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Applying polymorphism to classes

From: Python 3 Essential Training

Video: Applying polymorphism to classes

Polymorphism is the practice of using one object of one particular class as if it were another object of another class. Let's take a look at how this is done in Python. This is actually something that Python is very good at. We'll make a working copy of classes.py and we'll call it classes-working.py. We'll open that and we see we have our Duck class that has quack and walk in it. And we'll go ahead and create a Dog class and Dog will have bark.

Applying polymorphism to classes

Polymorphism is the practice of using one object of one particular class as if it were another object of another class. Let's take a look at how this is done in Python. This is actually something that Python is very good at. We'll make a working copy of classes.py and we'll call it classes-working.py. We'll open that and we see we have our Duck class that has quack and walk in it. And we'll go ahead and create a Dog class and Dog will have bark.

And fur. The dog has brown and white fur. Now, we'll go ahead and create a dog object called fido = Dog and fido.bark and fido.fur. So now we have a Dog object and a Duck object.

And if we go and run this, we see that the Duck quacks and walks like a duck. And the Dog barks and has brown and white fur. So these are two separate and distinct objects and they have two separate and distinct interfaces. If we want to be able to use them polymorphically, we need to make sure that they have a common interface. And so the Duck class, we can give it a bark and a fur and say bark.

print('The duck cannot bark'). And fur, and this one will say "The duck has feathers." And we can give the Dog, a walk. print('Walks like a dog'). And quack. The dog cannot quack.

Now, we can use them in the same way. For example, let's just get rid of all of this, and we have a Dog and we have a Duck. And if I say for object in and give it a list, donald and fido. I can say, o.quack(), o.walk(), o.bark(), o.fur().

So what we have here is we're calling all four of these functions for both of these objects. We're using them in exactly the same way. We're using them in a way that really does not know or care exactly what type of an object it is. It's simply calling these methods without concern for which type of an object, just assuming that these methods actually exist in there. So we'll save that and we'll run it and we see our result. We have the Duck first. Quaaack! Walks like a duck. The duck cannot bark. The duck has feathers.

And then we have the Dog. And there is all those same methods on the Dog. So if I had say a function, say in_the_ forest, and it expects a dog and it says, dog.bark() and dog.fur(). And let's say I had another one that says in_the_pond and it expects a duck.

And it says duck.quack() and it calls duck.walk(). Now, I can call either of these methods with either of these objects. I can say in_the_forest, and you see the in_the_forest is expecting a dog and I can pass it donald. And as long as donald implements the interface that is being used in this function, it will still work. Save this and run it. It says the duck cannot bark and The duck has feathers.

And likewise, if I call in_the_pond and I pass it fido, save that and run it. The dog cannot quack and it walks like a dog. So this is what polymorphism is. And Python is particularly good at this because the objects in Python don't actually care what the name of the class is. When you use an object, Python is what's called loosely typed or actually they call it duck typing, because the types in Python, everything is an object.

And if it walks like a duck, then you can use it like a duck. That's why they call it duck typing. It's loosely typed. When I declare that this in_the_forest function expects a dog, I'm just naming it dog, but it's an object. I could name it cat and it would still work exactly the same, because that's really just a variable name. That's not a type name at all. That's no kind of a restriction. It's just what I'm calling it here. So I save that and run it. It still works exactly the same.

So a strong advantage of this loosely typed or what they call duck typing is that polymorphism is natural. So I can create a super class that implements all of the things that an animal normally does, and then I can create subclasses from that with just the specific attributes of the type of an animal that I'm interfacing, and then anything that expects any of those interfaces can use it. Because all of those interfaces are guaranteed to be there.

So I know that that's a big concept to wrap your head around, but think of it this way. Any object of any class that implements the interface that's expected by any function can be used by that function. So when I create a function here called in_the_forest and it expects an object that can bark and an object that has fur, or a function that expects an object that can quack and the object that can walk, any object that implements those methods will work in that function regardless of what its type is.

And that is polymorphism and that's how it works in Python.

Show transcript

This video is part of

Image for Python 3 Essential Training
Python 3 Essential Training

87 video lessons · 43110 viewers

Bill Weinman
Author

 
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  1. 5m 14s
    1. Welcome
      1m 32s
    2. Understanding prerequisites for Python
      2m 4s
    3. Using the exercise files
      1m 38s
  2. 33m 29s
    1. Getting started with "Hello World"
      4m 43s
    2. Selecting code with conditionals
      4m 45s
    3. Repeating code with a loop
      4m 13s
    4. Reusing code with a function
      2m 43s
    5. Creating sequences with generator functions
      2m 46s
    6. Reusing code and data with a class
      4m 39s
    7. Greater reusability with inheritance and polymorphism
      7m 17s
    8. Handling errors with exceptions
      2m 23s
  3. 22m 32s
    1. Installing Python 3 and Eclipse for Windows
      11m 24s
    2. Installing Python 3 and Eclipse for Mac
      11m 8s
  4. 28m 0s
    1. Creating a main script
      3m 27s
    2. Understanding whitespace in Python
      4m 8s
    3. Commenting code
      3m 28s
    4. Assigning values
      3m 37s
    5. Selecting code and values with conditionals
      4m 46s
    6. Creating and using functions
      3m 54s
    7. Creating and using objects
      4m 40s
  5. 31m 23s
    1. Understanding variables and objects in Python
      2m 46s
    2. Distinguishing mutable and immutable objects
      2m 41s
    3. Using numbers
      3m 34s
    4. Using strings
      6m 38s
    5. Aggregating values with lists and tuples
      4m 55s
    6. Creating associative lists with dictionaries
      4m 24s
    7. Finding the type and identity of a variable
      4m 45s
    8. Specifying logical values with True and False
      1m 40s
  6. 9m 42s
    1. Selecting code with if and else conditional statements
      2m 22s
    2. Setting multiple choices with elif
      2m 14s
    3. Understanding other strategies for multiple choices
      2m 38s
    4. Using the conditional expression
      2m 28s
  7. 11m 26s
    1. Creating loops with while
      1m 27s
    2. Iterating with for
      3m 54s
    3. Enumerating iterators
      3m 22s
    4. Controlling loop flow with break, continue, and else
      2m 43s
  8. 23m 28s
    1. Performing simple arithmetic
      2m 14s
    2. Operating on bitwise values
      3m 30s
    3. Comparing values
      3m 32s
    4. Operating on Boolean values
      2m 59s
    5. Operating on parts of a container with the slice operator
      6m 52s
    6. Understanding operator precedence
      4m 21s
  9. 11m 34s
    1. Using the re module
      1m 4s
    2. Searching with regular expressions
      3m 12s
    3. Replacing with regular expressions
      3m 29s
    4. Reusing regular expressions with re.compile
      3m 49s
  10. 9m 10s
    1. Learning how exceptions work
      1m 18s
    2. Handling exceptions
      4m 15s
    3. Raising exceptions
      3m 37s
  11. 23m 1s
    1. Defining functions
      6m 23s
    2. Using lists of arguments
      2m 26s
    3. Using named function arguments
      4m 32s
    4. Returning values from functions
      1m 55s
    5. Creating a sequence with a generator function
      7m 45s
  12. 47m 29s
    1. Understanding classes and objects
      5m 12s
    2. Using methods
      6m 12s
    3. Using object data
      10m 4s
    4. Understanding inheritance
      5m 11s
    5. Applying polymorphism to classes
      7m 13s
    6. Using generators
      9m 48s
    7. Using decorators
      3m 49s
  13. 18m 54s
    1. Understanding strings as objects
      3m 25s
    2. Working with common string methods
      5m 24s
    3. Formatting strings with str.format
      5m 31s
    4. Splitting and joining strings
      2m 49s
    5. Finding and using standard string methods
      1m 45s
  14. 25m 27s
    1. Creating sequences with tuples and lists
      4m 6s
    2. Operating on sequences with built-in methods
      5m 50s
    3. Organizing data with dictionaries
      4m 56s
    4. Operating on character data with bytes and byte arrays
      10m 35s
  15. 11m 46s
    1. Opening files
      2m 4s
    2. Reading and writing text files
      4m 33s
    3. Reading and writing binary files
      5m 9s
  16. 21m 27s
    1. Creating a database with SQLite 3
      6m 56s
    2. Creating, retrieving, updating, and deleting records
      7m 31s
    3. Creating a database object
      7m 0s
  17. 18m 27s
    1. Using standard library modules
      8m 0s
    2. Finding third-party modules
      5m 47s
    3. Creating a module
      4m 40s
  18. 23m 11s
    1. Dealing with syntax errors
      8m 19s
    2. Dealing with runtime errors
      4m 0s
    3. Dealing with logical errors
      4m 22s
    4. Using unit tests
      6m 30s
  19. 19m 56s
    1. Normalizing a database interface
      6m 39s
    2. Deconstructing a database application
      8m 9s
    3. Displaying random entries from a database
      5m 8s
  20. 29s
    1. Goodbye
      29s

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