The usage statistics posted on various website depict the growing popularity of Python as a general-purpose programming language. The programmers have option to use Python for developing both desktop graphical user interface (GUI) applications and web applications. Unlike other modern programming languages, Python is a high-level and interpreted programming language. It further comes with features to simplify development of statistical, scientific, and big data solutions. The question which comes up always is how flexible is Python. The developers also have option to enhance the functionality and scalability of the open source programming language by using a variety of libraries and frameworks.

How Flexible is Python? 

High-Level Programming Language

Python is a high-level programming language. So it is different from other popular programming language. Unlike other programming languages, Python enables programmers to focus on the tasks to be completed or action to be performed without worrying about how to accomplish the specific objective.

No Need to Recompile the Source Code

As noted earlier, Python is an interpreted programming language. So the source code written in Python can run on directly on the computer without any compilation. The feature makes it easier for Python developers to make changes to the code, and check the impact of these changes immediately. The absence of code compilation further reduced the overall coding time significantly.

Simple and Easy Syntax Rules

The syntax rules of Python are influenced by a number of programming languages including C, C++ and Java. However, syntax rule of Python is completely different from these programming languages. For instance, Python delimits the code using whitespace. So the programmers are not required to use a ; or {} to organize the code. The simple and easy-to-read syntax of the Python further helps developers to express concept with less and readable code. Also, Python is hugely popular among beginners as an easy to read and learn programming language.

Multiple Implementations

Each programmer has option to choose from two stable versions of programming language- Python 2.x and Python 3.x. Also, he can combine Python with other programming languages and technologies through the specific implementation of Python. For instance, they can use Jython to import and use Java classes in Python code. Likewise, they can use PyPy which is an interpreter written completely in Python instead of C. They can even use IronPython and Boo to use Python and .NET technologies together.

Supports Common Programming Paradigms

Like other modern programming languages, Python also supports a number of commonly used programming paradigms. It supports object-oriented, functional, imperative, and procedural programming styles. At the same time, the programming language also supports dynamic type system, while featuring automatic memory management. The programmer can take advantage of these programming paradigms to build applications in a more organized way.

Large and Robust Standard Library

Python has a more robust standard library in comparison to other programming languages. As a wide variety of high use programming tasks are already scripted into the Python standard library, it becomes easier for developers to accomplish common programming tasks without writing additional code. For instance, they can use these libraries to accomplish common tasks like string operations, web services implementation, operating system interface management, and working with internet protocols.

An Interchangeable Component of LAMP Stack

The web developers have option to use Python as a key component of LAMP Stack along with Linux OS, Apache Web Server, and MySQL. As the components of LAMP Stack are interchangeable, they can choose the right platform and database according to specific requirements of each project. The combination of four open source components further enable developers to build and deploy a variety of web applications in a simple, flexible and cost-efficient way. Also, the programmers can use robust frameworks like Django, Flask, Pyramid, Bottle and Cherrypy to write web applications rapidly by using Python as a server-side programming language.

Build Standalone GUI Applications

As noted earlier, Python is a general-purpose programming language. Hence, the developers can use it for developing a variety of standalone GUI applications. But the developers have to put extra time and effort to write desktop applications in Python. Hence, they need to use additional libraries and frameworks to develop standalone GUI applications in Python rapidly. The programmers also has option to take advantage of a number of GUI toolkits and frameworks for Python including PyGTK, Kivy, Camelot, WxPython, TkInter and CEF Python. However, some of these tools are cross-platform whereas others are platform-specific.

Facilitates Development of Big Data Solutions

Experts have noticed a steady increase in the demand for custom big data solutions with features to collect, store, analyze and distribute large amount of structured and unstructured data. Unlike other programming languages, Python provides features for data analysis and visualization. The developers can avail file parsing, text filing, data visualizations and similar features provided by Python to build a variety of big data solutions efficiently. Also, they can avail specific Python frameworks to add functionality to the statistical applications without writing additional code.

Simplifies Data Analysis and Visualization

In addition to facilitating development of big data solutions, Python further simplifies presentation and visualization of data by providing a variety of data visualization libraries and application programming interfaces (APIs). The developers can take advantage of these libraries and APIs to embed graphics into data presentation and reports. Hence, Python makes it easier for programmers to generate data analysis reports that are both appealing and easy to understand.

Loads of Development Tools

Based on their specific requirements, programmers have option to choose from a wide range of Python libraries, frameworks, and development tools. For instance, they can avail robust frameworks like Django, Flask, Pyramid, Bottle and Cherrypy to build web applications in Python rapidly and efficiently. Likewise, they can avail libraries like SciPy, NumPy, SciKit, Pandas and Matplotlib to simplify development of statistical, big data and scientific applications. These development tools contribute hugely towards making Python more flexible than other programming languages.

However, the overall flexibility and versatility of Python is affected by a number of factors. For instance, Python, despite being used widely for web application development, lacks built-in web development capabilities. Likewise, Python does not find use for developing native mobile apps for iOS or Android. Hence, the developers need to avail several third-party frameworks and libraries to enhance the functionality, performance and flexibility of this high-level programming language.