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Vijay Singh Khatri
Vijay Singh Khatri

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Why is Python Programming for Data-driven Enterprises?

Why is Python Programming for Data-driven Enterprises
The COVID-19 pandemic and the continuous cycles of lockdown have transformed the way businesses and companies would perform their operations in the past. This wave of digital transformation has helped various data-driven sectors, such as insurance companies, to ensure maximum customer satisfaction, boost productivity, and reduce various operation costs. This is all because of software applications.

These days, enterprises are heavily relying on enterprise software development to create innovative solutions that assist them in improving their productivity and financial posture. Enterprise software is any computer program developed to fulfill certain demands of enterprises. And to build enterprise software, Python is among the most preferred programming languages.

Since its inception in 1991, Python has come a long way. It has evolved a lot, and today, it features in the list of the world’s top 5 popular programming languages. This popularity of Python has forced enterprises to choose it as the language for developing enterprise applications. The popularity of Python is due to its versatility, simplicity, and wide range of applications.

There are many other reasons why data-driven enterprises prefer Python over other programming languages. In this blog post, I shall walk you through some significant specialty of the Python language that makes it a perfect choice for developing data-driven enterprise applications. But before that, let us have a brief discussion on Python.

What is Python?

Designed by Guido van Rossum in 1991, Python is a high-level, interpreted scripting language. The primary focus of Python is on improving code readability with its simple and elegant syntax. Python syntax eliminates the need for delimiters, such as semicolons, curly braces, etc. Instead, it leverages indentation.

Moreover, Python is a dynamically-typed language. This means that you need not have to define the data type of variables while declaring them; the Python interpreter interprets the data type of variables based on the values assigned. It is a multi-paradigm programming language that supports object-oriented, structured, and functional programming.

The comprehensive collection of libraries is responsible for Python to be referred to as the ‘batteries included’ language. Besides, the language has its applications in various domains, such as data science, software development, finance, blockchain, and machine learning.

Why is Python Programming for Data-driven Enterprises?

Let us now shed some light on the features of Python that make it a suitable language for developing solutions for data-driven enterprises.

  • Easier to Learn

No doubt about Python’s simplicity and conciseness. It is a very simple and easy language to understand and learn. It has a very low learning curve because its syntax includes English-like keywords and is easy to comprehend even for absolute beginners.

More importantly, unlike other programming languages, you need not have to write lengthy Python code to accomplish a specific task. Also, there is no need to write include curly braces, semicolons, $ sign, and other kinds of symbols in Python code. With just a few lines of Python code, you can perform the task you desire.

  • Easy Debugging

Python code is simply easy to debug. Debugging is the process of identifying and fixing errors in the source code. A plethora of Python debugging tools is available out there that help you debug Python programs without any hassle.

Debugging tools allow you to set breakpoints for your Python code and debug each line of code at a time. Also, there is a belief among developers that debugging tools available for Python are far better than debugging tools available for other languages.

  • Simplified Data Structures

In programming, a data structure is an approach to organizing data in the memory so that it can be accessed and manipulated efficiently. Python supports various data structures, such as a tuple, list, dictionary, set, string, byte array, linked list, tree, graph, and many others. All these data structures are simplified and reduce the amount of time to access and manipulate data.

  • Database Accessbility

Every application or website you develop for data-driven enterprises requires a database to store data. When you choose Python as a language to develop enterprise software, it provides an easy way to access databases. This means that Python provides easy access to databases. The language is much more compatible with relational databases, such as MySQL, MS SQL Server, Oracle, etc.

  • Test-Driven Development

Python is a popular programming language for test-driven development. Test-driven development is an approach to the software development process that involves writing test cases before writing the source code that executes them. Alternatively, it involves transforming software requirements into test cases before developing a software and testing that software repeatedly against those test cases.

Such a type of approach to software development results in error-free and high-quality software products. Not only does Python help you write clean and concise code, but it also makes it easy to test it for bugs or errors.

  • Web Development

Not only does Python is capable of creating desktop applications but also web applications. Most businesses prefer Python for creating their websites for online presence. Also, Python assists you in creating web servers. Instagram and Pinterest are two widely used websites that are developed using Python.

Python also has the capability to carry out various back-end functionality of websites, such as connecting with databases, data processing, maintaining security, URL routing, and sending and receiving emails. Two major leaders in Python web development are Django and Flask, which are web frameworks for creating web applications.

  • Data Science and Scientific Computing

Nowadays, Python has become the most demanding and preferred programming language for data science. This is because it offers a galore of libraries that make various data science tasks, such as data analysis, data visualization, etc., easy and simple.

Some of the popular Python libraries for data science include:

  • NumPy: A Python library for scientific computing. It allows you to manipulate n-dimensional arrays.
  • Scikit-Learn: A machine learning library that provides all essential ML algorithms, including classification, clustering, and regression.
  • Matplotlib: A Python library for data visualization. It lets you create static, animated, and interactive visualizations.
  • SciPy: A Python library for scientific and technical computing.


This was all about the reasons for Python being used in the development of solutions for data-driven enterprises. The primary reason why data-driven enterprises leverage Python is its comprehensive set of standard libraries. Not only in data science and scientific computing, but Python libraries are also useful in machine learning and natural language processing.

We hope you might have got a clear idea of why Python programming is most preferred for data-driven enterprises.

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