classes_and_objects_basics.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "35cbd117",
   "metadata": {},
   "source": [
    "# Example 1: Creating Class and Object in Python"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff3f63fc",
   "metadata": {},
   "source": [
    "Class \n",
    "\n",
    "A class is a collection of objects. \n",
    "A class contains the blueprints or the prototype from which the objects are being created. \n",
    "It is a logical entity that contains some attributes and methods. \n",
    "\n",
    "To understand the need for creating a class let’s consider an example, let’s say you wanted to track the number of dogs that may have different attributes like breed, age. If a list is used, the first element could be the dog’s breed while the second element could represent its age. Let’s suppose there are 100 different dogs, then how would you know which element is supposed to be which? What if you wanted to add other properties to these dogs? This lacks organization and it’s the exact need for classes. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d7e9d7ab",
   "metadata": {},
   "source": [
    "Class Definition Syntax:\n",
    "\n",
    "class ClassName:\n",
    "\n",
    "   #Statement-1\n",
    "   .\n",
    "   \n",
    "   .\n",
    "   \n",
    "   .\n",
    "   \n",
    "    Statement-N"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e022207",
   "metadata": {},
   "source": [
    "Objects\n",
    "\n",
    "The object is an entity that has a state and behavior associated with it. \n",
    "It may be any real-world object like a mouse, keyboard, chair, table, pen, etc. Integers, strings, floating-point numbers, even arrays, and dictionaries, are all objects. \n",
    "More specifically, any single integer or any single string is an object. \n",
    "The number 12 is an object, the string “Hello, world” is an object, a list is an object that can hold other objects, and so on. \n",
    "You’ve been using objects all along and may not even realize it.\n",
    "\n",
    "An object consists of :\n",
    "\n",
    "State: It is represented by the attributes of an object. It also reflects the properties of an object.\n",
    "\n",
    "Behavior: It is represented by the methods of an object. It also reflects the response of an object to other objects.\n",
    "\n",
    "Identity: It gives a unique name to an object and enables one object to interact with other objects.\n",
    "\n",
    "To understand the state, behavior, and identity let us take the example of the class dog (explained above). \n",
    "\n",
    "- The identity can be considered as the name of the dog.\n",
    "\n",
    "- State or Attributes can be considered as the breed, age, or color of the dog.\n",
    "\n",
    "- The behavior can be considered as to whether the dog is eating or sleeping."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1d1c3877",
   "metadata": {},
   "source": [
    "# The self  "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79c6773b",
   "metadata": {},
   "source": [
    "Class methods must have an extra first parameter in the method definition.\n",
    "\n",
    "We do not give a value for this parameter when we call the method, Python provides it\n",
    "If we have a method that takes no arguments, then we still have to have one argument.\n",
    "This is similar to this pointer in C++ and this reference in Java.\n",
    "\n",
    "When we call a method of this object as myobject.method(arg1, arg2), this is automatically converted by Python into MyClass.method(myobject, arg1, arg2) – this is all the special self is about."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "90013b5c",
   "metadata": {},
   "source": [
    "# The __init__ method "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b5c82122",
   "metadata": {},
   "source": [
    "The __init__ method is similar to constructors in C++ and Java. It is run as soon as an object of a class is instantiated. The method is useful to do any initialization you want to do with your object. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a570a740",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "fruit name is: Apple\n"
     ]
    }
   ],
   "source": [
    "class Fruits:\n",
    "    \n",
    "    def __init__(self, name, color):\n",
    "        self.name = name\n",
    "        self.color = color\n",
    "        \n",
    "    def call_the_fruit_name(self):\n",
    "        print(\"fruit name is:\", self.name)\n",
    "        \n",
    "        \n",
    "fruit_obj = Fruits(\"Apple\", \"Red\")\n",
    "fruit_obj.call_the_fruit_name()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ca5f5b83",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "My name is Rodger\n",
      "My name is Tommy\n"
     ]
    }
   ],
   "source": [
    "class Dog:\n",
    "\n",
    "\t# class attribute\n",
    "\tattr1 = \"mammal\"\n",
    "\n",
    "\t# Instance attribute\n",
    "\tdef __init__(self, name):\n",
    "\t\tself.name = name\n",
    "\t\t\n",
    "\tdef speak(self):\n",
    "\t\tprint(\"My name is {}\".format(self.name))\n",
    "\n",
    "# Driver code\n",
    "# Object instantiation\n",
    "Rodger = Dog(\"Rodger\")\n",
    "Tommy = Dog(\"Tommy\")\n",
    "\n",
    "# Accessing class methods\n",
    "Rodger.speak()\n",
    "Tommy.speak()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff60925b",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "509a0955",
   "metadata": {},
   "source": [
    "# Encapsulation "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8790aa5b",
   "metadata": {},
   "source": [
    "- It is a mechanism of wrapping the data(variables/attributes) and code acting on the data(methods/functions) together as a single unit."
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "id": "83916ff6",
   "metadata": {},
   "source": [
    "![Screenshot%20from%202023-01-30%2020-15-08.png](attachment:Screenshot%20from%202023-01-30%2020-15-08.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6cb05220",
   "metadata": {},
   "source": [
    "# Constructors in Python\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e38819e5",
   "metadata": {},
   "source": [
    "- A constructor is a special type of method(function) which is used to initialize the members(class variables & class methods) of the class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a8ca82a3",
   "metadata": {},
   "source": [
    "Constructors can be of 2 types:\n",
    "\n",
    "1. Parameterized Constructor\n",
    "\n",
    "2. Non-Parameterized Constructor"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc186ccb",
   "metadata": {},
   "source": [
    "# Creating the constructor in Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "02ee507c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(111, 'ABC')\n",
      "(789, 'XYZ')\n"
     ]
    }
   ],
   "source": [
    "class Employee:\n",
    "    \n",
    "    def __init__(self, name, id):\n",
    "        self.id  = id\n",
    "        self.name = name\n",
    "        \n",
    "    def display(self):\n",
    "        return (self.id, self.name)\n",
    "    \n",
    "emp1 = Employee(\"ABC\", 111)\n",
    "emp2 = Employee(\"XYZ\", 789)\n",
    "print(emp1.display())\n",
    "print(emp2.display())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "17d2380c",
   "metadata": {},
   "source": [
    "# Non-parametrized Constructor:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b2c32f6",
   "metadata": {},
   "source": [
    "- this constructor has only self as argument.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "545b5180",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is non parameterized constructor\n",
      "Hello\n"
     ]
    }
   ],
   "source": [
    "class Student:\n",
    "    def __init__(self, name):\n",
    "        print(\"This is non parameterized constructor\")\n",
    "        \n",
    "    def show(self):\n",
    "        print(\"Hello \" )\n",
    "        \n",
    "stud = Student()\n",
    "stud.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56a3dcc5",
   "metadata": {},
   "source": [
    "- as we define the object of the class, the __init__ method, that is the constructor of the class is invoked."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7c9abca6",
   "metadata": {},
   "source": [
    "# Parameterized Constructor:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e0806f94",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This is non parameterized constructor\n",
      "Hello abc\n"
     ]
    }
   ],
   "source": [
    "class Student:\n",
    "    def __init__(self, name):\n",
    "        print(\"This is non parameterized constructor\")\n",
    "        self.n = name\n",
    "        \n",
    "    def show(self):\n",
    "        print(\"Hello \" + self.n)\n",
    "        \n",
    "stud = Student(\"abc\")\n",
    "stud.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff7430e2",
   "metadata": {},
   "source": [
    "# Default Constructor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "94fc5fb3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "101 abc\n"
     ]
    }
   ],
   "source": [
    "class Student:\n",
    "    \n",
    "    roll_no = 101\n",
    "    name = \"abc\"\n",
    "    \n",
    "    def display(self):\n",
    "        print(self.roll_no, self.name)\n",
    "        \n",
    "st = Student()\n",
    "st.display()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b683ca4b",
   "metadata": {},
   "source": [
    "# Classses and Ojects Example:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d34ff47",
   "metadata": {},
   "source": [
    "1. Create a class Fruit, in that class that user input fruit name and colour. \n",
    "2. Create a class Juice which inherits class Fruit and give in ouput the colour."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "dc0cd487",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "enter fruit name :mango\n",
      "enter color of fruit:yellow\n",
      "color of fruit : yellow\n"
     ]
    }
   ],
   "source": [
    "class Fruits:\n",
    "    def __init__(self,fruit_name,color):\n",
    "        self.fruit_name=fruit_name\n",
    "        self.color=color\n",
    "    def color_fruit(self):\n",
    "        print(\"color of fruit : \"+self.color)\n",
    "\n",
    "class Juice(Fruits):\n",
    "    def display(self):\n",
    "        return self.color_fruit\n",
    "\n",
    "inp1=input(\"enter fruit name :\")\n",
    "inp2=input(\"enter color of fruit:\")\n",
    "obj=Juice(inp1,inp2)\n",
    "obj.color_fruit() \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7f0d78c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ccb9b870",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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