We can use multiple inheritance to add this new class as a parent of our existing Friend class. The tricky part is that we now have two parent __init__ methods both of which need to be initialized. And they need to be initialized with different arguments. How do we do this? Well, we could start with a naive approach:

class Friend(Contact, AddressHolder):
    def __init__(
        self, name, email, phone,street, city, state, code):
        Contact.__init__(self, name, email)
        AddressHolder.__init__(self, street, city, state, code)
        self.phone = phone

In this example, we directly call the __init__ function on each of the superclasses and explicitly pass the self argument. This example technically works; we can access the different variables directly on the class. But there are a few problems.

First, it is possible for a superclass to go uninitialized if we neglect to explicitly call the initializer. That wouldn't break this example, but it could cause hard-to-debug program crashes in common scenarios. Imagine trying to insert data into a database that has not been connected to, for example.

Second, and more sinister, is the possibility of a superclass being called multiple times because of the organization of the class hierarchy. Look at this inheritance diagram:

The __init__ method from the Friend class first calls __init__ on Contact, which implicitly initializes the object superclass (remember, all classes derive from object). Friend then calls __init__ on AddressHolder, which implicitly initializes the object superclass again. This means the parent class has been set up twice. With the object class, that's relatively harmless, but in some situations, it could spell disaster. Imagine trying to connect to a database twice for every request!

The base class should only be called once. Once, yes, but when? Do we call Friend, then Contact, then Object, then AddressHolder? Or Friend, then Contact, then AddressHolder, then Object?

Let's look at a second contrived example that illustrates this problem more clearly. Here we have a base class that has a method named call_me. Two subclasses override that method, and then another subclass extends both of these using multiple inheritance. This is called diamond inheritance because of the diamond shape of the class diagram:

Let's convert this diagram to code; this example shows when the methods are called:

class BaseClass:
    num_base_calls = 0
    def call_me(self):
        print("Calling method on Base Class")
        self.num_base_calls += 1

class LeftSubclass(BaseClass):
    num_left_calls = 0
    def call_me(self):
        BaseClass.call_me(self)
        print("Calling method on Left Subclass")
        self.num_left_calls += 1

class RightSubclass(BaseClass):
    num_right_calls = 0
    def call_me(self):
        BaseClass.call_me(self)
        print("Calling method on Right Subclass")
        self.num_right_calls += 1

class Subclass(LeftSubclass, RightSubclass):
    num_sub_calls = 0
    def call_me(self):
        LeftSubclass.call_me(self)
        RightSubclass.call_me(self)
        print("Calling method on Subclass")
        self.num_sub_calls += 1

This example simply ensures that each overridden call_me method directly calls the parent method with the same name. It lets us know each time a method is called by printing the information to the screen. It also updates a static variable on the class to show how many times it has been called. If we instantiate one Subclass object and call the method on it once, we get this output:

>>> s = Subclass()
>>> s.call_me()
Calling method on Base Class
Calling method on Left Subclass
Calling method on Base Class
Calling method on Right Subclass
Calling method on Subclass
>>> print(
... s.num_sub_calls,
... s.num_left_calls,
... s.num_right_calls,
... s.num_base_calls)
1 1 1 2

Thus we can clearly see the base class's call_me method being called twice. This could lead to some insidious bugs if that method is doing actual work—like depositing into a bank account—twice.

The thing to keep in mind with multiple inheritance is that we only want to call the "next" method in the class hierarchy, not the "parent" method. In fact, that next method may not be on a parent or ancestor of the current class. The super keyword comes to our rescue once again. Indeed, super was originally developed to make complicated forms of multiple inheritance possible. Here is the same code written using super:

class BaseClass:
    num_base_calls = 0
    def call_me(self):
        print("Calling method on Base Class")
        self.num_base_calls += 1

class LeftSubclass(BaseClass):
    num_left_calls = 0
    def call_me(self):
        super().call_me()
        print("Calling method on Left Subclass")
        self.num_left_calls += 1

class RightSubclass(BaseClass):
    num_right_calls = 0
    def call_me(self):
        super().call_me()
        print("Calling method on Right Subclass")
        self.num_right_calls += 1

class Subclass(LeftSubclass, RightSubclass):
    num_sub_calls = 0
    def call_me(self):
        super().call_me()
        print("Calling method on Subclass")
        self.num_sub_calls += 1

The change is pretty minor; we simply replaced the naive direct calls with calls to super(), although the bottom subclass only calls super once rather than having to make the calls for both the left and right. The change is simple enough, but look at the difference when we execute it:

>>> s = Subclass()
>>> s.call_me()
Calling method on Base Class
Calling method on Right Subclass
Calling method on Left Subclass
Calling method on Subclass
>>> print(s.num_sub_calls, s.num_left_calls, s.num_right_calls,
s.num_base_calls)
1 1 1 1

Looks good, our base method is only being called once. But what is super() actually doing here? Since the print statements are executed after the super calls, the printed output is in the order each method is actually executed. Let's look at the output from back to front to see who is calling what.

First, call_me of Subclass calls super().call_me(), which happens to refer to LeftSubclass.call_me(). The LeftSubclass.call_me() method then calls super().call_me(), but in this case, super() is referring to RightSubclass.call_me().

Pay particular attention to this: the super call is not calling the method on the superclass of LeftSubclass (which is BaseClass). Rather, it is calling RightSubclass, even though it is not a direct parent of LeftSubclass! This is the next method, not the parent method. RightSubclass then calls BaseClass and the super calls have ensured each method in the class hierarchy is executed once.

This is going to make things complicated as we return to our Friend multiple inheritance example. In the __init__ method for Friend, we were originally calling __init__ for both parent classes, with different sets of arguments:

Contact.__init__(self, name, email)
AddressHolder.__init__(self, street, city, state, code)

How can we manage different sets of arguments when using super? We don't necessarily know which class super is going to try to initialize first. Even if we did, we need a way to pass the "extra" arguments so that subsequent calls to super, on other subclasses, receive the right arguments.

Specifically, if the first call to super passes the name and email arguments to Contact.__init__, and Contact.__init__ then calls super, it needs to be able to pass the address-related arguments to the "next" method, which is AddressHolder.__init__.

This is a problem whenever we want to call superclass methods with the same name, but with different sets of arguments. Most often, the only time you would want to call a superclass with a completely different set of arguments is in __init__, as we're doing here. Even with regular methods, though, we may want to add optional parameters that only make sense to one subclass or set of subclasses.

Sadly, the only way to solve this problem is to plan for it from the beginning. We have to design our base class parameter lists to accept keyword arguments for any parameters that are not required by every subclass implementation. Finally, we must ensure the method freely accepts unexpected arguments and passes them on to its super call, in case they are necessary to later methods in the inheritance order.

Python's function parameter syntax provides all the tools we need to do this, but it makes the overall code look cumbersome. Have a look at the proper version of the Friend multiple inheritance code:

class Contact:
    all_contacts = []
   
    def __init__(self, name='', email='', **kwargs):
        super().__init__(**kwargs)
        self.name = name
        self.email = email
        self.all_contacts.append(self)

class AddressHolder:
    def __init__(self, street='', city='', state='', code='',
            **kwargs):
        super().__init__(**kwargs)
        self.street = street
        self.city = city
        self.state = state
        self.code = code

class Friend(Contact, AddressHolder):
    def __init__(self, phone='', **kwargs):
        super().__init__(**kwargs)
        self.phone = phone

We've changed all arguments to keyword arguments by giving them an empty string as a default value. We've also ensured that a **kwargs parameter is included to capture any additional parameters that our particular method doesn't know what to do with. It passes these parameters up to the next class with the super call.

The previous example does what it is supposed to do. But it's starting to look messy, and it has become difficult to answer the question, What arguments do we need to pass into Friend.__init__? This is the foremost question for anyone planning to use the class, so a docstring should be added to the method to explain what is happening.

Further, even this implementation is insufficient if we want to reuse variables in parent classes. When we pass the **kwargs variable to super, the dictionary does not include any of the variables that were included as explicit keyword arguments. For example, in Friend.__init__, the call to super does not have phone in the kwargs dictionary. If any of the other classes need the phone parameter, we need to ensure it is in the dictionary that is passed. Worse, if we forget to do this, it will be tough to debug because the superclass will not complain, but will simply assign the default value (in this case, an empty string) to the variable.

There are a few ways to ensure that the variable is passed upwards. Assume the Contact class does, for some reason, need to be initialized with a phone parameter, and the Friend class will also need access to it. We can do any of the following:

We have covered many of the caveats involved with multiple inheritance in Python. When we need to account for all the possible situations, we have to plan for them and our code will get messy. Basic multiple inheritance can be handy but, in many cases, we may want to choose a more transparent way of combining two disparate classes, usually using composition or one of the design patterns we'll be covering in Chapter 10, Python Design Patterns I and Chapter 11, Python Design Patterns II.