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Object-Oriented Programming in Python

Object-Oriented Programming (OOP) is a programming paradigm that organizes code around objects and classes rather than functions and logic. OOP principles—such as encapsulation, inheritance, and polymorphism—enable developers to structure code in a modular, reusable, and maintainable way. This is especially useful in data science projects that require the handling of complex data structures, algorithms, and models.

In this article, we'll cover the fundamentals of OOP in Python and explore how it can be applied to structured data science workflows.

1. What is Object-Oriented Programming (OOP)?​

At its core, OOP revolves around the concept of objects, which bundle data (attributes) and functions (methods) together. These objects are instances of classes, which define the blueprint for the data and behavior the object will have.

The four main pillars of OOP are:

  1. Encapsulation: Bundling data and methods that operate on that data within a class.
  2. Abstraction: Hiding complex implementation details and exposing only the necessary parts.
  3. Inheritance: Allowing new classes to inherit attributes and methods from existing classes.
  4. Polymorphism: Allowing methods to have different behaviors based on the object calling them.

2. Defining Classes and Creating Objects​

Defining a Class​

A class defines a blueprint for an object. Inside a class, you define attributes (variables) and methods (functions).

class Vehicle:
    def __init__(self, make, model, year):
        # Constructor method to initialize the object's attributes
        self.make = make
        self.model = model
        self.year = year

    def start_engine(self):
        # Method to simulate starting the engine
        return f"The engine of {self.make} {self.model} is starting."