Reasons Why You Should Use Python

Authored By admin

March 10, 2021

Python is a versatile, high-level programming language that helps you to build a wide range of applications, including web applications, design of apps and games, network programming, graphical user interfaces (GUIs), applications for science and numbers, and more. “Python allows us to produce maintainable features in record time, with a minimum of developers,” according to Cuong Do, Software Architect of YouTube. For Python, there are several success stories, illustrating that this programming language helps various businesses accomplish their business objectives.

Why Do You Use Python?

We also mentioned several key points in this article that will help you appreciate Python’s advantages over other programming languages. Read the following in order to learn why you can use Python:

  1. Code is simple to read, use and maintain

The application’s effectiveness depends heavily on the quality of its source code.

A powerful programming language that is easy to read, update and maintain is required for a robust application. All these qualities are incorporated by Python. It offers everything that is needed to build amazing applications, utilities and frameworks (including a set of huge libraries and utilities).


The developers and the organization have some significant advantages that Python offers:

  • Python has a clean and structured code base, making it easier for developers to update and maintain software tasks. They do not require developers to write extra code, and this saves them effort and valuable time in turn. Therefore, they can use their time for something more productive for the company.
  • The Python syntax rule consists primarily of keywords in English, emphasizing the readability of the code. Readability of code plays a major role during the construction of the application. The requirements of clients can change when the software is used in the real world. You don’t need to worry about the changing requirements for Python. Even if developers who have previously developed the application have left the organization, the new developers can read and comprehend the original version of the code easily and apply the new requirements accordingly.
  1. Supports various paradigms for programming

For both large-scale and small-scale projects, Python is designed to help developers write rational and clean code.

The support of several programming paradigms is an essential aspect that makes Python ideal for large and complex applications (meaning ways to classify programming languages on the basis of their features).

The following programming paradigms are supported:

  • Procedural programming: It is based on the sequence or processes consisting of some set of steps in the calculation. Its primary goal is to reflect on the objective of the software, then define the challenges and split them into smaller and more achievable parts (called modules) to address the main objective. In this, the curriculum in a storyboard format is planned out.
  • Object-oriented programming: It is based on an object and a class definition. In simple words, objects and classes are data sets that, in the form of code, contain fields, variable data types, etc. Object-oriented programming initiates with planning and diagramming the program. Then objects and classes are created, functions are constructed and data fields are put, beginning to take over the world of creation.
  • Functional programming: Developers attempt to bind anything in a pure computational mathematical style in functional programming. It requires the assessment of difficult phrases. In this, programmers primarily concentrate on the desired effect, not on how it can be solved or calculated.

It is an unavoidable truth that all challenges can not be successfully addressed by one programming paradigm. Python is a popular programming language among businesses due to the support of these multi programming paradigms (procedural, object-oriented and functional programming). It also has automatic memory protection, making it much more effective on the market than other programming languages.

  3)Compatible with Major Platforms and Systems

All the large operating systems and architectures are supported by Python. Let’s take a look: 


Operating systems that Python supports Architectures that Python supports
  • Windows
  • Linux
  • macOS
  • FreeBSD
  • Solaris
  • NetBSD
  • OpenBSD
  • AIX
  • Cygwin
  • Intel x86 (32-bit and 64-bit)
  • p pc64
  • ARMv7
  • s390x


Python code can be run on all of these platforms with the help of operating systems and architectures. Python’s code, being an interpreted programming language, is executed line-by-line immediately without the need to first compile the human-written code into machine instructions. In addition to compiled programming languages such as C, C++, Java, etc., this is Compiled programming languages are those in which, instead of line-by-line, a compiler transforms the human-written code into computer instructions all at once and then code execution takes place.

As an interpreted language, over compiled programming languages such as C, C++, Java, etc, Python provides the following advantages:

  • On multiple platforms, including Windows, Linux, macOS, etc, the same Python program is simpler to run. All you need to do is make sure Python is installed on the computer where the code is running. Whereas we can not copy the compiled program from the Linux computer to the Windows computer in compiled programming languages such as C, C++, and Java and expect the program to run successfully. Unlike interpreted languages (like Python), in compiled programming languages, in order to run the same program successfully on another operating system, we need to recompile the program again on a Windows computer.


  • Since Python code is executed line-by-line, it is simple to make changes in the code instead of all at once and run the changed code and see the effect of changes in the result instantly. Without increasing the development time, this makes Python appropriate for ad-hoc requests. Whereas, making improvements is not as straightforward as in Python for compiled programming languages. After making changes, you need to recompile and redeploy the program.
  1. Standard Large Library

There is a robust and large standard library in Python that makes it stand out from other languages of programming. A wide range of modules, operations and web service tools are included in its standard library, which you can select and use for your applications without writing additional code.

Benefits With its huge standard library, Python offers:

  • The standard library provides developers with low-level details, meaning developers can concentrate on the application’s logic rather than searching for software with low-level details.
  • Since most developers are familiar with the standard library, the code given by the standard library of Python is more manageable and can be replicated quickly through projects. This encourages the new developers entering the company to easily grasp and shorten their learning curve with the on-going projects.
  • The standard library of Python is well-optimized and checked, proving the code for your program to be stable and effective.
  1. Cost-effective technique

Python is an open source programming language, which means that it does not need any license fees to use it. For companies, this makes Python the most fair and economical alternative.

It provides numerous open-source platforms, software for development, and libraries that greatly minimize the cost of development and time for development.

Examples of modules and toolkits for open-source Python:


Python web frameworks Python GUI frameworks and toolkit

Flask, Django, Pyramid,

Cherrypyv and Bottle

PyJs, PyQT, PyGUl, PyGTK7 and_WxPython

These Python frameworks

simplify and speed up web

application development.

They accelerate desktop Graphical user Interface

(GU’) application development.


  1. Simplifies the complexities of software development

Python is designed to construct a wide range of device realms, including web-based apps, smartphone apps, complicated scientific and computational apps, and more. It is designed with data visualization functionality (generating interactive representation of data for data analytics) and data analysis features (the process of analysis, cleansing, sorting and modeling data to extract meaningful insights).It provides numerous APIs and libraries for data visualization that allow engineers to represent data in a more powerful and attractive manner. This is why many applications for Business Intelligence (BI) and data science use Python as their language for programming. It can be used without asking you to expend additional work and resources to build custom Big Data solutions. Python may also be used to execute certain functions associated with other innovations, such as the analysis of natural languages and artificial intelligence (AI).

  1. Test-Driven Development adoption

The test-driven architecture methodology is supported by Python. Before going on, you can consider what a test-driven methodology entails and how the traditional testing method makes it better:

What is a test-driven approach to growth (TDD)?

Test-driven growth: It is only from this term that it is clear that TDD enables you to test your application design and development. It means constructing tests that define and verify what the code is going to do, what the next move is going to be, what the specification is, etc.

The following stages are specifically involved in TDD:

  1. Prior to the production process, write the test cases (when developers start coding for an application).
  2. Write the code so that the test cases are passable.
  3. If you find an error when checking the code, then make adjustments to the code.
  4. Run the code again for the test cases to pass.
  5. If the test cases are passed by code, then stop; otherwise,repeat step3

How would TDD be different than the conventional approach of testing?

In the conventional system of testing, after writing the code, test cases are created and applied. This influences the speed of distribution of goods. Whereas, in TDD, before writing the code, test cases are created and implemented. If the test case fails, then only the small amount of code needs to be written or changed by developers to pass the test case. In exchange, this eliminates code repetition, making the code easier, safer and bug-free.

TDD ensures100 % test coverage, versus a conventional testing process. No single line of code, offering an effective and error-free application, is left untested.

Now, returning to TDD’s Python support, Python makes it possible for the development team to concurrently conduct coding and checking by following the TDD technique. It helps to shorten the period of production and testing and reduce the product’s time-to-market.

If you need support developing your application in Python, contact us. At Molinatek, using Python, our team of committed engineers will develop a stable, modular and scalable framework.


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