In the programming world, duplicate code is considered evil. We should not have multiple copies of the same, or similar, code in different places.
There are many ways to merge pieces of code or objects that have a similar functionality. In this chapter, we'll be covering the most famous object-oriented principle: inheritance. As discussed in Chapter 1, Object-oriented Design, inheritance allows us to create is a relationships between two or more classes, abstracting common logic into superclasses and managing specific details in the subclass. In particular, we'll be covering the Python syntax and principles for:
- Basic inheritance
- Inheriting from built-ins
- Multiple inheritance
- Polymorphism and duck typing
Technically, every class we create uses inheritance. All Python classes are subclasses of the special class named object. This class provides very little in terms of data and behaviors (the behaviors it does provide are all double-underscore methods intended for internal use only), but it does allow Python to treat all objects in the same way.
If we don't explicitly inherit from a different class, our classes will automatically inherit from object. However, we can openly state that our class derives from object using the following syntax:
class MySubClass(object):
passThis is inheritance! This example is, technically, no different from our very first example in Chapter 2, Objects in Python, since Python 3 automatically inherits from object if we don't explicitly provide a different superclass. A superclass, or parent class, is a class that is being inherited from. A subclass is a class that is inheriting from a superclass. In this case, the superclass is object, and MySubClass is the subclass. A subclass is also said to be derived from its parent class or that the subclass extends the parent.
As you've probably figured out from the example, inheritance requires a minimal amount of extra syntax over a basic class definition. Simply include the name of the parent class inside parentheses after the class name but before the colon terminating the class definition. This is all we have to do to tell Python that the new class should be derived from the given superclass.
How do we apply inheritance in practice? The simplest and most obvious use of inheritance is to add functionality to an existing class. Let's start with a simple contact manager that tracks the name and e-mail address of several people. The contact class is responsible for maintaining a list of all contacts in a class variable, and for initializing the name and address for an individual contact:
class Contact:
all_contacts = []
def __init__(self, name, email):
self.name = name
self.email = email
Contact.all_contacts.append(self)
This example introduces us to class variables. The all_contacts list, because it is part of the class definition, is shared by all instances of this class. This means that there is only one Contact.all_contacts list, which we can access as Contact.all_contacts. Less obviously, we can also access it as self.all_contacts on any object instantiated from Contact. If the field can't be found on the object, then it will be found on the class and thus refer to the same single list.
This is a simple class that allows us to track a couple pieces of data about each contact. But what if some of our contacts are also suppliers that we need to order supplies from? We could add an order method to the Contact class, but that would allow people to accidentally order things from contacts who are customers or family friends. Instead, let's create a new Supplier class that acts like our Contact class, but has an additional order method:
class Supplier(Contact):
def order(self, order):
print("If this were a real system we would send "
"'{}' order to '{}'".format(order, self.name))Now, if we test this class in our trusty interpreter, we see that all contacts, including suppliers, accept a name and e-mail address in their __init__, but only suppliers have a functional order method:
>>> c = Contact("Some Body", "[email protected]") >>> s = Supplier("Sup Plier", "[email protected]") >>> print(c.name, c.email, s.name, s.email) Some Body [email protected] Sup Plier [email protected] >>> c.all_contacts [<__main__.Contact object at 0xb7375ecc>, <__main__.Supplier object at 0xb7375f8c>] >>> c.order("I need pliers") Traceback (most recent call last): File "<stdin>", line 1, in <module> AttributeError: 'Contact' object has no attribute 'order' >>> s.order("I need pliers") If this were a real system we would send 'I need pliers' order to 'Sup Plier '
So, now our Supplier class can do everything a contact can do (including adding itself to the list of all_contacts) and all the special things it needs to handle as a supplier. This is the beauty of inheritance.
One interesting use of this kind of inheritance is adding functionality to built-in classes. In the Contact class seen earlier, we are adding contacts to a list of all contacts. What if we also wanted to search that list by name? Well, we could add a method on the Contact class to search it, but it feels like this method actually belongs to the list itself. We can do this using inheritance:
class ContactList(list):
def search(self, name):
'''Return all contacts that contain the search value
in their name.'''
matching_contacts = []
for contact in self:
if name in contact.name:
matching_contacts.append(contact)
return matching_contacts
class Contact:
all_contacts = ContactList()
def __init__(self, name, email):
self.name = name
self.email = email
self.all_contacts.append(self)Instead of instantiating a normal list as our class variable, we create a new ContactList class that extends the built-in list. Then, we instantiate this subclass as our all_contacts list. We can test the new search functionality as follows:
>>> c1 = Contact("John A", "[email protected]") >>> c2 = Contact("John B", "[email protected]") >>> c3 = Contact("Jenna C", "[email protected]") >>> [c.name for c in Contact.all_contacts.search('John')] ['John A', 'John B']
Are you wondering how we changed the built-in syntax [] into something we can inherit from? Creating an empty list with [] is actually a shorthand for creating an empty list using list(); the two syntaxes behave identically:
>>> [] == list() True
In reality, the [] syntax is actually so-called syntax sugar that calls the list() constructor under the hood. The list data type is a class that we can extend. In fact, the list itself extends the object class:
>>> isinstance([], object) True
As a second example, we can extend the dict class, which is, similar to the list, the class that is constructed when using the {} syntax shorthand:
class LongNameDict(dict):
def longest_key(self):
longest = None
for key in self:
if not longest or len(key) > len(longest):
longest = key
return longestThis is easy to test in the interactive interpreter:
>>> longkeys = LongNameDict() >>> longkeys['hello'] = 1 >>> longkeys['longest yet'] = 5 >>> longkeys['hello2'] = 'world' >>> longkeys.longest_key() 'longest yet'
Most built-in types can be similarly extended. Commonly extended built-ins are object, list, set, dict, file, and str. Numerical types such as int and float are also occasionally inherited from.
So, inheritance is great for adding new behavior to existing classes, but what about changing behavior? Our contact class allows only a name and an e-mail address. This may be sufficient for most contacts, but what if we want to add a phone number for our close friends?
As we saw in Chapter 2, Objects in Python, we can do this easily by just setting a phone attribute on the contact after it is constructed. But if we want to make this third variable available on initialization, we have to override __init__. Overriding means altering or replacing a method of the superclass with a new method (with the same name) in the subclass. No special syntax is needed to do this; the subclass's newly created method is automatically called instead of the superclass's method. For example:
class Friend(Contact):
def __init__(self, name, email, phone):
self.name = name
self.email = email
self.phone = phoneAny method can be overridden, not just __init__. Before we go on, however, we need to address some problems in this example. Our Contact and Friend classes have duplicate code to set up the name and email properties; this can make code maintenance complicated as we have to update the code in two or more places. More alarmingly, our Friend class is neglecting to add itself to the all_contacts list we have created on the Contact class.
What we really need is a way to execute the original __init__ method on the Contact class. This is what the super function does; it returns the object as an instance of the parent class, allowing us to call the parent method directly:
class Friend(Contact):
def __init__(self, name, email, phone):
super().__init__(name, email)
self.phone = phoneThis example first gets the instance of the parent object using super, and calls __init__ on that object, passing in the expected arguments. It then does its own initialization, namely, setting the phone attribute.
Note
Note that the super() syntax does not work in older versions of Python. Like the [] and {} syntaxes for lists and dictionaries, it is a shorthand for a more complicated construct. We'll learn more about this shortly when we discuss multiple inheritance, but know for now that in Python 2, you would have to call super(EmailContact, self).__init__(). Specifically notice that the first argument is the name of the child class, not the name as the parent class you want to call, as some might expect. Also, remember the class comes before the object. I always forget the order, so the new syntax in Python 3 has saved me hours of having to look it up.
A super() call can be made inside any method, not just __init__. This means all methods can be modified via overriding and calls to super. The call to super can also be made at any point in the method; we don't have to make the call as the first line in the method. For example, we may need to manipulate or validate incoming parameters before forwarding them to the superclass.