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Top 10 Advantages of Using Python Scripts [2024]

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22nd Feb, 2021
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Top 10 Advantages of Using Python Scripts [2024]

Python is a high-level object-oriented programming language with dynamic semantics developed by Dutchman Guido van Russom in 1991. Its high-level data structures along with the dynamic binding and dynamic typing make it ideal for Rapid Application Development and connecting existing components. Python scripting language is simple, and its syntax is easy to learn.

It emphasizes readability and has a low cost of maintenance. Python supports package and module based programming, encouraging code reuse. The extensive standard library and the Python interpreter are available in binary or source form free of cost.

Python can be used to build a vast range of applications including software, web applications, graphical user interface, network programming,  and many more. Cuong Do, software architect of Google opines that “Python allows us to produce maintainable features in record times, with a minimum of developers”. TIOBE Index and PYPL Index have ranked Python scripting language as one the most popular languages in 2019, giving it the first and third position, respectively.

According to a survey by Python Software Foundation, 84% of programmers used Python as their primary language in 2019, up 5% from 79% in 2017.  

Some of the top global companies that use Python are Wikipedia, NASA, Google, Quora, IBM, Yahoo, Instagram, and Pinterest. It is the scripting language for web applications. It can automate a set of tasks to make it more efficient.  Hence Python is used in programs within a web browser, operating system shells and games. 

Top Advantages of using Python Scripts

Python Is the top pick among programming languages in most universities and businesses. It creates limitless possibilities for users to create applications from scratch. According to TechRepublic, the Python Developer position was one of the most in-demand tech positions of 2019. There are several reasons to learn Python scripting. Here are the top ones:

1. Code is Simple to Read and Manage

A dynamic application must rest on a robust and powerful language that is simple to read, debug, update and manage. Python embodies these qualities plus frameworks and utilities.

Python’s structured code base enables quick updation and code maintenance for the developers. They can divert their valuable time for productive tasks rather than writing additional code.

Python’s syntax mostly uses English words making the code readable and flexible to suit the fluctuating customer demands. Even if the original developers are no longer in the project, the new ones can effortlessly read and comprehend the original code to incorporate new requirements.   

2. Support for Many Programming Paradigms

Coders can write clean and logical code using Python scripting irrespective of the project size and scale. Since it supports multiple programming concepts, Python can be used to build even complex applications with ease.  It supports:

Procedural Programming

This is based on procedures and patterns involving computational steps. The main objective of this method is to identify the program problems, break them down into smaller components and work to attain the main objective. 

Object-Oriented Programming

Object-oriented programming begins with planning and sketching the program. The objects, classes and functions are constructed, data fields and functions are built before the product development phase begins.

Functional Programming

Here developers build all features in mathematical format involving solving complex expressions. The focus on functional programming is on the end result rather than the method of computation.

3. Compatible with Most Systems and Architectures

Python offers support for all popular architectures and operating systems. Python supports the following operating systems:

Windows, Linux, macOS, Solaris, NetBSD, OpenBSD, AIX, FreeBSD, Cygwin

Python supports the following architectures:

Intel x86, PPC64, ARMv7, s390x 

The python script is executed line-after-line without a need to compile code written by humans into machine instruction first like other programming languages.

Python offers many advantages over other programming languages:

  • It can be run on several platforms such as Windows, macOS, Linux and so on with just the Python installation on the machine. There is no need for recompilation of the program.
  • Since it is executed line after line, it is easier to make modifications in Python code, run the altered code and immediately see the results. But other programming languages need to be re-compiled and redeployed after making the changes.   

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4. Huge Standard Library

Python boasts of a comprehensive standard library when compared to other languages. Its library consists of several modules, tools, and features that can be leveraged without writing extra lines of code. Developers need not worry about the ground-level details and can focus on the program logic. Code offered by the Python library is maintainable and reusable. It is also a well-optimized, rested, reliable code that can be used for any application.

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5. No License needed

Being an open-source language, no licensing fees are essential to use Python making it the most popular choice of companies. It offers many tools, libraries and frameworks that notably bring down the development time and cost.  

Some of the Python web framework examples are Flask, Pyramid, Django, Bottle and cherrypy. These frameworks quicken web application development.

Some of the Python GUI toolkit examples are PyQT, PyGUI, PyJs, and Kivy. These tools speed up desktop GUI application development. 

6. Facilitates Data Visualization

Python scripting can be used to build applications for various domains such as web, desktop, and scientific applications. It has features that enable data visualization through graphical data representation and data analytics. The API’s and libraries of Python enable engineers to use it for Business Intelligence and data science needs. Customized solutions can be created through Python in Artificial Intelligence and Natural Language Processing areas as well.

7. Follows Test-Driven Development

Python supports test-driven development methodology. It lets the developers test the design and code at each stage of development. The tests will validate the code function, decide the future design and other steps. Any error encountered during testing can be corrected, modified, and re-tested.

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8. Data Science and AI

Data Science and Artificial Intelligence are the future of digital transformation and Python support these technologies. Data scientists use Python to conduct research in AI and Data Science. Users can directly run the application on any platform without compiling it into machine language which makes Python code to be easily interpreted by a virtual machine or an emulator. Python’s libraries Keras and TensorFlow support machine learning functionality. The OpenCV library aids in image recognition and computer vision.   

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9. Web Development

Python has a wide array of frameworks to support website application development. Some of the popular frameworks Django, Pylons and Flask enable stable and quick coding because their scripts are written in Python language. Python also helps the users to fetch information from other websites. 

10. Flexibility

Python’s many applications allow it to integrate with programming languages. For instance:

  • Iron Python is compatible with C# and .Net
  • CPython is compatible with a version of C
  • RubyPython can be combined with Ruby
  • Jython is Python integrated with Java
  • PyObjc is Python written with Objective C toolkits

Also Read: Python Project Ideas & Topics

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The frameworks and language you choose for your application plays a key role in determining the fate and longevity of your product. They decide what features you can build into it, how much time it will take, who can build it and its validation in the market. This is what you will get if you choose Python as your programming language choice for building your applications:

  • Faster time to market.
  • Fewer errors in the code which means less bug fixing.
  • Pre-determined solutions through libraries and open-source tools.

If you are curious to learn about Python, data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.


Rohit Sharma

Blog Author
Rohit Sharma is the Program Director for the UpGrad-IIIT Bangalore, PG Diploma Data Analytics Program.

Frequently Asked Questions (FAQs)

1Is Python worth learning in 2022?

Python is in high demand due to its versatility and flexibility. Its vast range of libraries and modules makes it compatible with various technical fields. The following are some of the popular tech domains that use Python and make it worth learning.
1. You can be a Python developer right after acquiring the Python knowledge. Python developers are responsible for building websites, optimize data algorithms, or write clean and efficient Python codes.
2. Python is considered the most suitable language for data science and data analysis, which are the hottest technical sectors of 2022.
3. Project management is in high demand, as a project manager is highly responsible for the business and marketing of the companies.
4. A machine learning engineer trains the machines or models for making predictions based on the data provided to them.

2Name some popular Python libraries?

Python has a wide range of rich libraries and modules but being a Python geek, you must be handy with the top and most used Python libraries. The following are some of the most popular Python libraries:
TensorFlow: TensorFlow is a boon to Machine Learning engineers. This library is developed by Google and can be considered a computational library.
Scikit-Learn: If you are working with complex data then you must have Scikit-Learn in your arsenal. This library provides the cross-validation feature which allows various methods to check the accuracy of your model.
Numpy: Numpy is again a machine learning library used by other Python libraries like TensorFlow to perform internal operations.
Keras: Keras is another popular Python library that provides a convenient mechanism for neural networks.

3Why is Python called a versatile language?

Python is the most versatile language because of its diversified applications in the field of software development and research areas. This is all because of its simplified syntax, powerful libraries, multiple programming paradigm support, and ease of usage.
The following are some of the areas where Python is mostly used:
1. Application Development- Web and Game Development
2. Machine Learning and Artificial Intelligence
3. Scientific Computations
4. Data Science
5. Custom Automation
6. Desktop GUI Applications
7. Audio and Video Applications
8. Console Applications
9. Image Processing Applications
10. Enterprise and Business Applications
11. CAD Applications

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