Object-Oriented is a programming paradigm that follows the concept of classes and objects in place of functions and logic. It is also known as the fancy way of coding that organizes the code in a way that increases the code readability and maintainability. OOP concept is an important topic in programming and helps to build reusable modules for a variety of tasks in Data Science.

This is often a pre-requisite while building Deep learning models using various libraries such as Pytorch where the base model is reused to add custom layers. Let’s explore what this concept teaches and how to apply this in practical use cases.

What is the OOP concept?

Consider a smartphone that can be of any brand, but they’re a variety of common things among all of them. All have screens, speakers, buttons and on the software level, almost all of them are android powered. Now consider a case where every company is making their software from scratch, even the kernel which controls most of the hardware components.

This would become a tedious and expensive process, therefore, increasing the price of the devices. What if there is an abstract or generalized model that can be changed over time by any manufacturer according to their requirements? This concept tries to capture this class-based method where the code is structured in classes with different accessor methods.

History of Object-Oriented Programming

Alan Kay coined the term object-oriented programming back in 1966. While we now learn about OOPs in Python, the first programming language with object-oriented programming features was Simula. The programming language was used for simulation programs, and the most important information in it was referred to as objects. 

Even though OOPs have been in the market since the early 1960s, they became more popular in the 1990s. C++ has a huge role in popularizing object-oriented programming. Today, object-oriented programming has real-world applications in machine learning, artificial intelligence, client-server systems, and more. 

Examples of OOPs

If you want a simplified understanding of OOPs concepts in Python with examples, let us consider a few objects. If you take into account a car, its properties will include price, color, model, brand, and more. The behavior and function of the car will be gear change, slowing down, and acceleration. 

Similarly, the properties would be breed, color, and weight when you consider a dog. Its behavior or function would be walking, playing, or barking. 

The ability to implement real-world entities like inheritance, hiding, and objects make OOP popular. The different features of OOP in Python make data visualization easier. 

Difference Between Object-Oriented Programming and Procedure-Oriented Programming

POP demands a particular procedure of steps. In procedure-oriented programming, you will come across different functions for particular tasks. These functions are organized in a specific sequence to handle the flow of the program. 

But OOP includes different objects. Therefore, the program gets divided into various objects in the object-oriented programming approach. These entities can combine various properties and behavior of real-world objects. 

Procedure-oriented programming is only applicable for small tasks because the code becomes more complex due to a lengthy program. It creates a web of functions that is extremely challenging to debug. 

Object-oriented programming can solve this issue with a clear and uncomplicated structure. It enables code reusability in the form of inheritance. 

Moreover, procedure-oriented programming enables all functions to access data. It leads to a lack of security. If you want to secure any critical information, the procedural approach won’t offer you that level of security. But the encapsulation features of OOP in Python will offer all the security you need. 

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What are Classes and Objects?

Classes are the blueprints of what has to be implemented. If we consider our previous example, we can have functionalities to call a person, receive calls, messages, play music, or do some other stuff.

All these things are common for every smartphone, their internal working is also similar and they can be considered as a class of smartphone functions or a class. Objects can be defined as all the smartphone brands that will use this common implementation in their products with modifications.

There can be multiple instances of this base class, and every instance can hold a different state of values without interfering with other objects. In Python,  a class can be declared by using the reserved keyword class. Further, __init__ constructor is used to initialize the class variables.

class Company:
def __init__ (self):
self.name = ‘upGrad’
def  display_name (self):
print(f”Company name is: {self.name}”)
cm = Company()
cm.display_name()

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Different Pillars of OOP

Now that we are familiar with the basic building blocks of this paradigm, let’s look at some of the most important features/characteristics of this concept:

Encapsulation

This states that methods (or functions) of the class and the data associated with it are encapsulated or protected from accidental or external access. This means that attributes that are defined in private or protected scope are not accessible outside the class.

There is a concern for Python that there is no concept of private variables in this language, so the attributes are accessible outside the class.

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There is a way to recognize private attributes by using a double underscore at the beginning of the declaration and if you try to access this outside the class via the object of the same, you will be prompted with AttributeError because Python applies name mangling whenever it detects a private variable. This doesn’t give any security to your attributes because they are still accessible.

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Inheritance

As the word suggests, it is taking a portion of an existing class called a parent class to a new class called a child class with little or no changes. We can connect this to our example in this way that all smartphone brands inherit a generic phone class that will help them perform basic functions, plus they can add their extra codes to enhance the user experience according to their needs. In Python, inheriting a class is done by:

class A:

some content

class B(A):

content of the derived class

There is another concept related to inheritance called function overriding. Suppose the camera function of the generic smartphone is not so good, and the manufacturer has a better solution for this. They can directly override this function by defining it again in the child class and apply the changes over there. 

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Abstraction

It defines the blueprint or an interface to the subclasses implementations. It means some methods are defined in the base class which is not fully implemented and only an abstract view is defined. It can help in tracking the various features of the module and sub-modules to be created.

For instance, some smartphones support NFC (near field connectivity) and this functionality can be defined in the base class and its implementation can be coded in the child class of the resultant phone. In this way, the abstract base class can provide the overall view of the module and subsequent implementations. Here is an example:

class Phone:
def camera(self):
pass
def NFC(self):
pass
class Xyz(Phone):
def NFC(self):
return True

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Polymorphism

If we go by the root meaning, it means multiple forms of the same thing. Polymorphism defines the functions based on the number or types of arguments passed. For instance, the length function in Python can take any type of iterable or object and returns the integer length.

This can also be quoted as function overloading but here is a catch in the Python language. We cannot define the same name functions with different arguments and if done, then it considers only the last entry. 

Practical Use Cases of OOP

We have seen what this concept is all about and what features it offers. Have a look at some examples where you can apply this concept:

Jinja Templating: If you have some experience with Python’s Flask framework which handles the routes and the server-side, this templating helps in handling this data on the front-end. Generally, a base HTML file is created which is then inherited by all the pages to have the same layout throughout the website. 

Kivy Applications: This is a library that allows you to build cross-platform (android and IOS) GUI-based python applications and here most of the programming is based on the OOP concept.

ORM: Object-relational Mappers offers a way to define the relational databases in application code using any language. For instance, in Django, you can define different types of models using classes for different types of users.

AI Expert System: OOPs holds the potential to develop AI expert systems with the help of forward and backward chaining for deriving a conclusion. The chaining revolves around different derivations and conditions to deliver the result. Different OOP capabilities can help make an AI system more reliable and responsive. 

Office Automation Systems: Companies often use automated systems to communicate and share information with people inside and outside the organization. RPA or robotic programming automation is useful for developing automation systems. These RPA models are based on object-oriented programming. 

Computer-Aided Design: CAD is useful for creating, modifying, evaluating, and improving designs. These models are also used in bigger systems to find out whether they function well. OOP is useful for building applications that help reduce the requirement of manual efforts. 

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Conclusion

In this article, we discussed what is OOP concept, it’s building blocks (classes and objects), different pillars, and highlighted some examples where this paradigm is adopted. There are numerous places where this method of programming is considered due to better code management, collaboration, and providing abstract functionalities to other programs dependent on this.

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