python-mastery/Exercises/ex4_2.md

\[ [Index](index.md) | [Exercise 4.1](ex4_1.md) | [Exercise 4.3](ex4_3.md) \]

# Exercise 4.2

*Objectives:*

- Learn more about the behavior of inheritance
- Understand the behavior of super().
- More cooperative inheritance.

*Files Created:* `validate.py`


## (a) The directions of inheritance

Python has two different "directions" of inheritance.  The first is
found in the concept of "single inheritance" where a series
of classes inherit from a single parent. For example, try this example:

```python
>>> class A:
        def spam(self):
            print('A.spam')

>>> class B(A):
        def spam(self):
            print('B.spam')
            super().spam()

>>> class C(B):
        def spam(self):
            print('C.spam')
            super().spam()


>>> C.__mro__
(<class '__main__.C'>, <class '__main__.B'>, <class '__main__.A'>, <class 'object'>)
>>> c = C()
>>> c.spam()
C.spam
B.spam
A.spam
>>> 
```

Observe that the `__mro__` attribute of class `C` encodes all of its ancestors in
order.  When you invoke the `spam()` method, it walks the MRO class-by-class up
the hierarchy.

With multiple inheritance, you get a different kind of inheritance that
allows different classes to be composed together.   Try this example:

```
>>> class Base:
        def spam(self):
            print('Base.spam')

>>> class X(Base):
        def spam(self):
            print('X.spam')
            super().spam()

>>> class Y(Base):
        def spam(self):
            print('Y.spam')
            super().spam()

>>> class Z(Base):
        def spam(self):
            print('Z.spam')
            super().spam()

>>>
```

Notice that all of the classes above inherit from a common parent `Base`.
However, the classes `X`, `Y`, and `Z` are not directly related 
to each other (there is no inheritance chain linking those classes together).

However, watch what happens in multiple inheritance:

```python
>>> class M(X,Y,Z):
        pass

>>> M.__mro__
(<class '__main__.M'>, <class '__main__.X'>, <class '__main__.Y'>, <class '__main__.Z'>, <class '__main__.Base'>, <class 'object'>)
>>> m = M()
>>> m.spam()
X.spam
Y.spam
Z.spam
Base.spam
>>>
```

Here, you see all of the classes stack together in the order supplied by the subclass.
Suppose the subclass rearranges the class order:

```python
>>> class N(Z,Y,X):
        pass

>>> N.__mro__
(<class '__main__.N'>, <class '__main__.Z'>, <class '__main__.Y'>, <class '__main__.X'>, <class '__main__.Base'>, <class 'object'>)
>>> n = N()
>>> n.spam()
Z.spam
Y.spam
X.spam
Base.spam
>>>
```

Here, you see the order of the parents flip around.  Carefully pay attention to what `super()`
is doing in both cases.  It doesn't delegate to the immediate parent of each class--instead,
it moves to the next class on the MRO.   Not only that, the exact order is controlled
by the child. This is pretty weird.

Also notice that the common parent `Base` serves to terminate the chain of `super()` operations.
Specifically, the `Base.spam()` method does not call any further methods. It also appears at
the end of the MRO since it is the parent to all of the classes being composed together.

## (b) Build a Value Checker

In [Exercise 3.4](ex3_4.md), you added some properties to the `Stock` class that
checked attributes for different types and values (e.g., shares had to be a positive
integer).  Let's play with that idea a bit.  Start by creating a file `validate.py` and
defining the following base class:

```python
# validate.py
class Validator:
    @classmethod
    def check(cls, value):
        return value
```

Now, let's make some classes for type checking:

```python
class Typed(Validator):
    expected_type = object
    @classmethod
    def check(cls, value):
        if not isinstance(value, cls.expected_type):
            raise TypeError(f'Expected {cls.expected_type}')
        return super().check(value)

class Integer(Typed):
    expected_type = int

class Float(Typed):
    expected_type = float

class String(Typed):
    expected_type = str
```

Here's how you use these classes (Note: the use of `@classmethod` allows us to 
avoid the extra step of creating instances which we don't really need):

```python
>>> Integer.check(10)
10
>>> Integer.check('10')
Traceback (most recent call last):
  File "<stdin>", line 1, in check
    raise TypeError(f'Expected {cls.expected_type}')
TypeError: Expected <class 'int'>
>>> String.check('10')
'10'
>>>
```

You could use the validators in a function. For example:

```python
>>> def add(x, y):
        Integer.check(x)
        Integer.check(y)
        return x + y

>>> add(2, 2)
4
>>> add('2', '3')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "<stdin>", line 2, in add
  File "validate.py", line 11, in check
    raise TypeError(f'Expected {cls.expected_type}')
TypeError: Expected <class 'int'>
>>>
```

Now, make some more classes for different kinds of domain checking:

```python
class Positive(Validator):
    @classmethod
    def check(cls, value):
        if value < 0:
            raise ValueError('Expected >= 0')
        return super().check(value)

class NonEmpty(Validator):
    @classmethod
    def check(cls, value):
        if len(value) == 0:
            raise ValueError('Must be non-empty')
        return super().check(value)
```

Where is all of this going?   Let's start composing classes together with multiple inheritance like toy blocks:

```python
class PositiveInteger(Integer, Positive):
    pass

class PositiveFloat(Float, Positive):
    pass

class NonEmptyString(String, NonEmpty):
    pass
```

Essentially, you're taking existing validators and composing them
together into new ones. Madness!  However, let's use them to validate
some things now:

```python
>>> PositiveInteger.check(10)
10
>>> PositiveInteger.check('10')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
    raise TypeError(f'Expected {cls.expected_type}')
TypeError: Expected <class 'int'>
>>> PositiveInteger.check(-10)
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
    raise ValueError('Expected >= 0')
ValueError: Must be >= 0


>>> NonEmptyString.check('hello')
'hello'
>>> NonEmptyString.check('')
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
    raise ValueError('Must be non-empty')
ValueError: Must be non-empty
>>>
```

At this point, your head is probably fully exploded.  However, the problem of composing
different bits of code together is one that arises in real-world programs. Cooperative
multiple inheritance is one of the tools that can be used to organize it. 

## (c) Using your validators

Your validators can be used to add value checking to functions and classes.  For
example, perhaps the validators could be used in the properties of `Stock`:

```python
class Stock:
    ...
    @property
    def shares(self):
        return self._shares

    @shares.setter
    def shares(self, value):
        self._shares = PositiveInteger.check(value)
    ...
```

Copy the `Stock` class from `stock.py` change it to use the validators in the property
code for `shares` and `price`.

\[ [Solution](soln4_2.md) | [Index](index.md) | [Exercise 4.1](ex4_1.md) | [Exercise 4.3](ex4_3.md) \]

----
`>>>` Advanced Python Mastery  
`...` A course by [dabeaz](https://www.dabeaz.com)  
`...` Copyright 2007-2023  

![](https://i.creativecommons.org/l/by-sa/4.0/88x31.png). This work is licensed under a [Creative Commons Attribution-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-sa/4.0/)