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Python Programming Language: Specifics, Advantages, And Application In Business Niches

According to the TIOBE Index for October 2022, Python continues to rank first. Python is in demand in education, research, entertainment, and business.

What is Python?

Python is a very popular high-level programming language. Its wide usage is based on simple synaxis and adaptation to different tasks. The language allows the Python app development company to focus on the essence and goals rather than specific steps and procedures. For this reason, writing code in Python is faster than in many other languages. Code on which you do not need to compile, but you can immediately run the program.

Benefits of the Python programming language

First, the Python interpreter makes it easy to test and move small blocks of code from platform to platform. Compatibility with most existing operating systems makes Python a universal programming language.

Secondly, it helps to learn how to program and write code, which is used as a tool for various scientific works.

Thirdly, Python programs are understandable to IT industry professionals.

But the most important thing is that you can write large commercial applications in Python. And there is confirmation of this. Many successful companies, universities, and research centers have chosen the Python language: Google, Instagram, Facebook, Netflix, Spotify, Uber, Pixar, Intel, Cisco, IBM, MIT, and NASA.

Notable companies that have used Python

  • Google corporation has been using Python in development since its inception. For example, programmers wrote almost the entire YouTube on it.
  • Instagram. Initially, the back end of the Instagram website was written entirely in Django. Today, the social network remains to work on this framework, albeit with several innovations.
  • Facebook. Python is responsible for several infrastructure management services for the world’s largest social network.
  • Spotify. The music streaming giant uses Python in the backend and for data analysis to provide suggestions and recommendations to users. A well-known streaming monopolist has written his recommendation service in Python from scratch. The programming language was also used in developing some other systems, such as a central notification gateway and a content delivery network.
  • Reddit. One of the most popular in IT in the US was initially written in Lisp, but six months after its launch, the owners decided to rewrite it in Python.
  • Uber. The company founders chose between Ruby and Python and settled on the latter. The service backend is written in Python. Also, using the programming language in Uber, they carry out asynchronous programming, predict supply and demand, and conduct data analytics.

Python libraries

Python is involved in various areas of web development, visual services, games, mobile applications, desktop applications, testing, databases, complex calculations, big data, machine learning, system programming and administration, and business process automation.

Python developers have been able to make custom libraries for just about any purpose. There have been several well-known libraries and frameworks for developing applications of various complexity.

The main difference between a framework and a library is that the former is a ready-made tool to run you need to add the logical structure of the program.

On the other hand, libraries are separate modules the developer connects to his code, thereby introducing new features to his project.

Given the fact that today the total number of libraries in Python is approaching 140,000, it is simply not possible to talk about each of them within the frame of one article. You can identify the most famous.

Django, Flask, and Falcon frameworks

Django, and Flask, and Falcon frameworks simplify the process of generating HTML pages that the user sees in his browser, database queries, and address processing. Falcon is used in their technology stacks by such big IT players as LinkedIn, OpenStack, and RackSpace. Flask is used by big companies like LinkedIn and Pinterest.

Dabo and PythonCard

In the field of graphics, many tasks are also solved using the Python programming language. Suppose you need to adapt the created graphical interface to the style of the operating system where the application will run. In that case, you can use Python with additional graphical libraries, Dabo and PythonCard, which will significantly simplify the development process.


To create desktop applications with a graphical user interface. Suitable is PyQt, which contains a GUI builder and allows you to add new GUI controls.

Database Libraries

With Python, you can interact clearly and easily with any database. The working environment of the language itself contains a programming interface for working with databases directly in the syntax using SQL queries.

NumPy, Matplotlib and SciPy

NumPy facilitates array math and vectorization. This dramatically improves performance and, accordingly, speeds up the execution time.

Matplotlib plots a variety of plots and is an add-on for other advanced Python plotting libraries.

SciPy has various modules for common scientific programming tasks such as linear algebra, integration, calculus, ordinary differential equations, and signal processing.

TensorFlow and Scikit-learn

Machine learning actively uses TensorFlow and Scikit-learn. With their help, they create functionality for machine learning, voice, and face recognition systems. They are used by deep learning specialists and creators of neural networks. Giants like Google, Coca-Cola, Airbnb, Twitter, Intel, and DeepMind all use TensorFlow!

Launched to the world as a Google Summer of Code project, Scikit Learn is a robust machine-learning library for Python. It includes ML algorithms.


Pandas is the perfect data manipulation tool. It is designed for fast and easy data processing, reading, aggregation, and visualization. Pandas is a library for learning Python for Data Science.

Python in Big Data

In addition to Data Science, Python is in demand in Big Data because of its easy readability and statistical analysis capacity.

Big Data is the most valuable commodity in the modern era. The amount of data generated by companies is increasing at a rapid pace.

Python is high-speed data processing language, so it is suitable for working with Big Data. Python codes are executed in a fraction of the time other programming languages need because of their simple syntax and easy-to-manage code. It supports various prototyping ideas, making code run faster while maintaining excellent transparency between code and execution. This consistently makes Python one of the tech industry’s most popular options for Big Data.

Niches that use Python

Python programming language is used in a variety of areas of life. Including in the entertainment and tourism industry, as well as in healthcare. The most famous companies in this regard are the following:

  • Netflix. This company uses over 1,300 recommendation clusters based on consumer viewing preferences to provide a personalized experience. Netflix collects user data, such as watch time, rewatch, rewind, search keywords, and more. Using this data, Netflix can predict what a viewer is likely to watch and provide the user with a personalized watchlist.
  • Pfizer. Healthcare providers need big data analytics to track and optimize patient flow, track equipment and drug usage, and organize patient information. In addition, machine learning techniques help pharmaceutical companies better predict the demand for vaccines and medicines and distribute them efficiently.
  • Airbnb. Using data as the voice of its customers, Airbnb captures a wealth of customer feedback, provides input to understand community trends, evaluates user experience, and uses these analytics to make informed decisions to build a better business model. Airbnb helps people find “local experiences” in a location through search algorithms that make searches and listings accurate.

Python is popular in almost all spheres of life in the modern world. Due to its versatility, this programming language will be in demand in the future.

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