Every programming language has its own unique characteristics that focus on a particular practice and style of coding. While some programming languages were developed for beginners and ease of accessibility, other languages were developed for very specific careers and projects. object-oriented programming (OOP) is a method of structuring a program or code based on the creation and manipulation of discrete objects. Commonly employed when using Python, object-oriented programming is popular among collaborative teams of data scientists working with real-world data. Data scientists seeking more advanced training in the practical applications of Python can further develop their skills through object-oriented programming.
What Is Object-Oriented Programming?
Object-oriented programming is a model of computer programming focused on defining objects and their data. Within computer programming, objects are units of code, i.e., functions, methods, or a specific data structure (like tables and columns), that also contain values used to make comparisons or analyses. Each object is composed of a state and behavior, or fields and methods, which include specific variables, properties, and attributes that define the object, its data, and how it can be used. In contrast to the programming models that focus on functions and logic, such as rule-based machine learning or more statistics-based coding, object-oriented programming makes it easier to work with real-world data which exists in a variety of forms.
For example, a company may manage a relational database that contains data on its clients or customers. In this database, an object can be defined as a column of data entries about the clients, such as their names and addresses. By focusing on objects, this type of programming is also beneficial for programmers and developers working with code on collaborative projects or the creation of a product, because OOP code is easily editable, so it can be reused and shared among community members. Object-oriented programming is used for open-source coding, software development, and database management.
The Role of Object-Oriented Programming with Python
Python is an open-source coding language, so it can be combined with many programs and tools to suit a wide range of project needs. Object-oriented programming is commonly deployed through Python within multiple fields and industries. When coding with Python, OOP is popular for its flexibility, efficiency, and data security. So many Python users rely on this style of coding to create projects and products that can be completed by a group or an individual.
The Structure of Object-Oriented Python Programs
There is a specific structure data scientists and developers can apply when writing code using OOP through Python. These Python programs are structured by calling and defining the classes, objects, methods, and attributes of the program. Classes are entities defined by the Python user that lay out the framework for the objects, methods, and attributes. Objects are a type of class that corresponds to real-world data and are also composed of both methods and attributes. Methods are used to define the behavior of the objects within a class, while attributes are used to define the state of an object.
Properties of the method must be established, its variables and parameters defined, and attributes added to the class. These attributes include class attributes (which apply to the entire class) or instance attributes (which only apply to a specific instance of the class). Each attribute corresponds to a specific value and is generally used to give some meaning to the variables in the dataset. Assigning value to these attributes also communicates to the machine which data to return when calling upon an object.
When writing Python code using the structure of object-oriented programming, data scientists and developers begin by defining the class using a single statement. Once the class name is established, the object can be created by defining the properties of the specified method for the object. This process of creating new objects from a class is known generally as instantiation. Instantiating an object is the foundation of object-oriented programming and is usually followed by encapsulation.
Encapsulation is using a unit of code to bundle–or encapsulate–all of the data and methods used to create and define an object, allowing data scientists to create complex arguments in fewer lines of code, as well as hide the data from other objects and direct external access to prevent accidental data modification.
Object-Oriented Programming in the Data Science Industry
Object-oriented programming is widely used in the data science industry to find and create solutions to real-world problems, as well as model hierarchical relationships, e.g., in a business or school. By being able to create and define objects, data scientists can control the composition of their code and data. When working with real-world data, it is especially important to be able to set and define the variables and attributes of an object because they will be very specific to the project or product in development. Returning to the client example, different companies may have different attributes for their client objects, so being able to define that object ensures that the program created is relevant to that company. Then, once those objects are established, they can continue to be used in any new projects that are created.
Object-oriented programming also allows Python users to easily reuse code by creating objects with specific methods and attributes. When working on a data science team or an online community of developers, this is useful because each team member can draw from the same library or resources to complete their tasks, such as data cleaning and organization. In this sense, reusability ensures that the process of coding is not only more efficient but reliable and reproducible as well, regardless of which team member is using the code. Most data scientists use object-oriented programming when they require a well-recorded structure or design for their programs that can be shared with others.
Interested in learning more about Object-Oriented Programming?
Object-oriented programming is an essential skill for data scientists working on projects relevant to an entire team or community of programmers and stakeholders. Many of Noble Desktop’s Data Science classes include some training in object-oriented programming, particularly bootcamps and certificates focused on Python. These Python classes include the Python for Data Science Bootcamp, which offers students hands-on instruction with real-world datasets through object-oriented programming and the fundamentals of Python. The Python Programming Bootcamp also focuses on object-oriented programming, teaching students how to create objects, applications, and projects for a data science portfolio.