class Student:
def __init__(self, name):
self.name = name

s1 = Student("Ramya")
print(s1.name)

💥Understanding OOP in Python

Today I explored Object-Oriented Programming (OOP) in Python — a very important concept in software development and data analytics.

OOP helps us write clean, reusable, and scalable code, especially when building data pipelines and machine learning projects.

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🧠 What is OOP?

OOP is a programming approach where we organize code using Classes and Objects.

✅ Class

A class is a blueprint/template for objects.

✅ Object

An object is an instance of a class.

📌 Example

class Student:
def __init__(self, name):
self.name = name

s1 = Student("Ramya")
print(s1.name)

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🏛️ Four Pillars of OOP With Examples

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1️⃣ Encapsulation

Encapsulation means wrapping data (variables) and methods together, and controlling access.

class BankAccount:
def __init__(self, balance):
self.__balance = balance   # private variable

def deposit(self, amount):
self.__balance += amount

def get_balance(self):
return self.__balance

acc = BankAccount(5000)
acc.deposit(2000)
print(acc.get_balance())

Why useful: Hides sensitive data like user credentials or balance.

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2️⃣ Inheritance

Inheritance allows one class to acquire properties of another class.

class Animal:
def sound(self):
print("Animals make sound")

class Dog(Animal):
def sound(self):
print("Dog barks")

d = Dog()
d.sound()

Why useful: Helps reuse code — no repeating functions.

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3️⃣ Polymorphism

Polymorphism means same function name, different behavior.

class Shape:
def area(self):
pass

class Square(Shape):
def area(self):
return "Area = side * side"

class Circle(Shape):
def area(self):
return "Area = π * r * r"

print(Square().area())
print(Circle().area())

Why useful: Makes code flexible — same function works differently depending on object.

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4️⃣ Abstraction

Abstraction means hiding implementation details and showing only necessary information.

from abc import ABC, abstractmethod

class Vehicle(ABC):
@abstractmethod
def start(self):
pass

class Car(Vehicle):
def start(self):
print("Car engine starts")

c = Car()
c.start()

Why useful: Simplifies complex code, improves security.

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💡 Why OOP is useful in Data Analytics?

✅ Reusable data preprocessing functions ✅ Cleaner code for ML pipelines ✅ Object models for datasets, features, models ✅ Better collaboration & scalability

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👣 My Progress Continues

Today was all about foundations, and these concepts are key for becoming a professional Data Analyst and understanding machine learning frameworks like Scikit-Learn, PyTorch, etc.

More learning every day… one step at a time 💪

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🏷️ Tags

#python #beginners #dataanalytics #learning #oop #devto

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