Static and Abstract

Static

Many programming languages include a special keyword static. In essence, a static attribute or method is part of the class in which it is declared instead of part of objects instantiated from that class. If we think about it, the word static means “lacking in change”, and that’s sort of a good way to think about it.

In a UML diagram, static attributes and methods are denoted by underlining them.

Static Attributes

In Python, any attributes declared outside of a method are class attributes, but they can be considered the same as static attributes until they are overwritten by an instance. Here’s an example:

class Stat:
    x = 5     # class or static attribute
  
    def __init__(self, an_y):
        self.y = an_y # instance attribute

In this class, we’ve created a class attribute named x, and a normal attribute named y. Here’s a main() method that will help us explore how the static keyword operates:

from Stat import *

class Main:
  
    def main():
        some_stat = Stat(7)
        another_stat = Stat(8)

        print(some_stat.x)     # 5
        print(some_stat.y)     # 7 
        print(another_stat.x)  # 5
        print(another_stat.y)  # 8

        Stat.x = 25           # change class attribute for all instances

        print(some_stat.x)     # 25
        print(some_stat.y)     # 7 
        print(another_stat.x)  # 25 
        print(another_stat.y)  # 8

        some_stat.x = 10      # overwrites class attribute in instance

        print(some_stat.x)     # 10 (now an instance attribute)
        print(some_stat.y)     # 7 
        print(another_stat.x)  # 25 (still class attribute)
        print(another_stat.y)  # 8

if __name__ == "__main__":
    Main.main()

First, we can see that the attribute x is set to 5 as its default value, so both objects some_stat and another_stat contain that same value. Interestingly, since the attribute x is static, we can access it directly from the class Stat, without even having to instantiate an object. So, we can update the value in that way to 25, and it will take effect in any objects instantiated from Stat.

Below that, we can update the value of x attached to some_stat to 10, and we’ll see that it now creates an instance attribute for that object that contains 10, overwriting the previous class attribute. The value attached to another_stat is unchanged.

Static Methods

Python also allows us to create static methods that work in a similar way:

class Stat:
    x = 5     # class or static attribute
  
    def __init__(self, an_y):
        self.y = an_y # instance attribute
  
    @staticmethod
    def sum(a):
        return Stat.x + a

We have now added a static method sum() to our Stat class. To create a static method, we place the @staticmethod decorator above the method declaration. We haven’t learned about decorators yet, but they allow us to tell Python some important information about the code below the decorator.

In addition, it is important to remember that a static method cannot access any non-static attributes or methods, since it doesn’t have access to an instantiated object in the self parameter.

As a tradeoff, we can call a static method without instantiating the class either, as in this example:

from Stat import *

class Main:
  
    @staticmethod
    def main():
        # other code omitted
        Stat.x = 25
    
        moreStat = Stat(7)
        print(moreStat.sum(5))  # 30
        print(Stat.sum(5))      # 30

if __name__ == "__main__":
    Main.main()

This becomes extremely useful in our main() method. Since we aren’t instantiating our Main class, we can use the decorator @staticmethod above the method to clearly mark that it should be considered a static method.

Abstract

Another major feature of class inheritance is the ability to define a method in a parent class, but not provide any code that implements that function. In effect, we are saying that all objects of that type must include that method, but it is up to the child classes to provide the code. These methods are called abstract methods, and the classes that contain them are abstract classes. Let’s look at how they work!

Abstract in Python

In the UML diagram above, we see that the describe() method in the Vehicle class is printed in italics. That means that the method should be abstract, without any code provided. To do this in Python, we simply inherit from a special class called ABC, short for “Abstract Base Class,” and then use the @abstractmethod decorator:

from abc import ABC, abstractmethod

class Vehicle(ABC):
  
    def __init__(self, name):
        self.__name = name
        self._speed = 1.0
    
    @property
    def name(self):
        return self.__name
    
    def move(self, distance):
        print("Moving");
        return distance / self._speed;
  
    @abstractmethod
    def describe(self):
        pass

Notice that we must first import both the ABC class and the @abstractmethod decorator from a library helpfully called ABC. Then, we can use ABC as the parent class of our class, and update each method using the @abstractmethod decorator before the method, similar to how we’ve already used @staticmethod in an earlier module.

In addition, since we have declared the method describe() to be abstract, we can either add some code to that method that can be called using super().describe() from a child class, or we can simply choose to use the pass keyword to avoid including any code in the method.

Now, any class that inherits from the Vehicle class must provide an implementation for the describe() method. If it does not, that class must also be declared to be abstract. So, for example, in the UML diagram above, we see that the MotorVehicle class does not include an implementation for describe(), so we’ll also have to make it abstract.

Of course, that means that we’ll have to inherit from both Vehicle and ABC. In Python, we can do that by simply including both classes in parentheses after the subclass name, separated by a comma.