Ten Reasons To Use Python


The growing reason for Python's popularity

Python gives you many reasons to use it as a programming language of choice. It is one of the most popular coding languages in the market right now, with many analysts predicting positive steps for the future of the coding environment. It’s a general-purpose high-level programming language, which can be used to develop desktop GUI applications, websites, and apps that run on sophisticated algorithms.

It’s widely accepted as one of the more versatile languages that can be used across a plethora of projects. It also allows you to focus on the core functionality of an application rather than focus on the minor details. As it is visible from the diagram Python has grown in popularity as compared to Java or Javascript

Growth of major programming languages
Source: https://www.electronicdesign.com/embedded-revolution/python-s-big-push-embedded-space[/caption]

This frees up resources and enhances the overall appeal of the project. The code base is readable, and the application remains maintainable simultaneously. It also makes it easier to code in complex applications using highly flexible language.  The engaged syntax rules make it so that developers can leverage the core power of python without needing to master the language entirely.

From a developer perspective, it’s one of the more desired skillsets after Swift. It’s one of the most valuable skillsets to have, along with being one of the most in-demand coding languages. Developers are keen to learn more about the language as it offers them a higher command over an entire project. They're able to focus on other details as well as the core project when they have dominion over Python.

All of this and much more, are the many reasons why coders should use Python regularly. Here are the ten significant reasons why you should use Python –

  1. Leveraging greater Data Science

Data science has been enhanced by using the Python coding language. There are many advantages to using Python, especially when it comes to Data Science. The coding language and the development environment make it easier to form complex algorithms on which Data or even Image processing can be performed. The necessary code makes it scalable to achieve rapid data analytics and create new insights from existing data.

Many of the most complex data sciences projects are developed on Python, owing to its flexibility and scale-oriented approach. The web apps designed to work with the Data Science projects are also scalable, making it easier to analyze more extensive sets of raw data. Coders can also create an entire project architecture based on the code shared by the Python ecosystem.

Data science projects are also more compatible with other projects and development environments. This makes it easier to create apps that revolve around Python as you can develop frameworks around the language. This enables for greater flexibility to work on a specific code for a particular project. This also means that Python can be improved upon to create a new language for that Data Sciences project.

  1. Python for Game development

Python is a highly flexible language for game development and can create magical game environments for coders to explore. There are many software packages in the marketplace that develop off the core Python script, and many developers use them to get a base ready for gaming. Game development is also a highly evolving subject area which needs versatility to explore fully.

Game development has been happening on Python for decades now, which is why it’s widely accepted as a core format of game coding. Development, testing and bug finding can be made that much simpler when you have Python installed and working. Games can become multidimensional if Python is being used across the board.

Additionally, game development is also a team-focused approach, which means that the coding language needs to be compatible across various teams. This opens up the need for Python as multiple coders can use it to flourish their systems. They can also use add-ons and multiple code injections to ensure that the project remains compliant with the overall goals of the scheme. Games can also leverage different engines being produced in Python and create a more robust experience for fans.

  1. The depth of open-source frameworks

Since Python is an open-source framework, many different libraries and frameworks can be developed off it. Toolkits such as PyQT, PyJs, PyGUI, Kivy, PyGTK, and WxPython have been developed because of developer demand. There is a significant amount of interest in developing these robust frameworks because there are many use-cases.

Additionally, Python can be used across industries as well. There can be a framework designed for niche projects and others that focus on generic modeling. Python can be used across the board as it is a highly flexible coding language. When it comes to multi-industry projects, Python is generally accepted as a highly fulfilling language.

Tools like Django, Flask, Pyramid, Bottle and Cherrypy are designed to make your web frameworks that much more robust, and your web projects scalable. The allow for basic frame development and formulation of a more coherent environment. Web frameworks developed off these coding environments are readily launchable in the marketplace with a broader focus on quality delivery.

Brands can create scalable web apps using these open-source frameworks and tools developed. They can also design new features that leverage the full potential of the browser or the computing environment.

  1. Machine learning and deeper insights

Much of the machine learning scale has been developed by Python, with the size and versatility it offers. Machine Learning is a highly complex procedure, taking in information at rapid speeds. All of this requires a standardized coding environment that can be molded into particular project requirements.

That’s why Machine Learning has become increasingly popular over the years. Python is powering many innovative projects that leverage the power of ML and has been providing support to the domain. The industry of ML depends a lot on the scope of Python while giving it more great shape from an industry stand-point.

Jupyter Notebook is one such program that uses a command-line Python interpreter. It also has a web-based interface and has enhanced visualization capabilities. It essentially combines the code and the output into a single document that has meta-data and lines of code embedded. This does the job of ML that much more comfortable, with visualizations being presented for enhanced presentation.

There is also an extensive library present that enhances the ML capabilities across the board. It creates for a more robust coding environment that produces higher value in the projects you’re working on. One scientist can pull information from another and collaborate efficiently. This collaborative nature has opened up the scope for greater ML processing and a more comprehensive environment for depth in ML.

  1. Python and IoT (Embedded electronics)

Even in the IoT sphere, Python is a preferred language of choice. It's flexibility across all parameters, and its scale makes it a viable candidate for being used across boundaries.

“The embedded engineering community is embracing the use of scripting languages. What began primarily isolated as a tool in the QA domain has quickly expanded within the software development ranks, with Python, in particular, showing incredible growth in the past few years.” - Chris Rommel, EVP of IoT & Embedded Technology research at VDC.

Python is being leveraged across the board, especially in the domain of the connected device. It allows for greater scale, owing to the impact of the coding environment developed. IoT specific frameworks can also be created using the core power of Python across the board.

Python also tends to be used as an interpretive scripting language, which makes it similar to using a Java JIIT compilation software. This means that there are native code compilers like Cython that make the job much more comfortable. It also allows for more compact implementations and microcontrollers can flourish under the paradigm.

It also creates a more robust coding environment in the domain. Zerynth is one such package that is developed on Python. It allows for a link to be built between IoT functions and the cloud at large. This and many more reasons are why Python is popular in the IoT space.

  1. Extended libraries and community support

The extensive libraries, offered under the Python ecosystem, allow for more significant support via the coding community. The community has an extensive library built up regarding resources that any developer can take advantage of. Additionally, from a community stand-point, the Python community is extensively diverse and holds many roles across industries.

Developers can gain the right type of insights from a more extensive variety of projects and develop on their schemas. This is true for projects of all kinds, with extensive libraries present in all of these endeavors. These libraries are also open-source leaving much support is available within the Python ecosystem.

“For almost 20 years, C, C++, and Java are consistently in the top 3, far ahead of the rest of the pack Python is joining these three languages now. It is the most frequently taught the first language at universities nowadays, and it is number one in the statistical domain, number one in AI programming, number one in scripting and number one in writing system tests. Besides this, Python is also leading in web programming and scientific computing (to name some other domains). In summary, Python is everywhere.” – Head of research, paper at TIOBE

The extended support offered under the open-source platform ensures that there are multiple layers of interjection and complexities addressed. Owing to the age of the language, many developers have formed ecosystems that work consistently on enhancing the expression for long-term benefits. This has improved the overall appeal of the language over time, owing to its extensive support being offered across the board.

  1. Easy basics to learn and leverage

Python is a more straightforward language to learn from the basics, making it a more natural approach to teaching the basics. The basics can also be leveraged across the board and students can pick up the language at an early age. It's one of the few languages that give direct feedback and results. That's an added factor as to the growth of the language and why it is here to stay in the long-run.

“In order to enable the students offering Computer Science irrespective of the programming language to appear for the Class 12 examination in 2019, the CBSE will be administering, the question papers in the subject of Computer Science on the basis of 2018 having sections i.e. C++ and Python and candidates be directed to attempt any one section.” – CBSE Board And 2019.

The language is being promoted across the world as a necessary coding skill that all young developers must have. This leads to more significant innovation in coding and a more enhanced approach to process development. There is also an additional layer of programmability that only Python can provide to the young developer.

Now with Coconut (for Python), much of the limitations of Python have been addressed as well. This makes Python a more popular language to learn, mainly owing to its flexibility and variable formats available. Coconut makes it easier to have a more holistic approach to Python, as well as create more robust projects that leverage the full potential. From a program and project perspective, Python checks off all the boxes present.

  1. A testing framework that’s extendable

Python has been created as a testable and fully extendable environment for the modern-day coder. It has been designed to integrate much of the design challenges we face as coders. From a testing standpoint, it has stand-alone capabilities that go beyond the regular coding environment. Developers can design features and core apps that can be tested regularly and integrated as well.

This makes for a more innovative and collaborative environment when it comes to developing new apps and features. It also allows for different teams to participate in projects across many platforms. From manual testing to automated tools, Python provides the necessary framework to perform better testing mechanisms.

Unit Tests and Integration Tests can also be performed using the core Python tools available in the open-source framework. Coders can verify only coding glitches or look at the entire code from the integration testing mechanisms available. Finding the right results is easier when developers have the right tools working within the Python framework.

  1. Robotics, AI, and Python

Self-learning and active knowledge-based robots can be designed using Python. The language has many features that provide the necessary support for robotics to flourish. Robotics requires a multi-faceted approach to coding, which is precisely what Python provides. It also caters to the coder's skill level and allows for sophisticated robots to be designed using simplified development tools.

Sophisticated features like Motion planning, navigation, localization, manipulation, and image accessing can be done using the Python language being used. It can also allow for more significant modification to the stand-alone features allowed under the environment. Developers can create great robots that can perform essential functions for long periods of time. This helps in the automation industry and creates efficiencies across the board.

New robots can also be added with ease, along with older robots being updated using the Python scripts being added. Therefore, the role of Python in AI and Robotics becomes extremely important.

  1. Enterprise-level applications (Business advantages)

At the enterprise level, there are sophisticated requirements that each application needs to satisfy. This is why Python is the perfect coding language to develop the apps in. It makes for a more robust coding ecosystem when app developers can connect multiple business domains into one coherent system.

Developers can also enhance existing apps that are developed in the Python ecosystem. They can create new applications that focus on existing solutions and work on improving them. By using a consistent language on which to develop these apps, it makes for much easier integration of all features and design elements.

This makes it much simpler to code complex applications that are enterprise-level. Additionally, designers can make coding and testing integrated into one ecosystem. This makes it easier to design modifications that may be incorporated shortly.


Python is a robust coding language that focuses on a plethora of new technologies while providing a standardized coding environment. The word can be used to develop GUI applications, websites, and web applications. It is a higher-level language that allows you to focus on the bigger picture when it comes to coding. Python is also flexible and can be translated into binary code like Java. That’s why developers enjoy using the language and focus on the range of applications that can be developed using it. It's one of the most popular words according to analysts and allows for simple syntax rules and logic.