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Procedural Python Coding versus Object-Oriented Programming- Which One to Learn

By Editorial Team on 2018-08-11

Python is very flexible coding language as it can be used as an Object Oriented Programming or OOP and procedural coding language. Such flexibility makes it easy for anyone to learn. For example, Java can only be used as an OOP language; that is why it is more difficult to learn than Python. This article articulates differences between two dominant methods of coding in Python: OOP and procedural.


Python has been used in many applications since its creation. Developers have used it to create libraries for itself, libraries for other languages, bindings for C and C++, games, basic prototyping, and the list goes on. Python has a lot of language features and as it has approached its 3rd version, it comes built in with so many features, developers barely feel like they are even writing code. Because of Python’s wide use cases, its basic paradigm for developers must have a variety as well. Languages like OCaml, with inherent pattern matching and currying, are often useful in recursive implementations of algorithms such as AVL trees or language parsers which can benefit from a recursive paradigm.


Python allows developers to develop in a both procedural way and an object oriented way. The object oriented paradigm of Python can often feel a little forced, but nonetheless still exists. Python classes can have instance variables, static variables, methods and data fields while maintaining object oriented inheritance and polymorphism. Nonetheless because Python was introduced as a scripting language, its object oriented nature can feel a little forced for new developers.


For new developers it is often advisable to learn procedural Python first. Procedural languages are the basis of every language. Languages like BASIC and Fortran were procedural and ultimately are the simplest way computers process programs. It also mitigates a lot of issues as those not completely well-versed in the object oriented paradigm will not end up breaking the program dealing with a new system they don’t completely understand. Additionally, by doing procedural Python, library functionality is not any different. While libraries like Numpy and Scipy handle their own computations in their own abstractions, this is through their own objects and classes. Developers don’t necessarily need to be well versed in the object oriented paradigms to understand or even use the features offered by such libraries.


Python notebooks have also grown in popularity in recent years, mainly for researchers and data analysts. Python notebooks ultimately support a very procedural approach to developers. As the Python notebooks have their own code fields where developers can write code and view the output, defining classes can often be verbose for what is trying to be done. Python notebooks almost force developers to write code in such a way where it can run from the beginning to end with a few outputs to view. Having objects and classes is secondary and pushed away to outside libraries which could then be used within the Python notebook.


While procedural Python is used quite a bit, object oriented Python has a useful place. In HTTP servers, object oriented Python enables developers to more easily handle connections. The object oriented nature of Python makes it easier to structure data and manage methods that can be run on such data. Additionally, in the creation of things like data structures, like graphs, Python’s object oriented nature is extremely useful as it enables implementations of them to be much more streamlined.


Ultimately Python’s object oriented paradigm is for developers to be able to write libraries for other developers while offering a much simpler abstraction. Developers don’t need to write their own methods to handle data when libraries can do it for them. Using procedural Python, it could be possible to handle data, but using classes and objects, data handling is considerably easier. If data structures and data handling is your interest, object oriented Python would be more effective, while for data analytics and data science, learning procedural Python may save a lot of time.


Programming Languages and Resources for Software Developers
The most common programming languages for software engineers are C, C++, Python, and Java. Also, for building native mobile Apps, iOS Swift and Java Android are used for building iPhone and Android Apps respectively.

Python coding is well suited for those interested in pursuing a career in software engineering; however, other options are  system admin, web design and development and mobile App design and development. It is advisable to consult with an IT career counselor to understand what career options best fits your skills. For instance, if you want to be a software engineer, learning HTML and CSS might not fit the bill. Here is an excellent article for learning more on coding and technology career roadmap. Once you know what career path you wish to pursue, you can make a plan on what, when, and how to learn. There are lots of online resources for learning coding and technology in general. For teenagers and high school students, High School Technology Services offers variety of hands-on training. For adults and professionals, Coding Bootcamps and DC Web Makers Companies offer basic to advance project-based programming and technology classes.


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