Object Oriented Programming.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Created by Berkay Alan**\n",
    "\n",
    "**Object Oriented Programming**\n",
    "\n",
    "**20 of November, 2020**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content\n",
    "\n",
    "- What is object oriented programming?\n",
    "- Defining Classes\n",
    "- Instantiation - Creating objects\n",
    "- Class and Instance Attributes\n",
    "- Instance(Object) Methods\n",
    "- Inheritance\n",
    "- Overriding - Extending the Functionality of a Parent Class\n",
    "- super() keyword"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What is Object oriented programming?\n",
    "\n",
    "Object-oriented programming is a programming paradigm that provides a means of structuring programs so that properties and behaviors are bundled into individual objects.\n",
    "\n",
    "For instance, an object could represent a person with properties like a name, age, and address and behaviors such as walking, talking, breathing, and running. Or it could represent an email with properties like a recipient list, subject, and body and behaviors like adding attachments and sending.\n",
    "\n",
    "Resource: https://realpython.com/python3-object-oriented-programming/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Defining Classes\n",
    "\n",
    "Classes are used to create user-defined data structures. Classes define functions called methods, which identify the behaviors and actions that an object created from the class can perform with its data."
   ]
  },
  {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " A class is a blueprint for how something should be defined. It doesn’t actually contain any data. The Car class specifies that a brand and an age are necessary for defining a car, but it doesn’t contain the brand or age of any specific dog.\n",
    "\n",
    " While the class is the blueprint, an **instance** is an object that is built from a class and contains real data. An instance of the car class is not a blueprint anymore. It’s an actual car with a brand, like Audi, what’s seven years old.\n",
    "\n",
    "Car >> Class\n",
    "\n",
    "7 years old Audi >> An object(instance) of Car class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class car(): # Classes can be defined with class keyword, you can give spesific names to it.\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "type"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.car"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.car at 0x10b46b730>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Car class is not very useful right now, it doesn't contain spesific properties of cars such as brand, model , age, price, color and so on. \n",
    " \n",
    " The properties that all Car objects must have are defined in a method called .__init__(). Every time a new Car object is created, .__init__() sets the initial state of the object by assigning the values of the object’s properties. That is, .__init__() initializes each new instance of the class.\n",
    "\n",
    " You can give .__init__() any number of parameters, but the first parameter will always be a variable called self. When a new class instance is created, the instance is automatically passed to the self parameter in .__init__() so that new attributes can be defined on the object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Car: # Car class with brand and age properties\n",
    "    def __init__(self, brand, age):\n",
    "        self.brand = brand\n",
    "        self.age = age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** Notice that the .__init__() method’s signature is indented four spaces. The body of the method is indented by eight spaces. This indentation is vitally important. It tells Python that the .__init__() method belongs to the Car class.\n",
    "\n",
    " Attributes created in .__init__() are called instance attributes. An instance attribute’s value is specific to a particular instance of the class. All Car objects have a brand and an age, but the values for the brand and age attributes will vary depending on the car instance."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " On the other hand, class attributes are attributes that have the same value for all class instances. You can define a class attribute by assigning a value to a variable name outside of .__init__()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Car: \n",
    "    # Class attribute\n",
    "    number_of_door = 4 #This class is spesific for 4-door cars\n",
    "    \n",
    "    def __init__(self, brand, age):\n",
    "        self.brand = brand\n",
    "        self.age = age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car.number_of_door"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Class attributes are defined directly beneath the first line of the class name and are indented by four spaces. They must always be assigned an initial value. When an instance of the class is created, class attributes are automatically created and assigned to their initial values.\n",
    "\n",
    " Class attributes are used to define same properties for every object of a class. In this example, every car has automatically 4 door."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instantiation - Creating objects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Car: \n",
    "    pass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Creating a new object from a class is called **instantiating** an object. You can instantiate a new Car object by typing the name of the class, followed by opening and closing parentheses."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Car at 0x10b6fd490>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Car() # This code is a memory address that indicates where the Car object is stored in your computer’s memory. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "car1 = Car() # We can assign car object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Car at 0x10bd65fd0>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Class and Instance Attributes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Car: \n",
    "    # Class attribute\n",
    "    number_of_door = 4 #This class is spesific for 4-door cars\n",
    "    \n",
    "    def __init__(self, brand, age):\n",
    "        self.brand = brand\n",
    "        self.age = age"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** To instantiate objects of this Car class, you need to provide values for the brand and age. If you don’t, then Python raises a TypeError"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "car1 = Car(\"Audi\",9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Car at 0x10b62f820>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car1.age # Reaching property of an object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Audi'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car1.brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car1.number_of_door # You can access class attributes the same way"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "car2 = Car(\"Volvo\",3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Car at 0x10b740220>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Volvo'"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car2.brand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "car2.age = 6 # Values of objects can be changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car2.age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "car3 = Car(\"Renault\",7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car3.age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "car3.number_of_door= 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car3.number_of_door"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The key takeaway here is that custom objects are mutable by default. An object is mutable if it can be altered dynamically. For example, lists and dictionaries are mutable, but strings and tuples are immutable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Instance(Object) Methods\n",
    "\n",
    "Instance(Object) methods are functions that are defined inside a class and can only be called from an instance of that class. Just like .__init__(), an instance method’s first parameter is always self."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Car: \n",
    "    # Class attribute\n",
    "    number_of_door = 4 #This class is spesific for 4-door cars\n",
    "    \n",
    "    def __init__(self, brand, age,model,color): # init is a dunder method because of __ at beginning and end\n",
    "        self.brand = brand\n",
    "        self.age = age\n",
    "        self.model = model \n",
    "        self.color = color\n",
    "    \n",
    "    # Instance(Object) method\n",
    "    def change_color(self,new_color):\n",
    "        self.color = new_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__module__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " 'change_color',\n",
       " 'number_of_door']"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(Car) # We can see the method here"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "car1 = Car(\"Dacia\",4,\"Duster\",\"Blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<__main__.Car at 0x10b6a8340>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car1.age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Blue'"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car1.color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [],
   "source": [
    "car1.change_color(\"Red\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Red'"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "car1.color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Inheritance\n",
    "\n",
    " Inheritance is the process by which one class takes on the attributes and methods of another. Newly formed classes are called child classes, and the classes that child classes are derived from are called parent classes.\n",
    " \n",
    "  Child classes can override or extend the attributes and methods of parent classes. In other words, child classes inherit all of the parent’s attributes and methods but can also specify attributes and methods that are unique to themselves."
   ]
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Car: \n",
    "    # Class attribute\n",
    "    number_of_door = 4 #This class is spesific for 4-door cars\n",
    "    \n",
    "    def __init__(self, brand, age,model,color): # init is a dunder method because of __ at beginning and end\n",
    "        self.brand = brand\n",
    "        self.age = age\n",
    "        self.model = model \n",
    "        self.color = color\n",
    "    \n",
    "    # Instance(Object) method\n",
    "    def change_color(self,new_color):\n",
    "        self.color = new_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "car1= Car(\"Lotus\",3,\"AA\",\"Purple\") #This is Racecar in normal\n",
    "car2= Car(\"Rover\",7,\"Stepway\",\"Red\") #This is SUV in normal\n",
    "car3= Car(\"Fiat\",6,\"A5\",\"Green\") #This is compact in normal"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s create a child class for each of the three car type mentioned above: Racecar, SUV and Compact\n",
    "\n",
    " In order to create a child class, you create new class with its own name and then put the name of the parent class in parentheses. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RaceCar(Car): \n",
    "    pass\n",
    "class SUV(Car):\n",
    "    pass\n",
    "class Compact(Car):\n",
    "    pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "lotus = RaceCar(\"Lotus\",3,\"AA\",\"Purple\")\n",
    "jeep = SUV(\"Jeep\",7,\"A4GG\",\"Red\")\n",
    "fiat= Compact(\"Fiat\",6,\"A5\",\"Green\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "__main__.RaceCar"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(lotus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lotus.number_of_door"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Purple'"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lotus.color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Note:** If you want to determine if lotus is also an instance of the Car class, you can do this with the built-in isinstance()."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(lotus,Car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(jeep,Car)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isinstance(lotus,SUV)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overriding - Extending the Functionality of a Parent Class\n",
    "\n",
    "Since different car types have slightly different colors, you want to provide a default value for the color argument of their respective .change_color() methods. To do this, you need to override .change_color() in the class definition for each car type. It is **overriding.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Car: \n",
    "    # Class attribute\n",
    "    number_of_door = 4 #This class is spesific for 4-door cars\n",
    "    \n",
    "    def __init__(self, brand, age,model,color): # init is a dunder method because of __ at beginning and end\n",
    "        self.brand = brand\n",
    "        self.age = age\n",
    "        self.model = model \n",
    "        self.color = color\n",
    "    \n",
    "    # Instance(Object) method\n",
    "    def change_color(self,new_color):\n",
    "        self.color = new_color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to override a method defined on the parent class, you define a method with the same name on the child class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RaceCar(Car): \n",
    "    def change_color(self,new_color=\"Red\"):\n",
    "        self.color = new_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [],
   "source": [
    "lotus = RaceCar(\"Lotus\",3,\"AA\",\"Purple\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "lotus.change_color()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Red'"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lotus.color # Color changed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [],
   "source": [
    "lotus.change_color(\"White\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'White'"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lotus.color"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## super() keyword\n",
    "\n",
    "The super() function is used to give access to methods and properties of a parent or sibling class. This function returns an object that represents the parent class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Car: \n",
    "    # Class attribute\n",
    "    number_of_door = 4 #This class is spesific for 4-door cars\n",
    "    \n",
    "    def __init__(self, brand,color): # init is a dunder method because of __ at beginning and end\n",
    "        self.brand = brand\n",
    "        self.color = color\n",
    "    \n",
    "    # Instance(Object) method\n",
    "    def change_color(self,new_color):\n",
    "        self.color = new_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "class RaceCar(Car): \n",
    "    \n",
    "    def __init__(self, brand, color,size):\n",
    "        super().__init__(brand, color)\n",
    "        self.size=size\n",
    "    \n",
    "    def change_color(self,new_color):\n",
    "        self.color = new_color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "lotus = RaceCar(\"Lotus\",\"Purple\",312)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "312"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lotus.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "lotus.change_color(\"Blue\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Blue'"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lotus.color"
   ]
  }
 ],
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