.

Python Interpreter

You can invoke the interpreter by typing python in the command shell like shown below. This shows the python installation is successful.

After invoking the interpreter, you can now write simple programs to test or interact with your application by importing the modules.

Applications of Python

Before diving into the applications of python, I would like to point out some important aspects of python which makes it a programming language it is today.

Programming in python is really simple and elegant. Here is a simple program using python. 

def greet(name):
    print(f"Hello {name}")
greet("World")

A lot is going on in the above three lines of code. We are defining a function, passing arguments, filling arguments in a string using special kind of formatter (F Strings), printing a string and calling the function. All of this is happening in those three lines of code.

It has a very good community and numerous libraries are out there to help you build your applications in less time.

It is a dynamically typed language.

Easy to learn and you can start coding with in no time.

There are numerous applications of python programming language. I will try to cover as much as possible here. We are going to look at the popular application domains and their respective frameworks, libraries or tools.

Popular domains that python rules these days are as follows

  • Desktop Application Development
  • Web Application
  • Data Science
  • Machine Learning
  • Artificial Intelligence
  • many more…

Now let us see some of the popular frameworks that are useful for above mentioned domains.

Desktop Applications

Most popular frameworks are PyQt5, TKinter, Qt for Python You can create the graphical interfaces using these frameworks for any desktop application.

Web Applications

The popular frameworks to build web applications are Django, Flask. You can kick start the designing of web applications using these in a very short span of time. Clear documentation with numerous inbuilt features helps you run a production grade application within no time.

Data Science

With the wide range of business applications and opportunities in this popular field everyone is very much interested in making a career.

Python has good community and numerous libraries which makes it as a favorite goto language for data scientists.

Three major process involved are Data Mining, Data Processing or Modeling and Data visualization. I am not going in depth explanation for these, it deserves a separate article of its own. Popular libraries are mentioned here.

  • Scrapy
  • BeautifulSoap
  • NumPy
  • Pandas
  • Matplotlib

Machine Learning

As the name suggests it means machines that are learning from training data either from a structured or unstructured data. The model created will evolve exponentially with the increase in the number of training sets. Organizing and processing the data by understanding and analyzing it will give apt results.

  • Tensorflow
  • PyTorch
  • OpenCV

Let us dive into the Python World!

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