# A Primer on Python Metaclasses

## Python, Classes, and Objects¶

Most readers are aware that Python is an object-oriented language. By object-oriented, we mean that Python can define classes, which bundle data and functionality into one entity. For example, we may create a class IntContainer which stores an integer and allows certain operations to be performed:

In [1]:
class IntContainer(object):
def __init__(self, i):
self.i = int(i)

def add_one(self):
self.i += 1

In [2]:
ic = IntContainer(2)
ic.add_one()
print(ic.i)

3



This is a bit of a silly example, but shows the fundamental nature of classes: their ability to bundle data and operations into a single object, which leads to cleaner, more manageable, and more adaptable code. Additionally, classes can inherit properties from parents and add or specialize attributes and methods. This object-oriented approach to programming can be very intuitive and powerful.

What many do not realize, though, is that quite literally everything in the Python language is an object.

For example, integers are simply instances of the built-in int type:

In [3]:
print type(1)

<type 'int'>



To emphasize that the int type really is an object, let's derive from it and specialize the __add__ method (which is the machinery underneath the + operator):

(Note: We'll used the super syntax to call methods from the parent class: if you're unfamiliar with this, take a look at this StackOverflow question).

In [4]:
class MyInt(int):
def __add__(self, other):
print "specializing addition"
return super(MyInt, self).__add__(other)

i = MyInt(2)
print(i + 2)

specializing addition
4



Using the + operator on our derived type goes through our __add__ method, as expected. We see that int really is an object that can be subclassed and extended just like user-defined classes. The same is true of floats, lists, tuples, and everything else in the Python language. They're all objects.

## Down the Rabbit Hole: Classes as Objects¶

We said above that everything in python is an object: it turns out that this is true of classes themselves. Let's look at an example.

We'll start by defining a class that does nothing

In [5]:
class DoNothing(object):
pass


If we instantiate this, we can use the type operator to see the type of object that it is:

In [6]:
d = DoNothing()
type(d)

Out[6]:
__main__.DoNothing


We see that our variable d is an instance of the class __main__.DoNothing.

We can do this similarly for built-in types:

In [7]:
L = [1, 2, 3]
type(L)

Out[7]:
list


A list is, as you may expect, an object of type list.

But let's take this a step further: what is the type of DoNothing itself?

In [8]:
type(DoNothing)

Out[8]:
type


The type of DoNothing is type. This tells us that the class DoNothing is itself an object, and that object is of type type.

It turns out that this is the same for built-in datatypes:

In [9]:
type(tuple), type(list), type(int), type(float)

Out[9]:
(type, type, type, type)


What this shows is that in Python, classes are objects, and they are objects of type type. type is a metaclass: a class which instantiates classes. All new-style classes in Python are instances of the type metaclass, including type itself:

In [10]:
type(type)

Out[10]:
type


Yes, you read that correctly: the type of type is type. In other words, type is an instance of itself. This sort of circularity cannot (to my knowledge) be duplicated in pure Python, and the behavior is created through a bit of a hack at the implementation level of Python.

## Metaprogramming: Creating Classes on the Fly¶

Now that we've stepped back and considered the fact that classes in Python are simply objects like everything else, we can think about what is known as metaprogramming. You're probably used to creating functions which return objects. We can think of these functions as an object factory: they take some arguments, create an object, and return it. Here is a simple example of a function which creates an int object:

In [11]:
def int_factory(s):
i = int(s)
return i

i = int_factory('100')
print(i)

100



This is overly-simplistic, but any function you write in the course of a normal program can be boiled down to this: take some arguments, do some operations, and create & return an object. With the above discussion in mind, though, there's nothing to stop us from creating an object of type type (that is, a class), and returning that instead -- this is a metafunction:

In [12]:
def class_factory():
class Foo(object):
pass
return Foo

F = class_factory()
f = F()
print(type(f))

<class '__main__.Foo'>



Just as the function int_factory constructs an returns an instance of int, the function class_factory constructs and returns an instance of type: that is, a class.

But the above construction is a bit awkward: especially if we were going to do some more complicated logic when constructing Foo, it would be nice to avoid all the nested indentations and define the class in a more dynamic way. We can accomplish this by instantiating Foo from type directly:

In [13]:
def class_factory():
return type('Foo', (), {})

F = class_factory()
f = F()
print(type(f))

<class '__main__.Foo'>



In fact, the construct

In [14]:
class MyClass(object):
pass


is identical to the construct

In [15]:
MyClass = type('MyClass', (), {})


MyClass is an instance of type type, and that can be seen explicitly in the second version of the definition. A potential confusion arises from the more common use of type as a function to determine the type of an object, but you should strive to separate these two uses of the keyword in your mind: here type is a class (more precisely, a metaclass), and MyClass is an instance of type.

The arguments to the type constructor are:

type(name, bases, dct)
• name is a string giving the name of the class to be constructed
• bases is a tuple giving the parent classes of the class to be constructed
• dct is a dictionary of the attributes and methods of the class to be constructed

So, for example, the following two pieces of code have identical results:

In [16]:
class Foo(object):
i = 4

class Bar(Foo):
def get_i(self):
return self.i

b = Bar()
print(b.get_i())

4


In [17]:
Foo = type('Foo', (), dict(i=4))

Bar = type('Bar', (Foo,), dict(get_i = lambda self: self.i))

b = Bar()
print(b.get_i())

4



This perhaps seems a bit over-complicated in the case of this contrived example, but it can be very powerful as a means of dynamically creating new classes on-the-fly.

## Making Things Interesting: Custom Metaclasses¶

Now things get really fun. Just as we can inherit from and extend a class we've created, we can also inherit from and extend the type metaclass, and create custom behavior in our metaclass.

### Example 1: Modifying Attributes¶

Let's use a simple example where we want to create an API in which the user can create a set of interfaces which contain a file object. Each interface should have a unique string ID, and contain an open file object. The user could then write specialized methods to accomplish certain tasks. There are certainly good ways to do this without delving into metaclasses, but such a simple example will (hopefully) elucidate what's going on.

First we'll create our interface meta class, deriving from type:

In [18]:
class InterfaceMeta(type):
def __new__(cls, name, parents, dct):
# create a class_id if it's not specified
if 'class_id' not in dct:
dct['class_id'] = name.lower()

# open the specified file for writing
if 'file' in dct:
filename = dct['file']
dct['file'] = open(filename, 'w')

# we need to call type.__new__ to complete the initialization
return super(InterfaceMeta, cls).__new__(cls, name, parents, dct)


Notice that we've modified the input dictionary (the attributes and methods of the class) to add a class id if it's not present, and to replace the filename with a file object pointing to that file name.

Now we'll use our InterfaceMeta class to construct and instantiate an Interface object:

In [19]:
Interface = InterfaceMeta('Interface', (), dict(file='tmp.txt'))

print(Interface.class_id)
print(Interface.file)

interface
<open file 'tmp.txt', mode 'w' at 0x21b8810>



This behaves as we'd expect: the class_id class variable is created, and the file class variable is replaced with an open file object. Still, the creation of the Interface class using InterfaceMeta directly is a bit clunky and difficult to read. This is where __metaclass__ comes in and steals the show. We can accomplish the same thing by defining Interface this way:

In [20]:
class Interface(object):
__metaclass__ = InterfaceMeta
file = 'tmp.txt'

print(Interface.class_id)
print(Interface.file)

interface
<open file 'tmp.txt', mode 'w' at 0x21b8ae0>



by defining the __metaclass__ attribute of the class, we've told the class that it should be constructed using InterfaceMeta rather than using type. To make this more definite, observe that the type of Interface is now InterfaceMeta:

In [21]:
type(Interface)

Out[21]:
__main__.InterfaceMeta


Furthermore, any class derived from Interface will now be constructed using the same metaclass:

In [22]:
class UserInterface(Interface):
file = 'foo.txt'

print(UserInterface.file)
print(UserInterface.class_id)

<open file 'foo.txt', mode 'w' at 0x21b8c00>
userinterface



This simple example shows how metaclasses can be used to create powerful and flexible APIs for projects. For example, the Django project makes use of these sorts of constructions to allow concise declarations of very powerful extensions to their basic classes.

### Example 2: Registering Subclasses¶

Another possible use of a metaclass is to automatically register all subclasses derived from a given base class. For example, you may have a basic interface to a database and wish for the user to be able to define their own interfaces, which are automatically stored in a master registry.

You might proceed this way:

In [23]:
class DBInterfaceMeta(type):
# we use __init__ rather than __new__ here because we want
# to modify attributes of the class *after* they have been
# created
def __init__(cls, name, bases, dct):
if not hasattr(cls, 'registry'):
# this is the base class.  Create an empty registry
cls.registry = {}
else:
# this is a derived class.  Add cls to the registry
interface_id = name.lower()
cls.registry[interface_id] = cls

super(DBInterfaceMeta, cls).__init__(name, bases, dct)


Our metaclass simply adds a registry dictionary if it's not already present, and adds the new class to the registry if the registry is already there. Let's see how this works:

In [24]:
class DBInterface(object):
__metaclass__ = DBInterfaceMeta

print(DBInterface.registry)

{}



Now let's create some subclasses, and double-check that they're added to the registry:

In [25]:
class FirstInterface(DBInterface):
pass

class SecondInterface(DBInterface):
pass

class SecondInterfaceModified(SecondInterface):
pass

print(DBInterface.registry)

{'firstinterface': <class '__main__.FirstInterface'>, 'secondinterface': <class '__main__.SecondInterface'>, 'secondinterfacemodified': <class '__main__.SecondInterfaceModified'>}



It works as expected! This could be used in conjunction with a function that chooses implementations from the registry, and any user-defined Interface-derived objects would be automatically accounted for, without the user having to remember to manually register the new types.

## Conclusion: When Should You Use Metaclasses?¶

I've gone through some examples of what metaclasses are, and some ideas about how they might be used to create very powerful and flexible APIs. Although metaclasses are in the background of everything you do in Python, the average coder rarely has to think about them.

But the question remains: when should you think about using custom metaclasses in your project? It's a complicated question, but there's a quotation floating around the web that addresses it quite succinctly:

Metaclasses are deeper magic than 99% of users should ever worry about. If you wonder whether you need them, you don’t (the people who actually need them know with certainty that they need them, and don’t need an explanation about why).

– Tim Peters

In a way, this is a very unsatisfying answer: it's a bit reminiscent of the wistful and cliched explanation of the border between attraction and love: "well, you just... know!"

But I think Tim is right: in general, I've found that most tasks in Python that can be accomplished through use of custom metaclasses can also be accomplished more cleanly and with more clarity by other means. As programmers, we should always be careful to avoid being clever for the sake of cleverness alone, though it is admittedly an ever-present temptation.

I personally spent six years doing science with Python, writing code nearly on a daily basis, before I found a problem for which metaclasses were the natural solution. And it turns out Tim was right:

I just knew.

This post was written entirely in an IPython Notebook: the notebook file is available for download here. For more information on blogging with notebooks in octopress, see my previous post on the subject.