Benefits and Limitations of Using Python?

Introduction to Benefits and Limitations of Using Python

Python is considered easy to learn and run almost anywhere. It is useful for a number of applications, including education, data analytics and web development. Some of the biggest companies in the world rely on Python extensively, including Instagram and Google.

It is a dynamic, object-oriented (OO) programming language comparable to the likes of Microsoft .NET based languages or Java, as a general-purpose substrate for several software development kinds. It provides strong support for integration with several technologies and higher programming productivity across the development life cycle. It is particularly suited for large and complex projects with changing requirements.

Python is also one of the fastest-growing open-source programming languages and is used in mission-critical applications for the world’s largest stock exchange. It also forms the base for various high-end publication websites, runs on several million cell phones and is used across industries such as air traffic control, feature-length movie animation and shipbuilding.

Let’s start on a positive note and discuss the advantages of this prolific programming language.

Pros of Using Python

Following are the pros are given below.

#1. Ease of use and read

Most Python programmers would agree that Python’s biggest advantage is that it is easy to pick up. Ease of use and easy readability is more than just a convenience. It can also benefit the users of your program. Easy usability helps you think more clearly when you write programs and others who have to enhance or maintain them.

Experts and beginners can easily understand the code, and you can quickly become productive with this language since it has fewer ‘dialects’ than other popular languages like Perl. Since its source code resembles the pseudo-code, it is also simple 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 it would using other languages like Java or C++. This is also rather popular among academia, resulting in a large talent pool. It is considered a very productive way of writing code, and some of this comes from its readability and simple syntax. Some come from its well-designed and rich inbuilt capabilities and standard library, and the availability of several third-party open source modules and libraries.

Since it is easy to understand, it is also easy to maintain. The language is also dynamically flexible and typed, with code that is not as verbose as other languages. But this dynamic typing could also play out as a disadvantage, which we will discuss later.

#2. Straightforward and speedy

The Python community offers fast and effective support to users, and hundreds of thousands of developers work hard to find and fix bugs and develop new patches and enhancements to the language. The also offers fast feedback in many ways. For one, programmers can skip various tasks that would have to be done in other languages. This brings down the time and cost of each program and the maintenance required for the program. It also permits fast adaptation of code. The language can be termed as ready-to-run, requiring just simple code to be executed. Playing around and testing your code becomes much simpler with the language, which also offers a bottom-up development style to easily construct your application by testing key functions in the interpreter before you start writing top-level code.

The interpreter is easily extensible, allowing you to embed C code with a simple compiled extension module. It motivates program reusability, too, with packages and modules. A number of modules are already available with the standard library, essential for Python distribution. You can share the functionality between different programs by breaking them into several modules.

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

#3. Usability with IoT

The Internet of Things or IoT has opened up huge opportunities, and it can play a key role in you utilizing these opportunities. The language is becoming a popular choice for IoT, with new platforms like the Raspberry Pi being based on it. The documentation for Raspberry P states that the language is easy to use and powerful.

#4. Asynchronous coding

It has proven to be quite effective for writing asynchronous code, which utilizes a single event loop for doing work in small units rather than writing up uses. This is because it is easier to write as well as maintain without any confusing research contention or deadlocks, or other issues. These generators are very useful for interleaving running several processing loops.

#5. A less limited programming approach

As compared to Java, it uses a much less limited multi-paradigm programming approach. For instance, you do not have to create a separate OO class for printing ‘Hello World’ in Python, but you do have to do it in Java. It 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 several programming paradigms, and you can still write crisp, clear and understandable OO code.

#6. Enterprise Application Integration

It is a great choice for a programming language that includes Enterprise Application Integration (EAI). It makes developing web development services easier, invoking CORBA or COM components and directly calling from and to Java, C++ or C code. It provides significant process control features and implements common internet data formats and protocols, processing markup languages such as XL, running from the same byte code on modern operating systems, and can be embedded as a scripting language.

#7. Its use in web development

It can be and is used extensively for web development, for purposes ranging from high-end web application development to simple CGI scripting to large-scale frameworks such as TurboGears and Django. Other examples of Python’s use in web development include the Quixote web application framework, Plone content management system and Zope application server. You can easily create your own solution based on Python’s easy-to-use and extensive standard libraries. Python provides interfaces for most databases, works well with other web development technologies and features powerful document and text processing facilities.

#8. Its use in scientific and numeric applications

You can use Python’s imaging library and MayaVi and VTK 3D visualization toolkits, and other tools like Scientific Python and Numeric Python to develop numeric and scientific applications. Many of these applications can also be supported by Enthought Python Distribution.

#9. Application scripting and software testing

Python’s strong integration with Java and C and C++ makes it very useful for application scripting. It was designed right from the beginning to be embeddable and can be a great choice for a scripting language for customizing or extending larger applications. Thanks to its strong text processing and integration capabilities, it can also be used for extensive software testing. In fact, it even comes with its very own unit testing framework. It can be used for developing high-end GUI desktop applications too. You can use open technologies to deploy your application across most operating systems. Support for other GUI frameworks such as Motif, X11, Delphi, Carbon and MFC are also available.

#10. Python’s use in prototyping and open-source advantage

Prototyping in Python is rather easy and quick, resulting in the development of the final system in several cases. Since Python is rather agile, you can easily refactor code for a rapid development from the first prototype to the final product. Python’s open-source nature is also a huge advantage. It is well-designed, scalable, portable, robust and fast due to its nature. Its syntax is easy to pick up, and it has uncluttered and well-developed advanced language features. In many ways, it exceeds the features and capabilities of other commercially available comparable solutions.

Python’s open-source license also allows unrestricted modification, redistribution and use of the language and applications based on it. The full source is available, and there are no licensing costs involved, which is a huge cost saver. Support is freely available through online resources.

#11. Server-side scripting

It is considered a strong server-side scripting language. Its code resembles pseudo-code like other scripting languages, and it hardly has any rich or complicated syntax. It has been built so that you can focus less on what command you want to use and instead focus on the business rules for your application.

#12. Portability and interactivity

Another huge benefit of Python is its portability and interactivity, making it that much easier to learn. It provides dynamic semantics and rapid prototyping capabilities. It is often considered a glue language, connecting disparate existing components. It is highly embeddable in applications, even those using other programming languages. This makes it possible for you to fix new modules to Python and extend its core vocabulary.

Cons of Using Python

As you can Python has huge benefits. But it has its fair share of limits too. Here is a look at them:

#1. Speed

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

#2. Lack of mobile computing and browsers

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

The language is also not present in web development browsers. The major reason for this is that it is difficult to secure. There is still a lack of a good secure sandbox for the language, and some programmers consider it difficult to impossible for the standard implementation, Python.

#3. Design restrictions

Even the biggest fans of Python would agree to certain design restrictions in the language because it is dynamically typed. This requires more testing and errors to turn up only during runtime. The language’s global interpreter lock means that just one thread can access Python internals at any time.

#4. Package maturity and availability

There is a lack of Python counterparts for several Matlab toolboxes. Many of these toolboxes, modules and packages are not yet mature in terms of development and are poorly supported and documented. This is to be expected, given that it is largely driven by a community of volunteers who may not have time for documenting and supporting every module. If you plan on getting a module or package for Python, it is always a good idea to see if the module is being actively maintained before you develop an application dependent on it. Otherwise, you will have to develop your own patches and workarounds for the code.

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

#5. Problems in matplotlib

There are also certain challenges in the matplotlib, which is quite a capable non-interactive plotting package. For one, there is a lack of uniformity in interfaces for various methods and functions. As an example, when you generate a text box with the pyplot.annotate function or the axes object’s annotated method; you can use the xycoords keyword to specify if the text location is specified as data coordinates, figure fractional coordinates or axes fractional coordinates. But this keyword is missing with the pyplot.text function and only data coordinates can be used to specify the text location, which is generally not what programmers want.

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