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Dive into exploring the four key pillars of Object-Oriented Programming in Python!

Object-Oriented Programming

When a program starts to get big, classes might start to share functionality or we may lose sight of the purpose of a class's inheritance structure. In order to alleviate issues like this, we can use the concept of _abstraction_.

Abstraction helps with the design of code by defining necessary behaviors to be implemented within a class structure. By doing so, abstraction also helps avoid leaving out or overlapping class functionality as class hierarchies get larger.
```py
from abc import ABC, abstractmethod

class Animal(ABC):
  def __init__(self, name):
    self.name = name

  @abstractmethod
  def make_noise(self):
    pass

class Cat(Animal):
  def make_noise(self):
    print("{} says, Meow!".format(self.name))

class Dog(Animal):
  def make_noise(self):
    print("{} says, Woof!".format(self.name))

kitty = Cat("Maisy")
doggy = Dog("Amber")
kitty.make_noise() # "Maisy says, Meow!"
doggy.make_noise() # "Amber says, Woof!"
```
Above we have `Cat` and `Dog` classes that inherit from `Animal`. The `Animal` class now inherits from an imported class `ABC`, which stands for Abstract Base Class. 

This is the first step to making `Animal` an abstract class that cannot be instantiated. The second step is using the imported decorator `@abstractmethod` on the empty method `.make_noise()`. 

The below line of code would throw an error.
```py
an_animal = Animal("Scruffy")

# TypeError: Can't instantiate abstract class Animal with abstract method make_noise
```

The abstraction process defines what an `Animal` is but does not allow the creation of one. The `.__init__()` method still requires a name, since we feel all animals deserve a name. 

The `.make_noise()` method exists since all animals make some form of noise, but the method is not implemented since each animal makes a different noise. Each subclass of `Animal` is now required to define their own `.make_noise()` method or an error will occur. 

These are some of the ways abstraction supports the design of an organized class structure.





OOP Pillar: Abstraction

When we hear the word “inheritance”, code may not be the first thing that springs to mind; we’re probably more likely to think of inheriting genetic traits, like the eye color from a mother or dimples from a grandfather. In the world of Object-Oriented Programming, inheritance is actually one of the core pillars for creating intricate structures with our classes. To dive into this concept, let's examine a `Dog` and `Cat` class: 

```py
class Dog:

  def bark(self):
    print('Woof!')

class Cat:

  def meow(self):
    print('Meow!')
```

These two classes define two distinct animals with their own methods of communication. Now, what if we wanted to give both of these classes the ability to eat by calling a method called `eat()`. We could write the method twice in both classes but then we would be repeating code! We also may need to write it inside every specific animal class we ever create.  Instead, we can utilize the power of inheritance.

Since both `Cat` and `Dog` fall under the classification of `Animal` we can create a _parent class_ to represent properties and methods they can both share! Here is what it might look like: 

```py
class Animal: 
  def eat(self): 
    print("Nom Nom Nom...eating food!")
```

Great, we have an `Animal` class with a `eat()` method, but how do we actually get the `Dog` and `Cat` class to **inherit** this method so it can be shared with both classes? Well here is what the base structure will look like: 

```py
class ParentClass:
  #class methods/properties...

class ChildClass(ParentClass):
  #class methods/properties...
```

If we apply this structure to our example, our code looks like this: 

```py
class Dog(Animal):
  def bark(self):
    print('Bark!')

class Cat(Animal):
  def meow(self):
    print('Meow!')
```

Now, let's see inheritance in action: 

```py
fluffy = Dog()
zoomie = Cat()

fluffy.eat() # Nom Nom Nom...eating food!
zoomie.eat() # Nom Nom Nom...eating food!
```
As we can see, there are some clear advantages of utilizing inheritance. Not only are we able to reuse methods across multiple classes using our _parent class_, but we are also able to create parent-child relationships between entities! 

OOP Pillar: Inheritance

Using _getter_, _setter_, and _deleter_ functions are one way to implement encapsulation within Python where the state of class attributes can be handled within the class. These functions are useful in making sure that the data being handled is appropriate for the defined class functionality.

```py
class Animal:
  def __init__(self, name):
    self._name = name
    self._age = None

  def get_age(self):
    return self._age

  def set_age(self, new_age):
    if isinstance(new_age, int):
      self._age = new_age
    else:
      raise TypeError

  def delete_age(self):
    print("_age Deleted")
    del self._age
```

Looking at the `Animal` class above there is an `_age` attribute with a single underscore. This notates it is intended to be used only within the module. There are then 3 methods related to `age` each with a different purpose. These define the getter, setter, and deleter of the specific property.

The first method related to `age` is a getter and returns `self._age`. The setter is implemented below that. It includes logic that ensures that the value passed to `new_age` is an integer. If so, `self._age = new_age`. If not, raise an error. This is useful and shows the power of using these functions for encapsulation.

The deleter is implemented below the setter. It outputs a confirmation message and uses the `del` keyword to delete the `self._age` attribute. 


```py
a = Animal("Rufus")
print(a.get_age()) # None

a.set_age(10)
print(a.get_age()) # 10

a.set_age("Ten") # Raises a TypeError

a.delete_age() # "_age Deleted"
print(a.get_age()) # Raises a AttributeError
```
Above we see `a.get_age()` gets the `_age` value, `a.set_age(10)` sets the value and `a.delete_age()` deletes the attribute entirely. A `TypeError` occurs with `a.set_age("Ten")` because the defined logic in the setter is looking only for an integer. An `AttributeError` occurs with `a.get_age()` after the attribute was deleted.

Getters, Setters and Deleters

_Encapsulation_ is the process of making methods and data hidden inside the object they relate to. Languages accomplish this with what are called access modifiers like:

- Public
- Protected
- Private

In general, public members can be accessed from anywhere, protected members can only be accessed from code within the same module and private members can only be accessed from code within the class that these members are defined.

Python doesn't have any inbuilt mechanism to prevent access from any member (i.e. all members are public in Python). However, there is a common convention amongst developers to use a single underscore `self._x` to indicate that a member is protected. Accessing a protected member outside of the module will not cause an error, it is added by developers to inform other developers that they should be careful when accessing this member in such a manner. 

Similarly, we can declare a member as private with two leading underscores `self.__x`. This is more than just a convention in Python because of a mechanism called _name mangling_. Members that are preceded with two underscores have their names modified in the background to `obj._Classname__x`. While they can still be publicly accessed, the purpose of this mechanism is to prevent clashing member names of any inheriting classes that might define a member of the same name.

Note that this is different from the dunder methods we discussed earlier. A dunder method has two leading and two trailing underscores and is treated differently than a private member. One important difference is that dunder method names are not mangled.




OOP Pillar: Encapsulation

In computer programming, _polymorphism_ is the ability to apply an identical operation onto different types of objects. This can be useful when an object type may not be known at the program runtime. Polymorphism can be applied using Python in multiple ways. We have already experienced a form of it when exploring inheritance.
```py
class Animal:
  def __init__(self, name):
    self.name = name

  def make_noise(self):
    print("{} says, Grrrr".format(self.name))

class Cat(Animal):

  def make_noise(self):
    print("{} says, Meow!".format(self.name))

class Robot:
  
  def make_noise(self):
    print("beep.boop...BEEEEP!!!")
``` 
The example above shows an `Animal` class, its subclass `Cat`, and another standalone class `Robot`. Each class has a method `.make_noise()` with different outputs. The identical method name with different behaviors is a form of polymorphism.
```py

an_animal = Animal("Bear")
my_pet = Cat("Maisy")
my_vacuum = Robot()
objects = [an_animal, my_pet, my_vacuum]
for o in objects:
  o.make_noise()

# OUTPUT
# "Bear says, Grrrr"
# "Maisy says, Meow!"
# "beep.boop...BEEEEP!!!"
```
With the classes instantiated and added to a list, we are able to iterate through the list and call `.make_noise()`. This is done without needing to know what type of class `.make_noise()` belongs to. 

OOP Pillar: Polymorphism

When implementing inheritance, a child class may want to change the behavior of a method from its parent class. In Python, all we have to do is _override_ a method definition. An overriding method in a subclass is one that has the same definition as the parent class but contains different behavior. 

```py
class Animal:
  def __init__(self, name):
    self.name = name

  def make_noise(self):
    print("{} says, Grrrr".format(self.name))

pet1 = Animal("Rex")
pet1.make_noise() # Rex says, Grrrr
```
The animal class above has one attribute, `self.name` and one method, `.make_noise()`. The `.make_noise()` method outputs a somewhat generic animal sound, `"Rex says, Grrrr"`. If we define a subclass of `Animal` we may want to make a different sound.
```py
class Cat(Animal):

  def make_noise(self):
    print("{} says, Meow!".format(self.name))

pet2 = Cat("Maisy")
pet2.make_noise() # Maisy says, Meow!
```
Now we've made a class for a more specific type of animal, `Cat`. It has all the attributes and methods of `Animal`. However, if you call the `.make_noise()` method on this instance of `Cat` it will say "Maisy says, Meow!".

Overriding Methods

Congratulations! You've completed the object-oriented programming with Python lesson. Let's do a quick recap of what you learned.

We discussed the four pillars of object-oriented programming as they apply to the Python programming language.


- Inheritance 

Python allows classes to inherit on multiple levels. Meaning a class can inherit from a base class as well as a derived class. Python also supports multiple inheritance, where one class can inherit from any number of other classes. This allows us to describe complex relationships between objects with minimal repeated code.


- Polymorphism

Polymorphism is a concept that allows functions and objects to behave in different ways depending on context. There is the polymorphism of functions like `len()` or the addition operator `+`, which can act differently depending on the provided data. 


- Abstraction

Python supports the concept of abstraction by allowing objects with methods that have the same name, to be called in a general manner. Further, Python provides the Abstract Base Class (ABC) for us to create a more clearly defined interface. 


- Encapsulation

Python's approach to encapsulation is unique compared to most other object-oriented programming languages. In Python, all members of an object are publicly accessible but there are conventions to indicate to developers that a member is intended to be protected or private. 


In this lesson, we learned more complicated relationships between classes. We learned:
* How to create a subclass of an existing class.
* How to redefine existing methods of a parent class in a subclass by overriding them.
* How to leverage a parent class's methods in the body of a subclass method using the `super()` function.
* How to write programs that are flexible using interfaces and polymorphism.
* How to write data types that look and feel like native data types with dunder methods.

These are really complicated concepts! It's a long journey to get to the state of comfortably being able to build class hierarchies that embody the concerns that your software will need to. Give yourself a pat on the back, you earned it!

Review

The code below shows that when working with different object types like, `int`, `str` or `list`, the `+` operator performs different functions. This is known as _operator overloading_ and is another form of polymorphism.
```py
# For an int and an int, + returns an int
2 + 4 == 6

# For a string and a string, + returns a string
"Is this " + "addition?" == "Is this addition?"

# For a list and a list, + returns a list
[1, 2] + [3, 4] == [1, 2, 3, 4]
```  
To implement this behavior, we must first discuss _dunder methods_. Every defined class in Python has access to a group of these special methods. We've explored a few already, the constructor `__init__()` and the string representation method `__repr__()`. The name dunder method is derived from the **D**ouble **UNDER**scores that surround the name of each method.

Recall that the `__repr__()` method takes only one parameter, `self`, and must return a string value. The returned value should be a _string representation_ of the class, which can be seen by using `print()` on an instance of the class. Review the sample code below for an example of how this method is used.

Defining a class's dunder methods is a way to perform operator overloading.
```py
class Animal:
  def __init__(self, name):
    self.name = name

  def __repr__(self):
    return self.name

  def __add__(self, another_animal):
    return Animal(self.name + another_animal.name)

a1 = Animal("Horse")
a2 = Animal("Penguin")
a3 = a1 + a2
print(a1) # Prints "Horse"
print(a2) # Prints "Penguin"
print(a3) # Prints "HorsePenguin"
```
The above code has the class `Animal` with a dunder method, `.__add__()`. This defines the `+` operator behavior when used on objects of this class type. The method returns a new `Animal` object with the names of the operand objects concatenated. In this example, we have created a `"HorsePenguin"`!

The line of code `a3 = a1 + a2` invokes the `.__add__()` method of the left operand, `a1`, with the right operand `a2` passed as an argument. The `name` attributes of `a1` and `a2` are concatenated using the `.__add__()` parameters, `self` and `another_animal`. The resulting string is used as the name of a new `Animal` object which is returned to become the value of `a3`.

Dunder Methods

Another form of multiple inhertance involves a subclass that inherits directly from two classes and can use the attributes and methods of both.

```py
class Animal:
  def __init__(self, name):
    self.name = name

class Dog(Animal):
  def action(self):
    print("{} wags tail. Awwww".format(self.name))

class Wolf(Animal):
  def action(self):
    print("{} bites. OUCH!".format(self.name))

class Hybrid(Dog, Wolf):
  def action(self):
    super().action()
    Wolf.action(self)

my_pet = Hybrid("Fluffy")
my_pet.action() # Fluffy wags tail. Awwww
                # Fluffy bites. OUCH!
```
The above example shows the class `Hybrid` is a subclass of both `Dog` and `Wolf` which are also both subclasses of `Animal`. All 3 subclasses can use the features in `Animal` and `Hybrid` can use the features of `Dog` and `Wolf`. But, `Dog` and `Wolf` can not use each other's features. 

This form of multiple inheritance can be useful by adding functionality from a class that does not fit in with the current design scheme of the current classes.

Care must be taken when creating an inheritance structure like this, especially when using the `super()` method. In the above example, calling `super().action()` inside the `Hybrid` class invokes the `.action()` method of the `Dog` class. This is due to it being listed before `Wolf` in the `Hybrid(Dog, Wolf)` definition.

The line `Wolf.action(self)` calls the `Wolf` class `.action()` method. The important thing to note here is that `self` is passed as an argument. This ensures that the `.action()` method in `Wolf` receives the `Hybrid` class instance to output the correct `name`.



Multiple Inheritance: Part 2

Let's now look at a feature allowed by Python called _multiple inheritance_. As you may have guessed from the name, this is when a subclass inherits from more than one superclass. One form of multiple inheritance is when there are multiple levels of inheritance. This means a class inherits members from its superclass and its super-superclass.

```py
class Animal:
  def __init__(self, name):
    self.name = name
 
  def say_hi(self):
    print("{} says, Hi!".format(self.name))

class Cat(Animal):
  pass

class Angry_Cat(Cat):
  pass

my_pet = Angry_Cat("Mr. Cranky")
my_pet.say_hi() # Mr. Cranky says, Hi!
```
In the above example, `Angry_Cat` inherits from `Cat` and `Cat` inherits from `Animal`. Both `Angry_Cat` and `Cat` have access to the `Animal` class `name` attribute and `.say_hi()` method. Any feature added to `Cat`, `Angry_Cat` will also have access to.

Multiple Inheritance: Part 1

In programming, most languages offer various features that give us different ways to tackle technical problems. With so many different languages out there, with their own unique set of features, it became necessary to create a classification system to help distinguish those sets of features. This ultimately led to the creation of the term _programming paradigm_ - a way to classify different programming languages and the unique features that they offered. 

As we explore Python deeper, our code might fall into multiple paradigm categories at once. This is because most modern-day languages offer more than one specific paradigm we can program in. While we could spend all day exploring all the paradigms Python offers, we will instead dive into one of the most popular paradigms, and one we have already been using (maybe unknowingly), called _Object-Oriented Programming_ (OOP).

At the forefront of any language classified as an OOP language, there must exist the ability to create programs around classes and objects. We have already started working with these concepts earlier as we built our own custom classes, class methods, and instance objects. To recap, let's take a look at an example: 

```py
class Dog:
  sound = "Woof"

  def __init__(self, name, age):
    self.name = name
    self.age = age

  def bark(self):
    print(Dog.sound)
```

In the above example, we are representing a real-world entity (a dog) as a class with properties (name and age) and methods (bark). These features make up the core of the OOP paradigm and ultimately allow us to build more intricate programs. This however only scratches the surface of what we can accomplish. To explore the paradigm further, we will examine the four core pillars of OOP: 

- Inheritance
- Polymorphism
- Abstraction
- Encapsulation

Each of these pillars will allow us to expand our skills so that we can take full advantage of the power of object-oriented programming in Python! We will begin exploring these pillars in later exercises but for now, let's refresh ourselves on OOP fundamentals.  

Introduction to Object-Oriented Programming

When overriding methods we sometimes want to still access the behavior of the parent method. In order to do that we need a way to call the method of the parent class. Python gives us a way to do that using `super()`. 

`super()` gives us a _proxy object_. With this proxy object, we can invoke the method of an object's parent class (also called its superclass). We call the required function as a method on `super()`:

```py
class Animal:
  def __init__(self, name, sound="Grrrr"):
    self.name = name
    self.sound = sound

  def make_noise(self):
    print("{} says, {}".format(self.name, self.sound))

class Cat(Animal):
  def __init__(self, name):
    super().__init__(name, "Meow!") 

pet_cat = Cat("Rachel")
pet_cat.make_noise() # Rachel says, Meow!
```
In the above example, we have the class `Animal` and the subclass `Cat`. `Animal` has 2 attributes, `name` and `sound` and one method, `.make_noise()`. The `.make_noise()` method outputs the `name` and `sound` of an instance. 

The `Cat` subclass has an `.__init__()` method which means the `.__init__()` method of its superclass, `Animal` will not be called when creating an instance of `Cat`. The `.__init__()` method from the subclass is overriding the one from the superclass. 

To still invoke the `.__init__()` method of `Animal`, `super().__init__(name, "Meow!")` is called inside the subclass `.__init__()` method. This additional logic allows us to add the `"Meow"` sound from within the `Cat` class, but still use the `.__init__()` method of the `Animal` class.

`super()` is used in subclasses to invoke a needed behavior from the superclass alongside the behavior of a subclass method. 