What are the Pros and Cons of Using Python?

Python is considered easy to learn and use almost everywhere. It is useful for a number of applications, including education, data analysis and web development. Some of the largest companies in the world rely heavily on Python, including Instagram and Google.

It is a dynamic object-oriented programming language (OOP) comparable to Microsoft .NET or Java, as a versatile substrate for several types of software development. It provides strong support for integration with multiple technologies and higher programming productivity throughout the development life cycle. It is particularly suitable for complex and large projects with changing requirements.

Python is also one of the fastest growing open source programming languages, and is used in critical applications for the world's largest stock exchange. It also serves as a base for various high-end publishing websites, operates on several million cell phones, and is used in such areas as air traffic control, feature film animation, and shipbuilding.

Let's start with a positive note and discuss the pros and cons of this prolific programming language.

Pros of Using Python

• Easy to Read and Use

Most Python programmers would agree that the biggest benefit of Python is that it is easy to pick up. Ease of use and ease of reading are more than just convenience. It can also benefit users of your program. Ease of Use helps you think more clearly when writing programs, and for others who need to improve or maintain the program.

Experts and beginners can easily understand the code and you can quickly become productive with this language because it has fewer "dialects" than other popular languages ​​like Perl. Since its source code looks like the pseudo code, it is also easy to learn. As soon as you start learning, you can start coding effectively almost immediately.

Overall, it takes less effort to write a program in Python than for other languages ​​like Java or C++. This is also very popular among academics, resulting in a large talent pool. It is considered a very productive way to write code, and this comes in part from its readability and simple syntax. Some come from its well-designed and rich standard capabilities and library, and the availability of several third-party open source modules and libraries.

As it is easy to understand, it is also easy to maintain. The language is also dynamically flexible and typed, with a code less talkative than other languages. But this dynamic typing could also play as a disadvantage, which we will discuss later.

• Faster and Straightforward

The Python community provides effective support for users, and hundreds of thousands of developers are working hard to find and fix bugs and develop new patches and language enhancements. The offer also provides quick feedback in many ways. On one hand, programmers can skip various tasks that should be done in other languages. This reduces the time and cost of each program, as well as the maintenance required for the program. Python also allows a quick adaptation of the code. The language can be called ready to use, requiring only a simple code to execute. Playing and testing your code becomes much simpler with the language, which also offers an upward development style to easily build your application by testing key functions in the interpreter before you start writing high-level code.

The interpreter is easily expandable, allowing you to integrate C code with a simple compiled plug-in. Python also motivates the reuse of programs with packages and modules. A number of modules are already available with the standard library, which is essential for Python distribution. You can share the functionality between different programs by breaking them down into several modules.

The language can run on multiple systems, but retains its similar interface, and its design does not change much with each operating system because it is written in ANSI C notebook. This means that you can easily write Python on a Mac, test it on a Linux system and download it to a Windows computer.

• Usability with IoT

The Internet of Things or IoT has opened huge opportunities, and Python can play a key role in using these opportunities. Language is becoming a popular choice for IoT, with new platforms like the Raspberry Pi. The Raspberry Pi documentation indicates that the language is easy to power and use.

• Asynchronous Coding

Python has been very effective at writing asynchronous code, which uses a single event loop to work in small units rather than writing uses. Indeed, it is easier to write and maintain without confused search conflict or blockages or other problems. These generators are very useful for running multiple processing loops.

• Limited Programming Approach

Compared to Java, Python uses a much less limited multi-paradigm programming approach. For example, you do not need to create a separate OO class to print 'Hello World' in Python, but you have to do it in Java. Python is multi-paradigm and supports functional, procedural and object-oriented programming styles. In Python, anything and everything can be an object. You can write applications in the language using multiple programming paradigms, and you can still write clear, clear, and understandable OO code.

• Enterprise Level Application Integration

Python is an excellent choice for a programming language that includes Enterprise Application Integration (EAI). It facilitates the development of Web development services by calling CORBA or COM components and calling directly from and to Java, C ++ or C. It provides meaningful process control functions and implements Internet data formats and protocols. Common, dealing with markup languages such as XL, runs from the same byte on modern operating systems and can be integrated as a scripting language.

• Can be Used for Web Development

Python can be and is widely used for web development, for purposes ranging from developing high-end web applications to simple CGI scripts to large-scale frameworks such as Turbgears and Django. Other examples of using Python in web development include the Quixote web application framework, the Plone content management system, and the Zope application server. You can easily create your own solution based on Python's easy-to-use extended standard libraries. Python provides interfaces for most databases, works well with other Web development technologies, and has powerful word processing and document features.

• Can be Used for Numerical and Scientific Applications

You can use the Python Imaging Library as well as the MayaVi and VTK 3D visualization tools, as well as other tools such as ScientificPython and Numeric Python to develop digital and scientific applications. Many of these applications can also be supported by Enthought Python Distribution.

• Software Testing and Application Scripting

The strong integration of Python with Java and C and C ++ makes it very useful for application scripts. It was designed from the beginning to be embeddable, and can be an excellent choice for a scripting language to customize or extend larger applications. Python can also be used for many software tests, thanks to its powerful word processing and integration capabilities. In fact, Python even comes with its own unit test framework. Python can be used to develop high end GUI desktop applications too. You can use open technologies to deploy your application on most operating systems. Support for other GUI frameworks such as Motif, X11, Delphi, Carbon and MFC is also available.

Cons of Using Python

• Speed

Speed, or lack of it, can be a major problem. Since this is an interpreted language, Python can be slower than other compiled languages. However, this brings us back to the separation of the language from the execution. Some Python benchmarks work faster than the equivalent of C or other coding languages. The slowness of Python's execution speed has been criticized in the past, but it has been treated to some extent with optimized packages in recent years. Still, Python can be slower in some ways to languages like C ++ and C, and newer ones like Go.

• Lacks Mobile Computing and Browsers

Python is strong in desktop and server platforms, but weak in mobile platforms. There has been only a handful of smartphone apps developed using Python, and the language is rarely seen on the client side of web development applications.

The language is also not present in Web development browsers. The main reason for this is that it is difficult to secure. It still lacks a good secure sandbox for the language, and some programmers consider it difficult to impossible for the standard implementation, CPython.

• Restrictions in Design

Even the biggest fans of Python would accept some design restrictions in the language because they are dynamically typed. This requires more tests and errors to be displayed only during execution. The global language interpreter lock means that only one thread can access Python interns at any time.

• Package Availability and Maturity

There is a lack of Python counterparts for several Matlab toolkits. Many of these toolkits, modules and packages are not yet mature in terms of development, and are poorly supported and documented. This is to be expected as Python is largely run by a community of volunteers who may not have the time to document and support each module. If you plan to get a module or package for Python, it's always good to see if the module is actively maintained before developing an application that depends on it. Otherwise, you will have to develop your own patches and workarounds for the code.

We briefly discussed the use of Python in engineering and scientific work. Among the modules for such work, matplotlib, SciPy and NumPy are among the most important. While matplotlib and NumPy are well documented, SciPy may have unclear or missing documentation. For example, scipy.interpolate.LSQUnivariateSpline is used to add a smoothing split for the data, but the documentation does not explain the meaning of the coefficients returned by the method. This can be problematic because the method returns fewer coefficients than expected.

• Problems with matplotlib

There are also some challenges in the matplotlib, which is a fairly powerful non-interactive tracing package. On the one hand, there is a lack of uniformity in the interfaces for various methods and functions. For example, when you generate a text box with the pyplot.annotate function or the annotate method of the axes object, you can use the xycoords keyword to specify whether the location of the text is specified as data coordinates, coordinates fractional or fractional coordinate axes. But this keyword is missing with the pyplot.text function and only data coordinates can be used to specify the location of the text, which is not usually what programmers want.

Wrapping Up

As you can see, despite its popularity, it's far from perfect. It has its fair share of problems, including some related to its design and performance. If you plan to develop something with Python, you must first be clear about its benefits and limitations. If you need any assistance related to Python web development or cross platform mobile app development, you can contact us. We are a leading Python development company in India & USA engaged in providing feature packed, secure and scalable web and mobile apps across all business verticals.