Why can `__subclasshook__` be monkeypatched onto the metaclass but `__instancecheck__` cannot?

Why can `__subclasshook__` be monkeypatched onto the metaclass but `__instancecheck__` cannot? - python

Here is a toy example of trying to create a decorator that allows declaration of attribute names which should be required parts of "interface checking" along the standard __subclasshook__ and __instancecheck__ patterns.
It seems to work as expected when I decorate the Foo class. I make a Bar class, unrelated to Foo, but which has the needed attributes, and it correctly satisfies isinstance(instance_of_bar, Foo) == True.
But then as another example, I make a subclass of dict augmented so that the key values will be accessible with getattr syntax as well (e.g. a dict where d['a'] can be replaced with d.a to get the same result). In this case, the attributes are just instance attributes, so __instancecheck__ should work.
Here is the code. Note that given that the example with the instance of Bar works, the choice to "monkeypatch" the __subclasshook__ function into the Foo class (that has a metaclass) works fine. So it does not seem that one must define the function directly in the class definition of the metaclass.
#Using Python 2.7.3
import abc
def interface(*attributes):
def decorator(Base):
def checker(Other):
return all(hasattr(Other, a) for a in attributes)
def __subclasshook__(cls, Other):
if checker(Other):
return True
return NotImplemented
def __instancecheck__(cls, Other):
return checker(Other)
Base.__subclasshook__ = classmethod(__subclasshook__)
Base.__instancecheck__ = classmethod(__instancecheck__)
return Base
return decorator
#interface("x", "y")
class Foo(object):
__metaclass__ = abc.ABCMeta
def x(self): return 5
def y(self): return 10
class Bar(object):
def x(self): return "blah"
def y(self): return "blah"
class Baz(object):
def __init__(self):
self.x = "blah"
self.y = "blah"
class attrdict(dict):
def __getattr__(self, attr):
return self[attr]
f = Foo()
b = Bar()
z = Baz()
t = attrdict({"x":27.5, "y":37.5})
print isinstance(f, Foo)
print isinstance(b, Foo)
print isinstance(z, Foo)
print isinstance(t, Foo)
Prints:
True
True
False
False
This is just a toy example -- I'm not looking for better ways to implement my attrdict class. The Bar example demonstrates the monkeypatched __subclasshook__ working. The other two examples demonstrate failure of __instancecheck__ for instances that have mere instance attributes to check on. In those cases, __instancecheck__ is not even called.
I can manually check that the condition from my __instancecheck__ function is satisfied by the instance of attrdict (that is, hasattr(instance_of_attrdict, "x") is True as needed) or z.
Again, it seems to work fine for the instance of Bar. This suggests that __subclasshook__ is being correctly applied by the decorator, and that patching a custom __metaclass__ is not the issue. But __instancecheck__ does not seem to be called in the process.
Why can __subclasshook__ be defined outside of the class definition of the metaclass and added later, but not __instancecheck__?

Everything works as is should. If you are using __metaclass__, you are overwriting the class creation process. It looks like your monkeypatch is working for __subclasshook__ but it's only called from the __subclasshook__ function of the ABCMeta. You can check it with this:
>>> type(Foo)
<class 'abc.ABCMeta'>
To be more explicit: the __subclasshook__ case works by accident in this example, because the metaclass's __subclasscheck__ happens to defer to the class's __subclasshook__ in some situations. The __instancecheck__ protocol of the metaclass does not ever defer to the class's definition of __instancecheck__, which is why the monkeypatched version of __subclasshook__ does eventually get called, but the monkeypatched version of __instancecheck__ does not get called.
In more details: If you are creating a class with the metaclass, the type of the class will be the metaclass. In this case the ABCMeta. And the definition of the isinstance() say the following: 'isinstance(object, class-or-type-or-tuple) -> bool', which means the instance checking will be executed on the given class, type or tuple of classes/types. In this case the isinstance check will be done on the ABCMeta (ABCMeta.__instancecheck__() will be called). Because the monkeypatch was applied to the Foo class and not the ABCMeta, the __instancecheck__ method of Foo will never run. But the __instancecheck__ of the ABCMethod will call the __subclasscheck__ method of itself, and this second method will try the validaion by executing the __subclasshook__ method of created class (in this example Foo).
In this case you can get the desired behavior if you overwrite the functions of the metaclass like this:
def interface(*attributes):
def decorator(Base):
def checker(Other):
return all(hasattr(Other, a) for a in attributes)
def __subclasshook__(cls, Other):
if checker(Other):
return True
return NotImplemented
def __instancecheck__(cls, Other):
return checker(Other)
Base.__metaclass__.__subclasshook__ = classmethod(__subclasshook__)
Base.__metaclass__.__instancecheck__ = classmethod(__instancecheck__)
return Base
return decorator
And the output with the updated code:
True
True
True
True
Another approach would be to define your own class to serve as the metaclass, and create the kind of __instancecheck__ protocol that you're looking for, so that it does defer to the class's definition of __instancecheck__ when the metaclass's definition hits some failure criteria. Then, set __metaclass__ to be that class inside of Foo and your existing decorator should work as-is.
More info: A good post about metaclasses

Related

Code explanation for example (metaclasses) [duplicate]

What are metaclasses? What are they used for?

Classes as objects
Before understanding metaclasses, it helps to understand Python classes more deeply. Python has a very peculiar idea of what classes are, which it borrows from the Smalltalk language.
In most languages, classes are just pieces of code that describe how to produce an object. That is somewhat true in Python too:
>>> class ObjectCreator(object):
... pass
>>> my_object = ObjectCreator()
>>> print(my_object)
<__main__.ObjectCreator object at 0x8974f2c>
But classes are more than that in Python. Classes are objects too.
Yes, objects.
When a Python script runs, every line of code is executed from top to bottom. When the Python interpreter encounters the class keyword, Python creates an object out of the "description" of the class that follows. Thus, the following instruction
>>> class ObjectCreator(object):
... pass
...creates an object with the name ObjectCreator!
This object (the class) is itself capable of creating objects (called instances).
But still, it's an object. Therefore, like all objects:
you can assign it to a variable1
JustAnotherVariable = ObjectCreator
you can attach attributes to it
ObjectCreator.class_attribute = 'foo'
you can pass it as a function parameter
print(ObjectCreator)
1 Note that merely assigning it to another variable doesn't change the class's __name__, i.e.,
>>> print(JustAnotherVariable)
<class '__main__.ObjectCreator'>
>>> print(JustAnotherVariable())
<__main__.ObjectCreator object at 0x8997b4c>
Creating classes dynamically
Since classes are objects, you can create them on the fly, like any object.
First, you can create a class in a function using class:
>>> def choose_class(name):
... if name == 'foo':
... class Foo(object):
... pass
... return Foo # return the class, not an instance
... else:
... class Bar(object):
... pass
... return Bar
...
>>> MyClass = choose_class('foo')
>>> print(MyClass) # the function returns a class, not an instance
<class '__main__.Foo'>
>>> print(MyClass()) # you can create an object from this class
<__main__.Foo object at 0x89c6d4c>
But it's not so dynamic, since you still have to write the whole class yourself.
Since classes are objects, they must be generated by something.
When you use the class keyword, Python creates this object automatically. But as
with most things in Python, it gives you a way to do it manually.
Remember the function type? The good old function that lets you know what
type an object is:
>>> print(type(1))
<type 'int'>
>>> print(type("1"))
<type 'str'>
>>> print(type(ObjectCreator))
<type 'type'>
>>> print(type(ObjectCreator()))
<class '__main__.ObjectCreator'>
Well, type has also a completely different ability: it can create classes on the fly. type can take the description of a class as parameters,
and return a class.
(I know, it's silly that the same function can have two completely different uses according to the parameters you pass to it. It's an issue due to backward
compatibility in Python)
type works this way:
type(name, bases, attrs)
Where:
name: name of the class
bases: tuple of the parent class (for inheritance, can be empty)
attrs: dictionary containing attributes names and values
e.g.:
>>> class MyShinyClass(object):
... pass
can be created manually this way:
>>> MyShinyClass = type('MyShinyClass', (), {}) # returns a class object
>>> print(MyShinyClass)
<class '__main__.MyShinyClass'>
>>> print(MyShinyClass()) # create an instance with the class
<__main__.MyShinyClass object at 0x8997cec>
You'll notice that we use MyShinyClass as the name of the class
and as the variable to hold the class reference. They can be different,
but there is no reason to complicate things.
type accepts a dictionary to define the attributes of the class. So:
>>> class Foo(object):
... bar = True
Can be translated to:
>>> Foo = type('Foo', (), {'bar':True})
And used as a normal class:
>>> print(Foo)
<class '__main__.Foo'>
>>> print(Foo.bar)
True
>>> f = Foo()
>>> print(f)
<__main__.Foo object at 0x8a9b84c>
>>> print(f.bar)
True
And of course, you can inherit from it, so:
>>> class FooChild(Foo):
... pass
would be:
>>> FooChild = type('FooChild', (Foo,), {})
>>> print(FooChild)
<class '__main__.FooChild'>
>>> print(FooChild.bar) # bar is inherited from Foo
True
Eventually, you'll want to add methods to your class. Just define a function
with the proper signature and assign it as an attribute.
>>> def echo_bar(self):
... print(self.bar)
...
>>> FooChild = type('FooChild', (Foo,), {'echo_bar': echo_bar})
>>> hasattr(Foo, 'echo_bar')
False
>>> hasattr(FooChild, 'echo_bar')
True
>>> my_foo = FooChild()
>>> my_foo.echo_bar()
True
And you can add even more methods after you dynamically create the class, just like adding methods to a normally created class object.
>>> def echo_bar_more(self):
... print('yet another method')
...
>>> FooChild.echo_bar_more = echo_bar_more
>>> hasattr(FooChild, 'echo_bar_more')
True
You see where we are going: in Python, classes are objects, and you can create a class on the fly, dynamically.
This is what Python does when you use the keyword class, and it does so by using a metaclass.
What are metaclasses (finally)
Metaclasses are the 'stuff' that creates classes.
You define classes in order to create objects, right?
But we learned that Python classes are objects.
Well, metaclasses are what create these objects. They are the classes' classes,
you can picture them this way:
MyClass = MetaClass()
my_object = MyClass()
You've seen that type lets you do something like this:
MyClass = type('MyClass', (), {})
It's because the function type is in fact a metaclass. type is the
metaclass Python uses to create all classes behind the scenes.
Now you wonder "why the heck is it written in lowercase, and not Type?"
Well, I guess it's a matter of consistency with str, the class that creates
strings objects, and int the class that creates integer objects. type is
just the class that creates class objects.
You see that by checking the __class__ attribute.
Everything, and I mean everything, is an object in Python. That includes integers,
strings, functions and classes. All of them are objects. And all of them have
been created from a class:
>>> age = 35
>>> age.__class__
<type 'int'>
>>> name = 'bob'
>>> name.__class__
<type 'str'>
>>> def foo(): pass
>>> foo.__class__
<type 'function'>
>>> class Bar(object): pass
>>> b = Bar()
>>> b.__class__
<class '__main__.Bar'>
Now, what is the __class__ of any __class__ ?
>>> age.__class__.__class__
<type 'type'>
>>> name.__class__.__class__
<type 'type'>
>>> foo.__class__.__class__
<type 'type'>
>>> b.__class__.__class__
<type 'type'>
So, a metaclass is just the stuff that creates class objects.
You can call it a 'class factory' if you wish.
type is the built-in metaclass Python uses, but of course, you can create your
own metaclass.
The __metaclass__ attribute
In Python 2, you can add a __metaclass__ attribute when you write a class (see next section for the Python 3 syntax):
class Foo(object):
__metaclass__ = something...
[...]
If you do so, Python will use the metaclass to create the class Foo.
Careful, it's tricky.
You write class Foo(object) first, but the class object Foo is not created
in memory yet.
Python will look for __metaclass__ in the class definition. If it finds it,
it will use it to create the object class Foo. If it doesn't, it will use
type to create the class.
Read that several times.
When you do:
class Foo(Bar):
pass
Python does the following:
Is there a __metaclass__ attribute in Foo?
If yes, create in-memory a class object (I said a class object, stay with me here), with the name Foo by using what is in __metaclass__.
If Python can't find __metaclass__, it will look for a __metaclass__ at the MODULE level, and try to do the same (but only for classes that don't inherit anything, basically old-style classes).
Then if it can't find any __metaclass__ at all, it will use the Bar's (the first parent) own metaclass (which might be the default type) to create the class object.
Be careful here that the __metaclass__ attribute will not be inherited, the metaclass of the parent (Bar.__class__) will be. If Bar used a __metaclass__ attribute that created Bar with type() (and not type.__new__()), the subclasses will not inherit that behavior.
Now the big question is, what can you put in __metaclass__?
The answer is something that can create a class.
And what can create a class? type, or anything that subclasses or uses it.
Metaclasses in Python 3
The syntax to set the metaclass has been changed in Python 3:
class Foo(object, metaclass=something):
...
i.e. the __metaclass__ attribute is no longer used, in favor of a keyword argument in the list of base classes.
The behavior of metaclasses however stays largely the same.
One thing added to metaclasses in Python 3 is that you can also pass attributes as keyword-arguments into a metaclass, like so:
class Foo(object, metaclass=something, kwarg1=value1, kwarg2=value2):
...
Read the section below for how Python handles this.
Custom metaclasses
The main purpose of a metaclass is to change the class automatically,
when it's created.
You usually do this for APIs, where you want to create classes matching the
current context.
Imagine a stupid example, where you decide that all classes in your module
should have their attributes written in uppercase. There are several ways to
do this, but one way is to set __metaclass__ at the module level.
This way, all classes of this module will be created using this metaclass,
and we just have to tell the metaclass to turn all attributes to uppercase.
Luckily, __metaclass__ can actually be any callable, it doesn't need to be a
formal class (I know, something with 'class' in its name doesn't need to be
a class, go figure... but it's helpful).
So we will start with a simple example, by using a function.
# the metaclass will automatically get passed the same argument
# that you usually pass to `type`
def upper_attr(future_class_name, future_class_parents, future_class_attrs):
"""
Return a class object, with the list of its attribute turned
into uppercase.
"""
# pick up any attribute that doesn't start with '__' and uppercase it
uppercase_attrs = {
attr if attr.startswith("__") else attr.upper(): v
for attr, v in future_class_attrs.items()
}
# let `type` do the class creation
return type(future_class_name, future_class_parents, uppercase_attrs)
__metaclass__ = upper_attr # this will affect all classes in the module
class Foo(): # global __metaclass__ won't work with "object" though
# but we can define __metaclass__ here instead to affect only this class
# and this will work with "object" children
bar = 'bip'
Let's check:
>>> hasattr(Foo, 'bar')
False
>>> hasattr(Foo, 'BAR')
True
>>> Foo.BAR
'bip'
Now, let's do exactly the same, but using a real class for a metaclass:
# remember that `type` is actually a class like `str` and `int`
# so you can inherit from it
class UpperAttrMetaclass(type):
# __new__ is the method called before __init__
# it's the method that creates the object and returns it
# while __init__ just initializes the object passed as parameter
# you rarely use __new__, except when you want to control how the object
# is created.
# here the created object is the class, and we want to customize it
# so we override __new__
# you can do some stuff in __init__ too if you wish
# some advanced use involves overriding __call__ as well, but we won't
# see this
def __new__(upperattr_metaclass, future_class_name,
future_class_parents, future_class_attrs):
uppercase_attrs = {
attr if attr.startswith("__") else attr.upper(): v
for attr, v in future_class_attrs.items()
}
return type(future_class_name, future_class_parents, uppercase_attrs)
Let's rewrite the above, but with shorter and more realistic variable names now that we know what they mean:
class UpperAttrMetaclass(type):
def __new__(cls, clsname, bases, attrs):
uppercase_attrs = {
attr if attr.startswith("__") else attr.upper(): v
for attr, v in attrs.items()
}
return type(clsname, bases, uppercase_attrs)
You may have noticed the extra argument cls. There is
nothing special about it: __new__ always receives the class it's defined in, as the first parameter. Just like you have self for ordinary methods which receive the instance as the first parameter, or the defining class for class methods.
But this is not proper OOP. We are calling type directly and we aren't overriding or calling the parent's __new__. Let's do that instead:
class UpperAttrMetaclass(type):
def __new__(cls, clsname, bases, attrs):
uppercase_attrs = {
attr if attr.startswith("__") else attr.upper(): v
for attr, v in attrs.items()
}
return type.__new__(cls, clsname, bases, uppercase_attrs)
We can make it even cleaner by using super, which will ease inheritance (because yes, you can have metaclasses, inheriting from metaclasses, inheriting from type):
class UpperAttrMetaclass(type):
def __new__(cls, clsname, bases, attrs):
uppercase_attrs = {
attr if attr.startswith("__") else attr.upper(): v
for attr, v in attrs.items()
}
# Python 2 requires passing arguments to super:
return super(UpperAttrMetaclass, cls).__new__(
cls, clsname, bases, uppercase_attrs)
# Python 3 can use no-arg super() which infers them:
return super().__new__(cls, clsname, bases, uppercase_attrs)
Oh, and in Python 3 if you do this call with keyword arguments, like this:
class Foo(object, metaclass=MyMetaclass, kwarg1=value1):
...
It translates to this in the metaclass to use it:
class MyMetaclass(type):
def __new__(cls, clsname, bases, dct, kwargs1=default):
...
That's it. There is really nothing more about metaclasses.
The reason behind the complexity of the code using metaclasses is not because
of metaclasses, it's because you usually use metaclasses to do twisted stuff
relying on introspection, manipulating inheritance, vars such as __dict__, etc.
Indeed, metaclasses are especially useful to do black magic, and therefore
complicated stuff. But by themselves, they are simple:
intercept a class creation
modify the class
return the modified class
Why would you use metaclasses classes instead of functions?
Since __metaclass__ can accept any callable, why would you use a class
since it's obviously more complicated?
There are several reasons to do so:
The intention is clear. When you read UpperAttrMetaclass(type), you know
what's going to follow
You can use OOP. Metaclass can inherit from metaclass, override parent methods. Metaclasses can even use metaclasses.
Subclasses of a class will be instances of its metaclass if you specified a metaclass-class, but not with a metaclass-function.
You can structure your code better. You never use metaclasses for something as trivial as the above example. It's usually for something complicated. Having the ability to make several methods and group them in one class is very useful to make the code easier to read.
You can hook on __new__, __init__ and __call__. Which will allow you to do different stuff, Even if usually you can do it all in __new__,
some people are just more comfortable using __init__.
These are called metaclasses, damn it! It must mean something!
Why would you use metaclasses?
Now the big question. Why would you use some obscure error-prone feature?
Well, usually you don't:
Metaclasses are deeper magic that
99% of users should never worry about it.
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).
Python Guru Tim Peters
The main use case for a metaclass is creating an API. A typical example of this is the Django ORM. It allows you to define something like this:
class Person(models.Model):
name = models.CharField(max_length=30)
age = models.IntegerField()
But if you do this:
person = Person(name='bob', age='35')
print(person.age)
It won't return an IntegerField object. It will return an int, and can even take it directly from the database.
This is possible because models.Model defines __metaclass__ and
it uses some magic that will turn the Person you just defined with simple statements
into a complex hook to a database field.
Django makes something complex look simple by exposing a simple API
and using metaclasses, recreating code from this API to do the real job
behind the scenes.
The last word
First, you know that classes are objects that can create instances.
Well, in fact, classes are themselves instances. Of metaclasses.
>>> class Foo(object): pass
>>> id(Foo)
142630324
Everything is an object in Python, and they are all either instance of classes
or instances of metaclasses.
Except for type.
type is actually its own metaclass. This is not something you could
reproduce in pure Python, and is done by cheating a little bit at the implementation
level.
Secondly, metaclasses are complicated. You may not want to use them for
very simple class alterations. You can change classes by using two different techniques:
monkey patching
class decorators
99% of the time you need class alteration, you are better off using these.
But 98% of the time, you don't need class alteration at all.

A metaclass is the class of a class. A class defines how an instance of the class (i.e. an object) behaves while a metaclass defines how a class behaves. A class is an instance of a metaclass.
While in Python you can use arbitrary callables for metaclasses (like Jerub shows), the better approach is to make it an actual class itself. type is the usual metaclass in Python. type is itself a class, and it is its own type. You won't be able to recreate something like type purely in Python, but Python cheats a little. To create your own metaclass in Python you really just want to subclass type.
A metaclass is most commonly used as a class-factory. When you create an object by calling the class, Python creates a new class (when it executes the 'class' statement) by calling the metaclass. Combined with the normal __init__ and __new__ methods, metaclasses therefore allow you to do 'extra things' when creating a class, like registering the new class with some registry or replace the class with something else entirely.
When the class statement is executed, Python first executes the body of the class statement as a normal block of code. The resulting namespace (a dict) holds the attributes of the class-to-be. The metaclass is determined by looking at the baseclasses of the class-to-be (metaclasses are inherited), at the __metaclass__ attribute of the class-to-be (if any) or the __metaclass__ global variable. The metaclass is then called with the name, bases and attributes of the class to instantiate it.
However, metaclasses actually define the type of a class, not just a factory for it, so you can do much more with them. You can, for instance, define normal methods on the metaclass. These metaclass-methods are like classmethods in that they can be called on the class without an instance, but they are also not like classmethods in that they cannot be called on an instance of the class. type.__subclasses__() is an example of a method on the type metaclass. You can also define the normal 'magic' methods, like __add__, __iter__ and __getattr__, to implement or change how the class behaves.
Here's an aggregated example of the bits and pieces:
def make_hook(f):
"""Decorator to turn 'foo' method into '__foo__'"""
f.is_hook = 1
return f
class MyType(type):
def __new__(mcls, name, bases, attrs):
if name.startswith('None'):
return None
# Go over attributes and see if they should be renamed.
newattrs = {}
for attrname, attrvalue in attrs.iteritems():
if getattr(attrvalue, 'is_hook', 0):
newattrs['__%s__' % attrname] = attrvalue
else:
newattrs[attrname] = attrvalue
return super(MyType, mcls).__new__(mcls, name, bases, newattrs)
def __init__(self, name, bases, attrs):
super(MyType, self).__init__(name, bases, attrs)
# classregistry.register(self, self.interfaces)
print "Would register class %s now." % self
def __add__(self, other):
class AutoClass(self, other):
pass
return AutoClass
# Alternatively, to autogenerate the classname as well as the class:
# return type(self.__name__ + other.__name__, (self, other), {})
def unregister(self):
# classregistry.unregister(self)
print "Would unregister class %s now." % self
class MyObject:
__metaclass__ = MyType
class NoneSample(MyObject):
pass
# Will print "NoneType None"
print type(NoneSample), repr(NoneSample)
class Example(MyObject):
def __init__(self, value):
self.value = value
#make_hook
def add(self, other):
return self.__class__(self.value + other.value)
# Will unregister the class
Example.unregister()
inst = Example(10)
# Will fail with an AttributeError
#inst.unregister()
print inst + inst
class Sibling(MyObject):
pass
ExampleSibling = Example + Sibling
# ExampleSibling is now a subclass of both Example and Sibling (with no
# content of its own) although it will believe it's called 'AutoClass'
print ExampleSibling
print ExampleSibling.__mro__

Note, this answer is for Python 2.x as it was written in 2008, metaclasses are slightly different in 3.x.
Metaclasses are the secret sauce that make 'class' work. The default metaclass for a new style object is called 'type'.
class type(object)
| type(object) -> the object's type
| type(name, bases, dict) -> a new type
Metaclasses take 3 args. 'name', 'bases' and 'dict'
Here is where the secret starts. Look for where name, bases and the dict come from in this example class definition.
class ThisIsTheName(Bases, Are, Here):
All_the_code_here
def doesIs(create, a):
dict
Lets define a metaclass that will demonstrate how 'class:' calls it.
def test_metaclass(name, bases, dict):
print 'The Class Name is', name
print 'The Class Bases are', bases
print 'The dict has', len(dict), 'elems, the keys are', dict.keys()
return "yellow"
class TestName(object, None, int, 1):
__metaclass__ = test_metaclass
foo = 1
def baz(self, arr):
pass
print 'TestName = ', repr(TestName)
# output =>
The Class Name is TestName
The Class Bases are (<type 'object'>, None, <type 'int'>, 1)
The dict has 4 elems, the keys are ['baz', '__module__', 'foo', '__metaclass__']
TestName = 'yellow'
And now, an example that actually means something, this will automatically make the variables in the list "attributes" set on the class, and set to None.
def init_attributes(name, bases, dict):
if 'attributes' in dict:
for attr in dict['attributes']:
dict[attr] = None
return type(name, bases, dict)
class Initialised(object):
__metaclass__ = init_attributes
attributes = ['foo', 'bar', 'baz']
print 'foo =>', Initialised.foo
# output=>
foo => None
Note that the magic behaviour that Initialised gains by having the metaclass init_attributes is not passed onto a subclass of Initialised.
Here is an even more concrete example, showing how you can subclass 'type' to make a metaclass that performs an action when the class is created. This is quite tricky:
class MetaSingleton(type):
instance = None
def __call__(cls, *args, **kw):
if cls.instance is None:
cls.instance = super(MetaSingleton, cls).__call__(*args, **kw)
return cls.instance
class Foo(object):
__metaclass__ = MetaSingleton
a = Foo()
b = Foo()
assert a is b

Others have explained how metaclasses work and how they fit into the Python type system. Here's an example of what they can be used for. In a testing framework I wrote, I wanted to keep track of the order in which classes were defined, so that I could later instantiate them in this order. I found it easiest to do this using a metaclass.
class MyMeta(type):
counter = 0
def __init__(cls, name, bases, dic):
type.__init__(cls, name, bases, dic)
cls._order = MyMeta.counter
MyMeta.counter += 1
class MyType(object): # Python 2
__metaclass__ = MyMeta
class MyType(metaclass=MyMeta): # Python 3
pass
Anything that's a subclass of MyType then gets a class attribute _order that records the order in which the classes were defined.

One use for metaclasses is adding new properties and methods to an instance automatically.
For example, if you look at Django models, their definition looks a bit confusing. It looks as if you are only defining class properties:
class Person(models.Model):
first_name = models.CharField(max_length=30)
last_name = models.CharField(max_length=30)
However, at runtime the Person objects are filled with all sorts of useful methods. See the source for some amazing metaclassery.

I think the ONLamp introduction to metaclass programming is well written and gives a really good introduction to the topic despite being several years old already.
http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html (archived at https://web.archive.org/web/20080206005253/http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html)
In short: A class is a blueprint for the creation of an instance, a metaclass is a blueprint for the creation of a class. It can be easily seen that in Python classes need to be first-class objects too to enable this behavior.
I've never written one myself, but I think one of the nicest uses of metaclasses can be seen in the Django framework. The model classes use a metaclass approach to enable a declarative style of writing new models or form classes. While the metaclass is creating the class, all members get the possibility to customize the class itself.
Creating a new model
The metaclass enabling this
The thing that's left to say is: If you don't know what metaclasses are, the probability that you will not need them is 99%.

What are metaclasses? What do you use them for?
TLDR: A metaclass instantiates and defines behavior for a class just like a class instantiates and defines behavior for an instance.
Pseudocode:
>>> Class(...)
instance
The above should look familiar. Well, where does Class come from? It's an instance of a metaclass (also pseudocode):
>>> Metaclass(...)
Class
In real code, we can pass the default metaclass, type, everything we need to instantiate a class and we get a class:
>>> type('Foo', (object,), {}) # requires a name, bases, and a namespace
<class '__main__.Foo'>
Putting it differently
A class is to an instance as a metaclass is to a class.
When we instantiate an object, we get an instance:
>>> object() # instantiation of class
<object object at 0x7f9069b4e0b0> # instance
Likewise, when we define a class explicitly with the default metaclass, type, we instantiate it:
>>> type('Object', (object,), {}) # instantiation of metaclass
<class '__main__.Object'> # instance
Put another way, a class is an instance of a metaclass:
>>> isinstance(object, type)
True
Put a third way, a metaclass is a class's class.
>>> type(object) == type
True
>>> object.__class__
<class 'type'>
When you write a class definition and Python executes it, it uses a metaclass to instantiate the class object (which will, in turn, be used to instantiate instances of that class).
Just as we can use class definitions to change how custom object instances behave, we can use a metaclass class definition to change the way a class object behaves.
What can they be used for? From the docs:
The potential uses for metaclasses are boundless. Some ideas that have been explored include logging, interface checking, automatic delegation, automatic property creation, proxies, frameworks, and automatic resource locking/synchronization.
Nevertheless, it is usually encouraged for users to avoid using metaclasses unless absolutely necessary.
You use a metaclass every time you create a class:
When you write a class definition, for example, like this,
class Foo(object):
'demo'
You instantiate a class object.
>>> Foo
<class '__main__.Foo'>
>>> isinstance(Foo, type), isinstance(Foo, object)
(True, True)
It is the same as functionally calling type with the appropriate arguments and assigning the result to a variable of that name:
name = 'Foo'
bases = (object,)
namespace = {'__doc__': 'demo'}
Foo = type(name, bases, namespace)
Note, some things automatically get added to the __dict__, i.e., the namespace:
>>> Foo.__dict__
dict_proxy({'__dict__': <attribute '__dict__' of 'Foo' objects>,
'__module__': '__main__', '__weakref__': <attribute '__weakref__'
of 'Foo' objects>, '__doc__': 'demo'})
The metaclass of the object we created, in both cases, is type.
(A side-note on the contents of the class __dict__: __module__ is there because classes must know where they are defined, and __dict__ and __weakref__ are there because we don't define __slots__ - if we define __slots__ we'll save a bit of space in the instances, as we can disallow __dict__ and __weakref__ by excluding them. For example:
>>> Baz = type('Bar', (object,), {'__doc__': 'demo', '__slots__': ()})
>>> Baz.__dict__
mappingproxy({'__doc__': 'demo', '__slots__': (), '__module__': '__main__'})
... but I digress.)
We can extend type just like any other class definition:
Here's the default __repr__ of classes:
>>> Foo
<class '__main__.Foo'>
One of the most valuable things we can do by default in writing a Python object is to provide it with a good __repr__. When we call help(repr) we learn that there's a good test for a __repr__ that also requires a test for equality - obj == eval(repr(obj)). The following simple implementation of __repr__ and __eq__ for class instances of our type class provides us with a demonstration that may improve on the default __repr__ of classes:
class Type(type):
def __repr__(cls):
"""
>>> Baz
Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
>>> eval(repr(Baz))
Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
"""
metaname = type(cls).__name__
name = cls.__name__
parents = ', '.join(b.__name__ for b in cls.__bases__)
if parents:
parents += ','
namespace = ', '.join(': '.join(
(repr(k), repr(v) if not isinstance(v, type) else v.__name__))
for k, v in cls.__dict__.items())
return '{0}(\'{1}\', ({2}), {{{3}}})'.format(metaname, name, parents, namespace)
def __eq__(cls, other):
"""
>>> Baz == eval(repr(Baz))
True
"""
return (cls.__name__, cls.__bases__, cls.__dict__) == (
other.__name__, other.__bases__, other.__dict__)
So now when we create an object with this metaclass, the __repr__ echoed on the command line provides a much less ugly sight than the default:
>>> class Bar(object): pass
>>> Baz = Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
>>> Baz
Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
With a nice __repr__ defined for the class instance, we have a stronger ability to debug our code. However, much further checking with eval(repr(Class)) is unlikely (as functions would be rather impossible to eval from their default __repr__'s).
An expected usage: __prepare__ a namespace
If, for example, we want to know in what order a class's methods are created in, we could provide an ordered dict as the namespace of the class. We would do this with __prepare__ which returns the namespace dict for the class if it is implemented in Python 3:
from collections import OrderedDict
class OrderedType(Type):
#classmethod
def __prepare__(metacls, name, bases, **kwargs):
return OrderedDict()
def __new__(cls, name, bases, namespace, **kwargs):
result = Type.__new__(cls, name, bases, dict(namespace))
result.members = tuple(namespace)
return result
And usage:
class OrderedMethodsObject(object, metaclass=OrderedType):
def method1(self): pass
def method2(self): pass
def method3(self): pass
def method4(self): pass
And now we have a record of the order in which these methods (and other class attributes) were created:
>>> OrderedMethodsObject.members
('__module__', '__qualname__', 'method1', 'method2', 'method3', 'method4')
Note, this example was adapted from the documentation - the new enum in the standard library does this.
So what we did was instantiate a metaclass by creating a class. We can also treat the metaclass as we would any other class. It has a method resolution order:
>>> inspect.getmro(OrderedType)
(<class '__main__.OrderedType'>, <class '__main__.Type'>, <class 'type'>, <class 'object'>)
And it has approximately the correct repr (which we can no longer eval unless we can find a way to represent our functions.):
>>> OrderedMethodsObject
OrderedType('OrderedMethodsObject', (object,), {'method1': <function OrderedMethodsObject.method1 at 0x0000000002DB01E0>, 'members': ('__module__', '__qualname__', 'method1', 'method2', 'method3', 'method4'), 'method3': <function OrderedMet
hodsObject.method3 at 0x0000000002DB02F0>, 'method2': <function OrderedMethodsObject.method2 at 0x0000000002DB0268>, '__module__': '__main__', '__weakref__': <attribute '__weakref__' of 'OrderedMethodsObject' objects>, '__doc__': None, '__d
ict__': <attribute '__dict__' of 'OrderedMethodsObject' objects>, 'method4': <function OrderedMethodsObject.method4 at 0x0000000002DB0378>})

Python 3 update
There are (at this point) two key methods in a metaclass:
__prepare__, and
__new__
__prepare__ lets you supply a custom mapping (such as an OrderedDict) to be used as the namespace while the class is being created. You must return an instance of whatever namespace you choose. If you don't implement __prepare__ a normal dict is used.
__new__ is responsible for the actual creation/modification of the final class.
A bare-bones, do-nothing-extra metaclass would like:
class Meta(type):
def __prepare__(metaclass, cls, bases):
return dict()
def __new__(metacls, cls, bases, clsdict):
return super().__new__(metacls, cls, bases, clsdict)
A simple example:
Say you want some simple validation code to run on your attributes -- like it must always be an int or a str. Without a metaclass, your class would look something like:
class Person:
weight = ValidateType('weight', int)
age = ValidateType('age', int)
name = ValidateType('name', str)
As you can see, you have to repeat the name of the attribute twice. This makes typos possible along with irritating bugs.
A simple metaclass can address that problem:
class Person(metaclass=Validator):
weight = ValidateType(int)
age = ValidateType(int)
name = ValidateType(str)
This is what the metaclass would look like (not using __prepare__ since it is not needed):
class Validator(type):
def __new__(metacls, cls, bases, clsdict):
# search clsdict looking for ValidateType descriptors
for name, attr in clsdict.items():
if isinstance(attr, ValidateType):
attr.name = name
attr.attr = '_' + name
# create final class and return it
return super().__new__(metacls, cls, bases, clsdict)
A sample run of:
p = Person()
p.weight = 9
print(p.weight)
p.weight = '9'
produces:
9
Traceback (most recent call last):
File "simple_meta.py", line 36, in <module>
p.weight = '9'
File "simple_meta.py", line 24, in __set__
(self.name, self.type, value))
TypeError: weight must be of type(s) <class 'int'> (got '9')
Note: This example is simple enough it could have also been accomplished with a class decorator, but presumably an actual metaclass would be doing much more.
The 'ValidateType' class for reference:
class ValidateType:
def __init__(self, type):
self.name = None # will be set by metaclass
self.attr = None # will be set by metaclass
self.type = type
def __get__(self, inst, cls):
if inst is None:
return self
else:
return inst.__dict__[self.attr]
def __set__(self, inst, value):
if not isinstance(value, self.type):
raise TypeError('%s must be of type(s) %s (got %r)' %
(self.name, self.type, value))
else:
inst.__dict__[self.attr] = value

Role of a metaclass' __call__() method when creating a class instance
If you've done Python programming for more than a few months you'll eventually stumble upon code that looks like this:
# define a class
class SomeClass(object):
# ...
# some definition here ...
# ...
# create an instance of it
instance = SomeClass()
# then call the object as if it's a function
result = instance('foo', 'bar')
The latter is possible when you implement the __call__() magic method on the class.
class SomeClass(object):
# ...
# some definition here ...
# ...
def __call__(self, foo, bar):
return bar + foo
The __call__() method is invoked when an instance of a class is used as a callable. But as we've seen from previous answers a class itself is an instance of a metaclass, so when we use the class as a callable (i.e. when we create an instance of it) we're actually calling its metaclass' __call__() method. At this point most Python programmers are a bit confused because they've been told that when creating an instance like this instance = SomeClass() you're calling its __init__() method. Some who've dug a bit deeper know that before __init__() there's __new__(). Well, today another layer of truth is being revealed, before __new__() there's the metaclass' __call__().
Let's study the method call chain from specifically the perspective of creating an instance of a class.
This is a metaclass that logs exactly the moment before an instance is created and the moment it's about to return it.
class Meta_1(type):
def __call__(cls):
print "Meta_1.__call__() before creating an instance of ", cls
instance = super(Meta_1, cls).__call__()
print "Meta_1.__call__() about to return instance."
return instance
This is a class that uses that metaclass
class Class_1(object):
__metaclass__ = Meta_1
def __new__(cls):
print "Class_1.__new__() before creating an instance."
instance = super(Class_1, cls).__new__(cls)
print "Class_1.__new__() about to return instance."
return instance
def __init__(self):
print "entering Class_1.__init__() for instance initialization."
super(Class_1,self).__init__()
print "exiting Class_1.__init__()."
And now let's create an instance of Class_1
instance = Class_1()
# Meta_1.__call__() before creating an instance of <class '__main__.Class_1'>.
# Class_1.__new__() before creating an instance.
# Class_1.__new__() about to return instance.
# entering Class_1.__init__() for instance initialization.
# exiting Class_1.__init__().
# Meta_1.__call__() about to return instance.
Observe that the code above doesn't actually do anything more than logging the tasks. Each method delegates the actual work to its parent's implementation, thus keeping the default behavior. Since type is Meta_1's parent class (type being the default parent metaclass) and considering the ordering sequence of the output above, we now have a clue as to what would be the pseudo implementation of type.__call__():
class type:
def __call__(cls, *args, **kwarg):
# ... maybe a few things done to cls here
# then we call __new__() on the class to create an instance
instance = cls.__new__(cls, *args, **kwargs)
# ... maybe a few things done to the instance here
# then we initialize the instance with its __init__() method
instance.__init__(*args, **kwargs)
# ... maybe a few more things done to instance here
# then we return it
return instance
We can see that the metaclass' __call__() method is the one that's called first. It then delegates creation of the instance to the class's __new__() method and initialization to the instance's __init__(). It's also the one that ultimately returns the instance.
From the above it stems that the metaclass' __call__() is also given the opportunity to decide whether or not a call to Class_1.__new__() or Class_1.__init__() will eventually be made. Over the course of its execution it could actually return an object that hasn't been touched by either of these methods. Take for example this approach to the singleton pattern:
class Meta_2(type):
singletons = {}
def __call__(cls, *args, **kwargs):
if cls in Meta_2.singletons:
# we return the only instance and skip a call to __new__()
# and __init__()
print ("{} singleton returning from Meta_2.__call__(), "
"skipping creation of new instance.".format(cls))
return Meta_2.singletons[cls]
# else if the singleton isn't present we proceed as usual
print "Meta_2.__call__() before creating an instance."
instance = super(Meta_2, cls).__call__(*args, **kwargs)
Meta_2.singletons[cls] = instance
print "Meta_2.__call__() returning new instance."
return instance
class Class_2(object):
__metaclass__ = Meta_2
def __new__(cls, *args, **kwargs):
print "Class_2.__new__() before creating instance."
instance = super(Class_2, cls).__new__(cls)
print "Class_2.__new__() returning instance."
return instance
def __init__(self, *args, **kwargs):
print "entering Class_2.__init__() for initialization."
super(Class_2, self).__init__()
print "exiting Class_2.__init__()."
Let's observe what happens when repeatedly trying to create an object of type Class_2
a = Class_2()
# Meta_2.__call__() before creating an instance.
# Class_2.__new__() before creating instance.
# Class_2.__new__() returning instance.
# entering Class_2.__init__() for initialization.
# exiting Class_2.__init__().
# Meta_2.__call__() returning new instance.
b = Class_2()
# <class '__main__.Class_2'> singleton returning from Meta_2.__call__(), skipping creation of new instance.
c = Class_2()
# <class '__main__.Class_2'> singleton returning from Meta_2.__call__(), skipping creation of new instance.
a is b is c # True

A metaclass is a class that tells how (some) other class should be created.
This is a case where I saw metaclass as a solution to my problem:
I had a really complicated problem, that probably could have been solved differently, but I chose to solve it using a metaclass. Because of the complexity, it is one of the few modules I have written where the comments in the module surpass the amount of code that has been written. Here it is...
#!/usr/bin/env python
# Copyright (C) 2013-2014 Craig Phillips. All rights reserved.
# This requires some explaining. The point of this metaclass excercise is to
# create a static abstract class that is in one way or another, dormant until
# queried. I experimented with creating a singlton on import, but that did
# not quite behave how I wanted it to. See now here, we are creating a class
# called GsyncOptions, that on import, will do nothing except state that its
# class creator is GsyncOptionsType. This means, docopt doesn't parse any
# of the help document, nor does it start processing command line options.
# So importing this module becomes really efficient. The complicated bit
# comes from requiring the GsyncOptions class to be static. By that, I mean
# any property on it, may or may not exist, since they are not statically
# defined; so I can't simply just define the class with a whole bunch of
# properties that are #property #staticmethods.
#
# So here's how it works:
#
# Executing 'from libgsync.options import GsyncOptions' does nothing more
# than load up this module, define the Type and the Class and import them
# into the callers namespace. Simple.
#
# Invoking 'GsyncOptions.debug' for the first time, or any other property
# causes the __metaclass__ __getattr__ method to be called, since the class
# is not instantiated as a class instance yet. The __getattr__ method on
# the type then initialises the class (GsyncOptions) via the __initialiseClass
# method. This is the first and only time the class will actually have its
# dictionary statically populated. The docopt module is invoked to parse the
# usage document and generate command line options from it. These are then
# paired with their defaults and what's in sys.argv. After all that, we
# setup some dynamic properties that could not be defined by their name in
# the usage, before everything is then transplanted onto the actual class
# object (or static class GsyncOptions).
#
# Another piece of magic, is to allow command line options to be set in
# in their native form and be translated into argparse style properties.
#
# Finally, the GsyncListOptions class is actually where the options are
# stored. This only acts as a mechanism for storing options as lists, to
# allow aggregation of duplicate options or options that can be specified
# multiple times. The __getattr__ call hides this by default, returning the
# last item in a property's list. However, if the entire list is required,
# calling the 'list()' method on the GsyncOptions class, returns a reference
# to the GsyncListOptions class, which contains all of the same properties
# but as lists and without the duplication of having them as both lists and
# static singlton values.
#
# So this actually means that GsyncOptions is actually a static proxy class...
#
# ...And all this is neatly hidden within a closure for safe keeping.
def GetGsyncOptionsType():
class GsyncListOptions(object):
__initialised = False
class GsyncOptionsType(type):
def __initialiseClass(cls):
if GsyncListOptions._GsyncListOptions__initialised: return
from docopt import docopt
from libgsync.options import doc
from libgsync import __version__
options = docopt(
doc.__doc__ % __version__,
version = __version__,
options_first = True
)
paths = options.pop('<path>', None)
setattr(cls, "destination_path", paths.pop() if paths else None)
setattr(cls, "source_paths", paths)
setattr(cls, "options", options)
for k, v in options.iteritems():
setattr(cls, k, v)
GsyncListOptions._GsyncListOptions__initialised = True
def list(cls):
return GsyncListOptions
def __getattr__(cls, name):
cls.__initialiseClass()
return getattr(GsyncListOptions, name)[-1]
def __setattr__(cls, name, value):
# Substitut option names: --an-option-name for an_option_name
import re
name = re.sub(r'^__', "", re.sub(r'-', "_", name))
listvalue = []
# Ensure value is converted to a list type for GsyncListOptions
if isinstance(value, list):
if value:
listvalue = [] + value
else:
listvalue = [ None ]
else:
listvalue = [ value ]
type.__setattr__(GsyncListOptions, name, listvalue)
# Cleanup this module to prevent tinkering.
import sys
module = sys.modules[__name__]
del module.__dict__['GetGsyncOptionsType']
return GsyncOptionsType
# Our singlton abstract proxy class.
class GsyncOptions(object):
__metaclass__ = GetGsyncOptionsType()

The tl;dr version
The type(obj) function gets you the type of an object.
The type() of a class is its metaclass.
To use a metaclass:
class Foo(object):
__metaclass__ = MyMetaClass
type is its own metaclass. The class of a class is a metaclass-- the body of a class is the arguments passed to the metaclass that is used to construct the class.
Here you can read about how to use metaclasses to customize class construction.

type is actually a metaclass -- a class that creates another classes.
Most metaclass are the subclasses of type. The metaclass receives the new class as its first argument and provide access to class object with details as mentioned below:
>>> class MetaClass(type):
... def __init__(cls, name, bases, attrs):
... print ('class name: %s' %name )
... print ('Defining class %s' %cls)
... print('Bases %s: ' %bases)
... print('Attributes')
... for (name, value) in attrs.items():
... print ('%s :%r' %(name, value))
...
>>> class NewClass(object, metaclass=MetaClass):
... get_choch='dairy'
...
class name: NewClass
Bases <class 'object'>:
Defining class <class 'NewClass'>
get_choch :'dairy'
__module__ :'builtins'
__qualname__ :'NewClass'
Note:
Notice that the class was not instantiated at any time; the simple act of creating the class triggered execution of the metaclass.

Python classes are themselves objects - as in instance - of their meta-class.
The default metaclass, which is applied when when you determine classes as:
class foo:
...
meta class are used to apply some rule to an entire set of classes. For example, suppose you're building an ORM to access a database, and you want records from each table to be of a class mapped to that table (based on fields, business rules, etc..,), a possible use of metaclass is for instance, connection pool logic, which is share by all classes of record from all tables. Another use is logic to to support foreign keys, which involves multiple classes of records.
when you define metaclass, you subclass type, and can overrided the following magic methods to insert your logic.
class somemeta(type):
__new__(mcs, name, bases, clsdict):
"""
mcs: is the base metaclass, in this case type.
name: name of the new class, as provided by the user.
bases: tuple of base classes
clsdict: a dictionary containing all methods and attributes defined on class
you must return a class object by invoking the __new__ constructor on the base metaclass.
ie:
return type.__call__(mcs, name, bases, clsdict).
in the following case:
class foo(baseclass):
__metaclass__ = somemeta
an_attr = 12
def bar(self):
...
#classmethod
def foo(cls):
...
arguments would be : ( somemeta, "foo", (baseclass, baseofbase,..., object), {"an_attr":12, "bar": <function>, "foo": <bound class method>}
you can modify any of these values before passing on to type
"""
return type.__call__(mcs, name, bases, clsdict)
def __init__(self, name, bases, clsdict):
"""
called after type has been created. unlike in standard classes, __init__ method cannot modify the instance (cls) - and should be used for class validaton.
"""
pass
def __prepare__():
"""
returns a dict or something that can be used as a namespace.
the type will then attach methods and attributes from class definition to it.
call order :
somemeta.__new__ -> type.__new__ -> type.__init__ -> somemeta.__init__
"""
return dict()
def mymethod(cls):
""" works like a classmethod, but for class objects. Also, my method will not be visible to instances of cls.
"""
pass
anyhow, those two are the most commonly used hooks. metaclassing is powerful, and above is nowhere near and exhaustive list of uses for metaclassing.

The type() function can return the type of an object or create a new type,
for example, we can create a Hi class with the type() function and do not need to use this way with class Hi(object):
def func(self, name='mike'):
print('Hi, %s.' % name)
Hi = type('Hi', (object,), dict(hi=func))
h = Hi()
h.hi()
Hi, mike.
type(Hi)
type
type(h)
__main__.Hi
In addition to using type() to create classes dynamically, you can control creation behavior of class and use metaclass.
According to the Python object model, the class is the object, so the class must be an instance of another certain class.
By default, a Python class is instance of the type class. That is, type is metaclass of most of the built-in classes and metaclass of user-defined classes.
class ListMetaclass(type):
def __new__(cls, name, bases, attrs):
attrs['add'] = lambda self, value: self.append(value)
return type.__new__(cls, name, bases, attrs)
class CustomList(list, metaclass=ListMetaclass):
pass
lst = CustomList()
lst.add('custom_list_1')
lst.add('custom_list_2')
lst
['custom_list_1', 'custom_list_2']
Magic will take effect when we passed keyword arguments in metaclass, it indicates the Python interpreter to create the CustomList through ListMetaclass. new (), at this point, we can modify the class definition, for example, and add a new method and then return the revised definition.

In addition to the published answers I can say that a metaclass defines the behaviour for a class. So, you can explicitly set your metaclass. Whenever Python gets a keyword class then it starts searching for the metaclass. If it's not found – the default metaclass type is used to create the class's object. Using the __metaclass__ attribute, you can set metaclass of your class:
class MyClass:
__metaclass__ = type
# write here other method
# write here one more method
print(MyClass.__metaclass__)
It'll produce the output like this:
class 'type'
And, of course, you can create your own metaclass to define the behaviour of any class that are created using your class.
For doing that, your default metaclass type class must be inherited as this is the main metaclass:
class MyMetaClass(type):
__metaclass__ = type
# you can write here any behaviour you want
class MyTestClass:
__metaclass__ = MyMetaClass
Obj = MyTestClass()
print(Obj.__metaclass__)
print(MyMetaClass.__metaclass__)
The output will be:
class '__main__.MyMetaClass'
class 'type'

Note that in python 3.6 a new dunder method __init_subclass__(cls, **kwargs) was introduced to replace a lot of common use cases for metaclasses. Is is called when a subclass of the defining class is created. See python docs.

Here's another example of what it can be used for:
You can use the metaclass to change the function of its instance (the class).
class MetaMemberControl(type):
__slots__ = ()
#classmethod
def __prepare__(mcs, f_cls_name, f_cls_parents, # f_cls means: future class
meta_args=None, meta_options=None): # meta_args and meta_options is not necessarily needed, just so you know.
f_cls_attr = dict()
if not "do something or if you want to define your cool stuff of dict...":
return dict(make_your_special_dict=None)
else:
return f_cls_attr
def __new__(mcs, f_cls_name, f_cls_parents, f_cls_attr,
meta_args=None, meta_options=None):
original_getattr = f_cls_attr.get('__getattribute__')
original_setattr = f_cls_attr.get('__setattr__')
def init_getattr(self, item):
if not item.startswith('_'): # you can set break points at here
alias_name = '_' + item
if alias_name in f_cls_attr['__slots__']:
item = alias_name
if original_getattr is not None:
return original_getattr(self, item)
else:
return super(eval(f_cls_name), self).__getattribute__(item)
def init_setattr(self, key, value):
if not key.startswith('_') and ('_' + key) in f_cls_attr['__slots__']:
raise AttributeError(f"you can't modify private members:_{key}")
if original_setattr is not None:
original_setattr(self, key, value)
else:
super(eval(f_cls_name), self).__setattr__(key, value)
f_cls_attr['__getattribute__'] = init_getattr
f_cls_attr['__setattr__'] = init_setattr
cls = super().__new__(mcs, f_cls_name, f_cls_parents, f_cls_attr)
return cls
class Human(metaclass=MetaMemberControl):
__slots__ = ('_age', '_name')
def __init__(self, name, age):
self._name = name
self._age = age
def __getattribute__(self, item):
"""
is just for IDE recognize.
"""
return super().__getattribute__(item)
""" with MetaMemberControl then you don't have to write as following
#property
def name(self):
return self._name
#property
def age(self):
return self._age
"""
def test_demo():
human = Human('Carson', 27)
# human.age = 18 # you can't modify private members:_age <-- this is defined by yourself.
# human.k = 18 # 'Human' object has no attribute 'k' <-- system error.
age1 = human._age # It's OK, although the IDE will show some warnings. (Access to a protected member _age of a class)
age2 = human.age # It's OK! see below:
"""
if you do not define `__getattribute__` at the class of Human,
the IDE will show you: Unresolved attribute reference 'age' for class 'Human'
but it's ok on running since the MetaMemberControl will help you.
"""
if __name__ == '__main__':
test_demo()
The metaclass is powerful, there are many things (such as monkey magic) you can do with it, but be careful this may only be known to you.

The top answer is correct.
But readers may be coming here searching answers about similarly named inner classes. They are present in popular libraries, such as Django and WTForms.
As DavidW points out in the comments beneath this answer, these are library-specific features and are not to be confused with the advanced, unrelated Python language feature with a similar name.
Rather, these are namespaces within classes' dicts. They are constructed using inner classes for sake of readability.
In this example special field, abstract is visibly separate from fields of Author model.
from django.db import models
class Author(models.Model):
name = models.CharField(max_length=50)
email = models.EmailField()
class Meta:
abstract = True
Another example is from the documentation for WTForms:
from wtforms.form import Form
from wtforms.csrf.session import SessionCSRF
from wtforms.fields import StringField
class MyBaseForm(Form):
class Meta:
csrf = True
csrf_class = SessionCSRF
name = StringField("name")
This syntax does not get special treatment in the python programming language. Meta is not a keyword here, and does not trigger metaclass behavior. Rather, third-party library code in packages like Django and WTForms reads this property in the constructors of certain classes, and elsewhere.
The presence of these declarations modifies the behavior of the classes that have these declarations. For example, WTForms reads self.Meta.csrf to determine if the form needs a csrf field.

In object-oriented programming, a metaclass is a class whose instances are classes. Just as an ordinary class defines the behavior of certain objects, a metaclass defines the behavior of certain class and their instances
The term metaclass simply means something used to create classes. In other words, it is the class of a class. The metaclass is used to create the class so like the object being an instance of a class, a class is an instance of a metaclass. In python classes are also considered objects.

A class, in Python, is an object, and just like any other object, it is an instance of "something". This "something" is what is termed as a Metaclass. This metaclass is a special type of class that creates other class's objects. Hence, metaclass is responsible for making new classes. This allows the programmer to customize the way classes are generated.
To create a metaclass, overriding of new() and init() methods is usually done. new() can be overridden to change the way objects are created, while init() can be overridden to change the way of initializing the object. Metaclass can be created by a number of ways. One of the ways is to use type() function. type() function, when called with 3 parameters, creates a metaclass. The parameters are :-
Class Name
Tuple having base classes inherited by class
A dictionary having all class methods and class variables
Another way of creating a metaclass comprises of 'metaclass' keyword. Define the metaclass as a simple class. In the parameters of inherited class, pass metaclass=metaclass_name
Metaclass can be specifically used in the following situations :-
when a particular effect has to be applied to all the subclasses
Automatic change of class (on creation) is required
By API developers

I saw an interesting use case for metaclasses in a package called classutilities. It checks if all class variables are in upper case format (it is convenient to have unified logic for configuration classes), and checks if there are no instance level methods in class.
Another interesting example for metaclases was deactivation of unittests based on complex conditions (checking values of multiple environmental variables).

In Python, a metaclass is a subclass of a subclass that determines how a subclass behaves. A class is an instance of another metaclass. In Python, a class specifies how the class's instance will behave.
Since metaclasses are in charge of class generation, you can write your own custom metaclasses to change how classes are created by performing additional actions or injecting code. Custom metaclasses aren't always important, but they can be.

look this:
Python 3.10.0rc2 (tags/v3.10.0rc2:839d789, Sep 7 2021, 18:51:45) [MSC v.1929 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> class Object:
... pass
...
>>> class Meta(type):
... test = 'Worked!!!'
... def __repr__(self):
... return 'This is "Meta" metaclass'
...
>>> class ObjectWithMetaClass(metaclass=Meta):
... pass
...
>>> Object or type(Object())
<class '__main__.Object'>
>>> ObjectWithMetaClass or type(ObjectWithMetaClass())
This is "Meta" metaclass
>>> Object.test
AttributeError: ...
>>> ObjectWithMetaClass.test
'Worked!!!'
>>> type(Object)
<class 'type'>
>>> type(ObjectWithMetaClass)
<class '__main__.Meta'>
>>> type(type(ObjectWithMetaClass))
<class 'type'>
>>> Object.__bases__
(<class 'object'>,)
>>> ObjectWithMetaClass.__bases__
(<class 'object'>,)
>>> type(ObjectWithMetaClass).__bases__
(<class 'type'>,)
>>> Object.__mro__
(<class '__main__.Object'>, <class 'object'>)
>>> ObjectWithMetaClass.__mro__
(This is "Meta" metaclass, <class 'object'>)
>>>
In other words, when an object was not created (type of object), we looking MetaClass.

i want to add a little on why type.__new__() over type()
first, take a look at these classes
In [1]: class MyMeta(type):
...: def __new__(cls, cls_name, bases, attrs):
...: print(cls, cls_name, bases, attrs)
...: return super().__new__(cls, cls_name, bases, attrs)
...:
In [2]: class AClass(metaclass=MyMeta):
...: pass
...:
<class '__main__.MyMeta'> AClass () {'__module__': '__main__', '__qualname__': 'AClass'}
In [3]: class BClass:
...: pass
...:
In [4]: AClass.__class__
Out[4]: __main__.MyMeta
In [5]: BClass.__class__
Out[5]: type
In [6]: class SubAClass(AClass):
...: pass
...:
<class '__main__.MyMeta'> SubAClass (<class '__main__.AClass'>,) {'__module__': '__main__', '__qualname__': 'SubAClass'}
when we were trying to create SubAClass a subclass of AClass, Python would take a look at the metaclass we designated to be used to create SubAClass
and in this case, we did not pass a metaclass for SubAClass, so Python got a None for metaclass.
and Python would try to pick up the first base class of SubAClass, which is AClass, has a metaclass, which is MyMeta, then it would call MyMeta.__new__ to create SubAClass
for simplicity, we can say
type.__new__ just assigned our metaclass to the parent.__class__(AClass.__class__).
and when a subclass was defined(SubAClass) by class statement, Python
would just use the parent.__class__ to create that subclass unless
we've manually passed a metaclass to our subclass
apparently, if you called type() instead of type.__new__, the class
would have type as its __class__ attr.
that means AClass would be equivalent of BClass, both of them have type
as their __class__ attr
how the searching of metaclass works in C code?
it works pretty much like what we've just mentioned
the function builtin___build_class__ would be called when you defined a class
and code is just so straightforward
static PyObject *
builtin___build_class__(PyObject *self, PyObject *const *args, Py_ssize_t nargs,
PyObject *kwnames){
if (meta == NULL) {
/* if there are no bases, use type: */
if (PyTuple_GET_SIZE(bases) == 0) {
meta = (PyObject *) (&PyType_Type);
}
/* else get the type of the first base */
else {
PyObject *base0 = PyTuple_GET_ITEM(bases, 0);
meta = (PyObject *)Py_TYPE(base0);
}
Py_INCREF(meta);
isclass = 1; /* meta is really a class */
}
PyObject *margs[3] = {name, bases, ns};
cls = PyObject_VectorcallDict(meta, margs, 3, mkw);
}
basically, meta = (PyObject *)Py_TYPE(base0); is everything we want to know
it can be translated to be
meta = MyMeta = AClass.__class__ = Py_TYPE(AClass)

In Python or in any other language we have a type for every variable or object we declare. For getting type of anything(variable,object,etc.) in Python we can use type() function.
Bypassing the metaclass keyword in the class definition we can customize the class creation process.
class meta(type):
pass
class baseclass(metaclass=meta): # This is Mestaclass
pass
class derivedclass(baseclass):
pass
print(type(meta))
print(type(baseclass))
print(type(derivedclass))
When defining a new class if no metaclass is defined the default type metaclass is used. If a given metaclass is not the object(instance) of type(), in that situation it is used directly as a metaclass.

How to detect if `__init_subclass__` has been overridden in a subclass?

Normally in Python, it is possible to detect whether a method has been overridden in a subclass using the following technique:
>>> class Foo:
... def mymethod(self): pass
...
>>> class Bar(Foo): pass
...
>>> Bar.mymethod is Foo.mymethod
True
The expression Bar.mymethod is Foo.mymethod will evaluate to True if the method from Foo has not been overridden in Bar, but will evaluate to False if the method has been overridden in Bar. This technique works well with dunder methods inherited from object, as well as non-dunder methods:
>>> Bar.__new__ is Foo.__new__
True
>>> Bar.__eq__ is Foo.__eq__
True
We can formalise this logic in a function like so:
def method_has_been_overridden(superclass, subclass, method_name):
"""
Return `True` if the method with the name `method_name`
has been overridden in the subclass
or an intermediate class in the method resolution order
"""
if not issubclass(subclass, superclass):
raise ValueError(
"This function only makes sense if `subclass` is a subclass of `superclass`"
)
subclass_method = getattr(subclass, method_name)
if not callable(method):
raise ValueError(f"'{subclass.__name__}.{method_name}' is not a method")
return subclass_method is not getattr(superclass, method_name, object())
However, this technique fails when it comes to two methods: __init_subclass__ and __subclasshook__:
>>> class Foo: pass
...
>>> class Bar(Foo): pass
...
>>> Bar.__init_subclass__ is Foo.__init_subclass__
False
>>> Bar.__subclasshook__ is Foo.__subclasshook__
False
And, for an even more perplexing example:
>>> type.__init_subclass__ is type.__init_subclass__
False
I have two questions:
Why does this technique fail with these methods, and these methods only? (I have not been able to find any other examples where this technique fails -- but if there are any, I'd love to know about them!)
Is there an alternative technique that can be used to detect if __init_subclass__ or __subclasshook__ have been defined in a subclass after being left undefined in the superclass?

__init_subclass__ is special-cased to be a class method, whether you decorate it with classmethod or not. Just like Foo().mymethod returns a new method instance every time you access the attribute via a class instance, Foo.__init_subclass__ produces a new instance method every time you access the attribute via the class itself.
__subclasshook__, on the other hand, must be declared as a class method to work properly. It is not assumed to be a class method if you define it as a simple function/instance method.

__init_subclass__ is a special method, in that it is implicitly a classmethod even if you do not decorate it with #classmethod when defining it. However, the issue here does not arise from the fact that __init_subclass__ is a special method. Instead, there is a fundamental error in the technique you are using to detect whether a method has been overridden in a subclass: it won't work with any classmethods at all:
>>> class Foo:
... def mymethod(self): pass
... #classmethod
... def my_classmethod(cls): pass
...
>>> class Bar(Foo): pass
...
>>> Bar.mymethod is Foo.mymethod
True
>>> Bar.my_classmethod is Foo.my_classmethod
False
This is because of how bound methods in Python work: methods, in Python, are descriptors.
Observe the equivalence of the following lines of code with respect to instance methods. The first (more normal) way of calling mymethod on the instance f is merely syntactic sugar for the second way of calling the method on the instance f:
>>> class Foo:
... def mymethod(self):
... print('Instance method')
...
>>> f = Foo()
>>> f.mymethod()
Instance method
>>> Foo.__dict__['mymethod'].__get__(f, Foo)()
Instance method
Calling __get__ on the unbound method in Foo.__dict__ produces a new object each time; it is only by accessing the instance method on the class that we can test for identity, as you do in your question. However, with regards to classmethods, even accessing the method from the class will call __get__ on the method:
>>> class Foo:
... #classmethod
... def my_classmethod(cls):
... print('Class method')
...
>>> Foo.my_classmethod is Foo.my_classmethod
False
>>> Foo.my_classmethod()
Class method
>>> Foo.__dict__['my_classmethod'].__get__(Foo, Foo)()
Class method
What about __new__?
Your question points out that your existing method works with __new__. That's strange -- we've just established that this method doesn't work with classmethods, and __new__ certainly looks like a classmethod. The first parameter of __new__ is named cls, after all! However, the Python documentation makes clear that this isn't the case at all:
object.__new__(cls[, ...])
Called to create a new instance of class cls. __new__() is a static method (special-cased so you need not declare it as such) that takes the class of which an instance was requested as its first argument.
It's a staticmethod, not a classmethod! Mystery solved.
A better way of detecting whether a subclass overrides a method from a superclass
The only sure-fire way of knowing for sure if a method has been overridden in a subclass is by traversing the __dict__ of each class in the method resolution order:
def method_has_been_overridden(superclass, subclass, method_name):
"""
Return `True` if the method with the name `method_name`
has been overridden in the subclass
or an intermediate class in the method resolution order
"""
if not issubclass(subclass, superclass):
raise ValueError(
"This function only makes sense if `subclass` is a subclass of `superclass`"
)
subclass_method = getattr(subclass, method_name)
if not callable(subclass_method):
raise ValueError(f"'{subclass.__name__}.{method_name}' is not a method")
for cls in subclass.__mro__:
if cls is superclass:
return False
if method_name in cls.__dict__:
return True
This function can correctly determine whether or not __init_subclass__, or any other classmethod, has been overridden in a subclass:
>>> class Foo: pass
...
>>> class Bar(Foo): pass
...
>>> class Baz(Foo):
... def __init_subclass__(cls, *args, **kwargs):
... return super().__init_subclass__(*args, **kwargs)
>>> method_has_been_overridden(Foo, Bar, '__init_subclass__')
False
>>> method_has_been_overridden(Foo, Baz, '__init_subclass__')
True
Many thanks to #chepner and U12-Forward, whose excellent answers helped me figure this problem out.

__init_subclass__ and __subclasshook__ are class methods. As you can see here:
>>> Bar.__sizeof__
<method '__sizeof__' of 'object' objects>
>>> Bar.__eq__
<slot wrapper '__eq__' of 'object' objects>
>>> Bar.__subclasshook__
<built-in method __subclasshook__ of type object at 0x000002D70AAC5340>
>>> Bar.__init_subclass__
<built-in method __init_subclass__ of type object at 0x000002D70AAC5340>
>>> Foo.__init_subclass__
<built-in method __init_subclass__ of type object at 0x000002D70AACAF70>
>>>
__init_subclass__ and __subclasshook__ refer to their classes that are different instances, Bar's hex is 0x000002D70AAC5340, and Foo's hex is 0x000002D70AACAF70.
As you can see in the documentation of __init_subclass__, it says:
classmethod object.__init_subclass__(cls)
It says "classmethod".

Python Metaclasses are objects? [duplicate]

What are metaclasses? What are they used for?

Classes as objects
Before understanding metaclasses, it helps to understand Python classes more deeply. Python has a very peculiar idea of what classes are, which it borrows from the Smalltalk language.
In most languages, classes are just pieces of code that describe how to produce an object. That is somewhat true in Python too:
>>> class ObjectCreator(object):
... pass
>>> my_object = ObjectCreator()
>>> print(my_object)
<__main__.ObjectCreator object at 0x8974f2c>
But classes are more than that in Python. Classes are objects too.
Yes, objects.
When a Python script runs, every line of code is executed from top to bottom. When the Python interpreter encounters the class keyword, Python creates an object out of the "description" of the class that follows. Thus, the following instruction
>>> class ObjectCreator(object):
... pass
...creates an object with the name ObjectCreator!
This object (the class) is itself capable of creating objects (called instances).
But still, it's an object. Therefore, like all objects:
you can assign it to a variable1
JustAnotherVariable = ObjectCreator
you can attach attributes to it
ObjectCreator.class_attribute = 'foo'
you can pass it as a function parameter
print(ObjectCreator)
1 Note that merely assigning it to another variable doesn't change the class's __name__, i.e.,
>>> print(JustAnotherVariable)
<class '__main__.ObjectCreator'>
>>> print(JustAnotherVariable())
<__main__.ObjectCreator object at 0x8997b4c>
Creating classes dynamically
Since classes are objects, you can create them on the fly, like any object.
First, you can create a class in a function using class:
>>> def choose_class(name):
... if name == 'foo':
... class Foo(object):
... pass
... return Foo # return the class, not an instance
... else:
... class Bar(object):
... pass
... return Bar
...
>>> MyClass = choose_class('foo')
>>> print(MyClass) # the function returns a class, not an instance
<class '__main__.Foo'>
>>> print(MyClass()) # you can create an object from this class
<__main__.Foo object at 0x89c6d4c>
But it's not so dynamic, since you still have to write the whole class yourself.
Since classes are objects, they must be generated by something.
When you use the class keyword, Python creates this object automatically. But as
with most things in Python, it gives you a way to do it manually.
Remember the function type? The good old function that lets you know what
type an object is:
>>> print(type(1))
<type 'int'>
>>> print(type("1"))
<type 'str'>
>>> print(type(ObjectCreator))
<type 'type'>
>>> print(type(ObjectCreator()))
<class '__main__.ObjectCreator'>
Well, type has also a completely different ability: it can create classes on the fly. type can take the description of a class as parameters,
and return a class.
(I know, it's silly that the same function can have two completely different uses according to the parameters you pass to it. It's an issue due to backward
compatibility in Python)
type works this way:
type(name, bases, attrs)
Where:
name: name of the class
bases: tuple of the parent class (for inheritance, can be empty)
attrs: dictionary containing attributes names and values
e.g.:
>>> class MyShinyClass(object):
... pass
can be created manually this way:
>>> MyShinyClass = type('MyShinyClass', (), {}) # returns a class object
>>> print(MyShinyClass)
<class '__main__.MyShinyClass'>
>>> print(MyShinyClass()) # create an instance with the class
<__main__.MyShinyClass object at 0x8997cec>
You'll notice that we use MyShinyClass as the name of the class
and as the variable to hold the class reference. They can be different,
but there is no reason to complicate things.
type accepts a dictionary to define the attributes of the class. So:
>>> class Foo(object):
... bar = True
Can be translated to:
>>> Foo = type('Foo', (), {'bar':True})
And used as a normal class:
>>> print(Foo)
<class '__main__.Foo'>
>>> print(Foo.bar)
True
>>> f = Foo()
>>> print(f)
<__main__.Foo object at 0x8a9b84c>
>>> print(f.bar)
True
And of course, you can inherit from it, so:
>>> class FooChild(Foo):
... pass
would be:
>>> FooChild = type('FooChild', (Foo,), {})
>>> print(FooChild)
<class '__main__.FooChild'>
>>> print(FooChild.bar) # bar is inherited from Foo
True
Eventually, you'll want to add methods to your class. Just define a function
with the proper signature and assign it as an attribute.
>>> def echo_bar(self):
... print(self.bar)
...
>>> FooChild = type('FooChild', (Foo,), {'echo_bar': echo_bar})
>>> hasattr(Foo, 'echo_bar')
False
>>> hasattr(FooChild, 'echo_bar')
True
>>> my_foo = FooChild()
>>> my_foo.echo_bar()
True
And you can add even more methods after you dynamically create the class, just like adding methods to a normally created class object.
>>> def echo_bar_more(self):
... print('yet another method')
...
>>> FooChild.echo_bar_more = echo_bar_more
>>> hasattr(FooChild, 'echo_bar_more')
True
You see where we are going: in Python, classes are objects, and you can create a class on the fly, dynamically.
This is what Python does when you use the keyword class, and it does so by using a metaclass.
What are metaclasses (finally)
Metaclasses are the 'stuff' that creates classes.
You define classes in order to create objects, right?
But we learned that Python classes are objects.
Well, metaclasses are what create these objects. They are the classes' classes,
you can picture them this way:
MyClass = MetaClass()
my_object = MyClass()
You've seen that type lets you do something like this:
MyClass = type('MyClass', (), {})
It's because the function type is in fact a metaclass. type is the
metaclass Python uses to create all classes behind the scenes.
Now you wonder "why the heck is it written in lowercase, and not Type?"
Well, I guess it's a matter of consistency with str, the class that creates
strings objects, and int the class that creates integer objects. type is
just the class that creates class objects.
You see that by checking the __class__ attribute.
Everything, and I mean everything, is an object in Python. That includes integers,
strings, functions and classes. All of them are objects. And all of them have
been created from a class:
>>> age = 35
>>> age.__class__
<type 'int'>
>>> name = 'bob'
>>> name.__class__
<type 'str'>
>>> def foo(): pass
>>> foo.__class__
<type 'function'>
>>> class Bar(object): pass
>>> b = Bar()
>>> b.__class__
<class '__main__.Bar'>
Now, what is the __class__ of any __class__ ?
>>> age.__class__.__class__
<type 'type'>
>>> name.__class__.__class__
<type 'type'>
>>> foo.__class__.__class__
<type 'type'>
>>> b.__class__.__class__
<type 'type'>
So, a metaclass is just the stuff that creates class objects.
You can call it a 'class factory' if you wish.
type is the built-in metaclass Python uses, but of course, you can create your
own metaclass.
The __metaclass__ attribute
In Python 2, you can add a __metaclass__ attribute when you write a class (see next section for the Python 3 syntax):
class Foo(object):
__metaclass__ = something...
[...]
If you do so, Python will use the metaclass to create the class Foo.
Careful, it's tricky.
You write class Foo(object) first, but the class object Foo is not created
in memory yet.
Python will look for __metaclass__ in the class definition. If it finds it,
it will use it to create the object class Foo. If it doesn't, it will use
type to create the class.
Read that several times.
When you do:
class Foo(Bar):
pass
Python does the following:
Is there a __metaclass__ attribute in Foo?
If yes, create in-memory a class object (I said a class object, stay with me here), with the name Foo by using what is in __metaclass__.
If Python can't find __metaclass__, it will look for a __metaclass__ at the MODULE level, and try to do the same (but only for classes that don't inherit anything, basically old-style classes).
Then if it can't find any __metaclass__ at all, it will use the Bar's (the first parent) own metaclass (which might be the default type) to create the class object.
Be careful here that the __metaclass__ attribute will not be inherited, the metaclass of the parent (Bar.__class__) will be. If Bar used a __metaclass__ attribute that created Bar with type() (and not type.__new__()), the subclasses will not inherit that behavior.
Now the big question is, what can you put in __metaclass__?
The answer is something that can create a class.
And what can create a class? type, or anything that subclasses or uses it.
Metaclasses in Python 3
The syntax to set the metaclass has been changed in Python 3:
class Foo(object, metaclass=something):
...
i.e. the __metaclass__ attribute is no longer used, in favor of a keyword argument in the list of base classes.
The behavior of metaclasses however stays largely the same.
One thing added to metaclasses in Python 3 is that you can also pass attributes as keyword-arguments into a metaclass, like so:
class Foo(object, metaclass=something, kwarg1=value1, kwarg2=value2):
...
Read the section below for how Python handles this.
Custom metaclasses
The main purpose of a metaclass is to change the class automatically,
when it's created.
You usually do this for APIs, where you want to create classes matching the
current context.
Imagine a stupid example, where you decide that all classes in your module
should have their attributes written in uppercase. There are several ways to
do this, but one way is to set __metaclass__ at the module level.
This way, all classes of this module will be created using this metaclass,
and we just have to tell the metaclass to turn all attributes to uppercase.
Luckily, __metaclass__ can actually be any callable, it doesn't need to be a
formal class (I know, something with 'class' in its name doesn't need to be
a class, go figure... but it's helpful).
So we will start with a simple example, by using a function.
# the metaclass will automatically get passed the same argument
# that you usually pass to `type`
def upper_attr(future_class_name, future_class_parents, future_class_attrs):
"""
Return a class object, with the list of its attribute turned
into uppercase.
"""
# pick up any attribute that doesn't start with '__' and uppercase it
uppercase_attrs = {
attr if attr.startswith("__") else attr.upper(): v
for attr, v in future_class_attrs.items()
}
# let `type` do the class creation
return type(future_class_name, future_class_parents, uppercase_attrs)
__metaclass__ = upper_attr # this will affect all classes in the module
class Foo(): # global __metaclass__ won't work with "object" though
# but we can define __metaclass__ here instead to affect only this class
# and this will work with "object" children
bar = 'bip'
Let's check:
>>> hasattr(Foo, 'bar')
False
>>> hasattr(Foo, 'BAR')
True
>>> Foo.BAR
'bip'
Now, let's do exactly the same, but using a real class for a metaclass:
# remember that `type` is actually a class like `str` and `int`
# so you can inherit from it
class UpperAttrMetaclass(type):
# __new__ is the method called before __init__
# it's the method that creates the object and returns it
# while __init__ just initializes the object passed as parameter
# you rarely use __new__, except when you want to control how the object
# is created.
# here the created object is the class, and we want to customize it
# so we override __new__
# you can do some stuff in __init__ too if you wish
# some advanced use involves overriding __call__ as well, but we won't
# see this
def __new__(upperattr_metaclass, future_class_name,
future_class_parents, future_class_attrs):
uppercase_attrs = {
attr if attr.startswith("__") else attr.upper(): v
for attr, v in future_class_attrs.items()
}
return type(future_class_name, future_class_parents, uppercase_attrs)
Let's rewrite the above, but with shorter and more realistic variable names now that we know what they mean:
class UpperAttrMetaclass(type):
def __new__(cls, clsname, bases, attrs):
uppercase_attrs = {
attr if attr.startswith("__") else attr.upper(): v
for attr, v in attrs.items()
}
return type(clsname, bases, uppercase_attrs)
You may have noticed the extra argument cls. There is
nothing special about it: __new__ always receives the class it's defined in, as the first parameter. Just like you have self for ordinary methods which receive the instance as the first parameter, or the defining class for class methods.
But this is not proper OOP. We are calling type directly and we aren't overriding or calling the parent's __new__. Let's do that instead:
class UpperAttrMetaclass(type):
def __new__(cls, clsname, bases, attrs):
uppercase_attrs = {
attr if attr.startswith("__") else attr.upper(): v
for attr, v in attrs.items()
}
return type.__new__(cls, clsname, bases, uppercase_attrs)
We can make it even cleaner by using super, which will ease inheritance (because yes, you can have metaclasses, inheriting from metaclasses, inheriting from type):
class UpperAttrMetaclass(type):
def __new__(cls, clsname, bases, attrs):
uppercase_attrs = {
attr if attr.startswith("__") else attr.upper(): v
for attr, v in attrs.items()
}
# Python 2 requires passing arguments to super:
return super(UpperAttrMetaclass, cls).__new__(
cls, clsname, bases, uppercase_attrs)
# Python 3 can use no-arg super() which infers them:
return super().__new__(cls, clsname, bases, uppercase_attrs)
Oh, and in Python 3 if you do this call with keyword arguments, like this:
class Foo(object, metaclass=MyMetaclass, kwarg1=value1):
...
It translates to this in the metaclass to use it:
class MyMetaclass(type):
def __new__(cls, clsname, bases, dct, kwargs1=default):
...
That's it. There is really nothing more about metaclasses.
The reason behind the complexity of the code using metaclasses is not because
of metaclasses, it's because you usually use metaclasses to do twisted stuff
relying on introspection, manipulating inheritance, vars such as __dict__, etc.
Indeed, metaclasses are especially useful to do black magic, and therefore
complicated stuff. But by themselves, they are simple:
intercept a class creation
modify the class
return the modified class
Why would you use metaclasses classes instead of functions?
Since __metaclass__ can accept any callable, why would you use a class
since it's obviously more complicated?
There are several reasons to do so:
The intention is clear. When you read UpperAttrMetaclass(type), you know
what's going to follow
You can use OOP. Metaclass can inherit from metaclass, override parent methods. Metaclasses can even use metaclasses.
Subclasses of a class will be instances of its metaclass if you specified a metaclass-class, but not with a metaclass-function.
You can structure your code better. You never use metaclasses for something as trivial as the above example. It's usually for something complicated. Having the ability to make several methods and group them in one class is very useful to make the code easier to read.
You can hook on __new__, __init__ and __call__. Which will allow you to do different stuff, Even if usually you can do it all in __new__,
some people are just more comfortable using __init__.
These are called metaclasses, damn it! It must mean something!
Why would you use metaclasses?
Now the big question. Why would you use some obscure error-prone feature?
Well, usually you don't:
Metaclasses are deeper magic that
99% of users should never worry about it.
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).
Python Guru Tim Peters
The main use case for a metaclass is creating an API. A typical example of this is the Django ORM. It allows you to define something like this:
class Person(models.Model):
name = models.CharField(max_length=30)
age = models.IntegerField()
But if you do this:
person = Person(name='bob', age='35')
print(person.age)
It won't return an IntegerField object. It will return an int, and can even take it directly from the database.
This is possible because models.Model defines __metaclass__ and
it uses some magic that will turn the Person you just defined with simple statements
into a complex hook to a database field.
Django makes something complex look simple by exposing a simple API
and using metaclasses, recreating code from this API to do the real job
behind the scenes.
The last word
First, you know that classes are objects that can create instances.
Well, in fact, classes are themselves instances. Of metaclasses.
>>> class Foo(object): pass
>>> id(Foo)
142630324
Everything is an object in Python, and they are all either instance of classes
or instances of metaclasses.
Except for type.
type is actually its own metaclass. This is not something you could
reproduce in pure Python, and is done by cheating a little bit at the implementation
level.
Secondly, metaclasses are complicated. You may not want to use them for
very simple class alterations. You can change classes by using two different techniques:
monkey patching
class decorators
99% of the time you need class alteration, you are better off using these.
But 98% of the time, you don't need class alteration at all.

A metaclass is the class of a class. A class defines how an instance of the class (i.e. an object) behaves while a metaclass defines how a class behaves. A class is an instance of a metaclass.
While in Python you can use arbitrary callables for metaclasses (like Jerub shows), the better approach is to make it an actual class itself. type is the usual metaclass in Python. type is itself a class, and it is its own type. You won't be able to recreate something like type purely in Python, but Python cheats a little. To create your own metaclass in Python you really just want to subclass type.
A metaclass is most commonly used as a class-factory. When you create an object by calling the class, Python creates a new class (when it executes the 'class' statement) by calling the metaclass. Combined with the normal __init__ and __new__ methods, metaclasses therefore allow you to do 'extra things' when creating a class, like registering the new class with some registry or replace the class with something else entirely.
When the class statement is executed, Python first executes the body of the class statement as a normal block of code. The resulting namespace (a dict) holds the attributes of the class-to-be. The metaclass is determined by looking at the baseclasses of the class-to-be (metaclasses are inherited), at the __metaclass__ attribute of the class-to-be (if any) or the __metaclass__ global variable. The metaclass is then called with the name, bases and attributes of the class to instantiate it.
However, metaclasses actually define the type of a class, not just a factory for it, so you can do much more with them. You can, for instance, define normal methods on the metaclass. These metaclass-methods are like classmethods in that they can be called on the class without an instance, but they are also not like classmethods in that they cannot be called on an instance of the class. type.__subclasses__() is an example of a method on the type metaclass. You can also define the normal 'magic' methods, like __add__, __iter__ and __getattr__, to implement or change how the class behaves.
Here's an aggregated example of the bits and pieces:
def make_hook(f):
"""Decorator to turn 'foo' method into '__foo__'"""
f.is_hook = 1
return f
class MyType(type):
def __new__(mcls, name, bases, attrs):
if name.startswith('None'):
return None
# Go over attributes and see if they should be renamed.
newattrs = {}
for attrname, attrvalue in attrs.iteritems():
if getattr(attrvalue, 'is_hook', 0):
newattrs['__%s__' % attrname] = attrvalue
else:
newattrs[attrname] = attrvalue
return super(MyType, mcls).__new__(mcls, name, bases, newattrs)
def __init__(self, name, bases, attrs):
super(MyType, self).__init__(name, bases, attrs)
# classregistry.register(self, self.interfaces)
print "Would register class %s now." % self
def __add__(self, other):
class AutoClass(self, other):
pass
return AutoClass
# Alternatively, to autogenerate the classname as well as the class:
# return type(self.__name__ + other.__name__, (self, other), {})
def unregister(self):
# classregistry.unregister(self)
print "Would unregister class %s now." % self
class MyObject:
__metaclass__ = MyType
class NoneSample(MyObject):
pass
# Will print "NoneType None"
print type(NoneSample), repr(NoneSample)
class Example(MyObject):
def __init__(self, value):
self.value = value
#make_hook
def add(self, other):
return self.__class__(self.value + other.value)
# Will unregister the class
Example.unregister()
inst = Example(10)
# Will fail with an AttributeError
#inst.unregister()
print inst + inst
class Sibling(MyObject):
pass
ExampleSibling = Example + Sibling
# ExampleSibling is now a subclass of both Example and Sibling (with no
# content of its own) although it will believe it's called 'AutoClass'
print ExampleSibling
print ExampleSibling.__mro__

Note, this answer is for Python 2.x as it was written in 2008, metaclasses are slightly different in 3.x.
Metaclasses are the secret sauce that make 'class' work. The default metaclass for a new style object is called 'type'.
class type(object)
| type(object) -> the object's type
| type(name, bases, dict) -> a new type
Metaclasses take 3 args. 'name', 'bases' and 'dict'
Here is where the secret starts. Look for where name, bases and the dict come from in this example class definition.
class ThisIsTheName(Bases, Are, Here):
All_the_code_here
def doesIs(create, a):
dict
Lets define a metaclass that will demonstrate how 'class:' calls it.
def test_metaclass(name, bases, dict):
print 'The Class Name is', name
print 'The Class Bases are', bases
print 'The dict has', len(dict), 'elems, the keys are', dict.keys()
return "yellow"
class TestName(object, None, int, 1):
__metaclass__ = test_metaclass
foo = 1
def baz(self, arr):
pass
print 'TestName = ', repr(TestName)
# output =>
The Class Name is TestName
The Class Bases are (<type 'object'>, None, <type 'int'>, 1)
The dict has 4 elems, the keys are ['baz', '__module__', 'foo', '__metaclass__']
TestName = 'yellow'
And now, an example that actually means something, this will automatically make the variables in the list "attributes" set on the class, and set to None.
def init_attributes(name, bases, dict):
if 'attributes' in dict:
for attr in dict['attributes']:
dict[attr] = None
return type(name, bases, dict)
class Initialised(object):
__metaclass__ = init_attributes
attributes = ['foo', 'bar', 'baz']
print 'foo =>', Initialised.foo
# output=>
foo => None
Note that the magic behaviour that Initialised gains by having the metaclass init_attributes is not passed onto a subclass of Initialised.
Here is an even more concrete example, showing how you can subclass 'type' to make a metaclass that performs an action when the class is created. This is quite tricky:
class MetaSingleton(type):
instance = None
def __call__(cls, *args, **kw):
if cls.instance is None:
cls.instance = super(MetaSingleton, cls).__call__(*args, **kw)
return cls.instance
class Foo(object):
__metaclass__ = MetaSingleton
a = Foo()
b = Foo()
assert a is b

Others have explained how metaclasses work and how they fit into the Python type system. Here's an example of what they can be used for. In a testing framework I wrote, I wanted to keep track of the order in which classes were defined, so that I could later instantiate them in this order. I found it easiest to do this using a metaclass.
class MyMeta(type):
counter = 0
def __init__(cls, name, bases, dic):
type.__init__(cls, name, bases, dic)
cls._order = MyMeta.counter
MyMeta.counter += 1
class MyType(object): # Python 2
__metaclass__ = MyMeta
class MyType(metaclass=MyMeta): # Python 3
pass
Anything that's a subclass of MyType then gets a class attribute _order that records the order in which the classes were defined.

One use for metaclasses is adding new properties and methods to an instance automatically.
For example, if you look at Django models, their definition looks a bit confusing. It looks as if you are only defining class properties:
class Person(models.Model):
first_name = models.CharField(max_length=30)
last_name = models.CharField(max_length=30)
However, at runtime the Person objects are filled with all sorts of useful methods. See the source for some amazing metaclassery.

I think the ONLamp introduction to metaclass programming is well written and gives a really good introduction to the topic despite being several years old already.
http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html (archived at https://web.archive.org/web/20080206005253/http://www.onlamp.com/pub/a/python/2003/04/17/metaclasses.html)
In short: A class is a blueprint for the creation of an instance, a metaclass is a blueprint for the creation of a class. It can be easily seen that in Python classes need to be first-class objects too to enable this behavior.
I've never written one myself, but I think one of the nicest uses of metaclasses can be seen in the Django framework. The model classes use a metaclass approach to enable a declarative style of writing new models or form classes. While the metaclass is creating the class, all members get the possibility to customize the class itself.
Creating a new model
The metaclass enabling this
The thing that's left to say is: If you don't know what metaclasses are, the probability that you will not need them is 99%.

What are metaclasses? What do you use them for?
TLDR: A metaclass instantiates and defines behavior for a class just like a class instantiates and defines behavior for an instance.
Pseudocode:
>>> Class(...)
instance
The above should look familiar. Well, where does Class come from? It's an instance of a metaclass (also pseudocode):
>>> Metaclass(...)
Class
In real code, we can pass the default metaclass, type, everything we need to instantiate a class and we get a class:
>>> type('Foo', (object,), {}) # requires a name, bases, and a namespace
<class '__main__.Foo'>
Putting it differently
A class is to an instance as a metaclass is to a class.
When we instantiate an object, we get an instance:
>>> object() # instantiation of class
<object object at 0x7f9069b4e0b0> # instance
Likewise, when we define a class explicitly with the default metaclass, type, we instantiate it:
>>> type('Object', (object,), {}) # instantiation of metaclass
<class '__main__.Object'> # instance
Put another way, a class is an instance of a metaclass:
>>> isinstance(object, type)
True
Put a third way, a metaclass is a class's class.
>>> type(object) == type
True
>>> object.__class__
<class 'type'>
When you write a class definition and Python executes it, it uses a metaclass to instantiate the class object (which will, in turn, be used to instantiate instances of that class).
Just as we can use class definitions to change how custom object instances behave, we can use a metaclass class definition to change the way a class object behaves.
What can they be used for? From the docs:
The potential uses for metaclasses are boundless. Some ideas that have been explored include logging, interface checking, automatic delegation, automatic property creation, proxies, frameworks, and automatic resource locking/synchronization.
Nevertheless, it is usually encouraged for users to avoid using metaclasses unless absolutely necessary.
You use a metaclass every time you create a class:
When you write a class definition, for example, like this,
class Foo(object):
'demo'
You instantiate a class object.
>>> Foo
<class '__main__.Foo'>
>>> isinstance(Foo, type), isinstance(Foo, object)
(True, True)
It is the same as functionally calling type with the appropriate arguments and assigning the result to a variable of that name:
name = 'Foo'
bases = (object,)
namespace = {'__doc__': 'demo'}
Foo = type(name, bases, namespace)
Note, some things automatically get added to the __dict__, i.e., the namespace:
>>> Foo.__dict__
dict_proxy({'__dict__': <attribute '__dict__' of 'Foo' objects>,
'__module__': '__main__', '__weakref__': <attribute '__weakref__'
of 'Foo' objects>, '__doc__': 'demo'})
The metaclass of the object we created, in both cases, is type.
(A side-note on the contents of the class __dict__: __module__ is there because classes must know where they are defined, and __dict__ and __weakref__ are there because we don't define __slots__ - if we define __slots__ we'll save a bit of space in the instances, as we can disallow __dict__ and __weakref__ by excluding them. For example:
>>> Baz = type('Bar', (object,), {'__doc__': 'demo', '__slots__': ()})
>>> Baz.__dict__
mappingproxy({'__doc__': 'demo', '__slots__': (), '__module__': '__main__'})
... but I digress.)
We can extend type just like any other class definition:
Here's the default __repr__ of classes:
>>> Foo
<class '__main__.Foo'>
One of the most valuable things we can do by default in writing a Python object is to provide it with a good __repr__. When we call help(repr) we learn that there's a good test for a __repr__ that also requires a test for equality - obj == eval(repr(obj)). The following simple implementation of __repr__ and __eq__ for class instances of our type class provides us with a demonstration that may improve on the default __repr__ of classes:
class Type(type):
def __repr__(cls):
"""
>>> Baz
Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
>>> eval(repr(Baz))
Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
"""
metaname = type(cls).__name__
name = cls.__name__
parents = ', '.join(b.__name__ for b in cls.__bases__)
if parents:
parents += ','
namespace = ', '.join(': '.join(
(repr(k), repr(v) if not isinstance(v, type) else v.__name__))
for k, v in cls.__dict__.items())
return '{0}(\'{1}\', ({2}), {{{3}}})'.format(metaname, name, parents, namespace)
def __eq__(cls, other):
"""
>>> Baz == eval(repr(Baz))
True
"""
return (cls.__name__, cls.__bases__, cls.__dict__) == (
other.__name__, other.__bases__, other.__dict__)
So now when we create an object with this metaclass, the __repr__ echoed on the command line provides a much less ugly sight than the default:
>>> class Bar(object): pass
>>> Baz = Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
>>> Baz
Type('Baz', (Foo, Bar,), {'__module__': '__main__', '__doc__': None})
With a nice __repr__ defined for the class instance, we have a stronger ability to debug our code. However, much further checking with eval(repr(Class)) is unlikely (as functions would be rather impossible to eval from their default __repr__'s).
An expected usage: __prepare__ a namespace
If, for example, we want to know in what order a class's methods are created in, we could provide an ordered dict as the namespace of the class. We would do this with __prepare__ which returns the namespace dict for the class if it is implemented in Python 3:
from collections import OrderedDict
class OrderedType(Type):
#classmethod
def __prepare__(metacls, name, bases, **kwargs):
return OrderedDict()
def __new__(cls, name, bases, namespace, **kwargs):
result = Type.__new__(cls, name, bases, dict(namespace))
result.members = tuple(namespace)
return result
And usage:
class OrderedMethodsObject(object, metaclass=OrderedType):
def method1(self): pass
def method2(self): pass
def method3(self): pass
def method4(self): pass
And now we have a record of the order in which these methods (and other class attributes) were created:
>>> OrderedMethodsObject.members
('__module__', '__qualname__', 'method1', 'method2', 'method3', 'method4')
Note, this example was adapted from the documentation - the new enum in the standard library does this.
So what we did was instantiate a metaclass by creating a class. We can also treat the metaclass as we would any other class. It has a method resolution order:
>>> inspect.getmro(OrderedType)
(<class '__main__.OrderedType'>, <class '__main__.Type'>, <class 'type'>, <class 'object'>)
And it has approximately the correct repr (which we can no longer eval unless we can find a way to represent our functions.):
>>> OrderedMethodsObject
OrderedType('OrderedMethodsObject', (object,), {'method1': <function OrderedMethodsObject.method1 at 0x0000000002DB01E0>, 'members': ('__module__', '__qualname__', 'method1', 'method2', 'method3', 'method4'), 'method3': <function OrderedMet
hodsObject.method3 at 0x0000000002DB02F0>, 'method2': <function OrderedMethodsObject.method2 at 0x0000000002DB0268>, '__module__': '__main__', '__weakref__': <attribute '__weakref__' of 'OrderedMethodsObject' objects>, '__doc__': None, '__d
ict__': <attribute '__dict__' of 'OrderedMethodsObject' objects>, 'method4': <function OrderedMethodsObject.method4 at 0x0000000002DB0378>})

Python 3 update
There are (at this point) two key methods in a metaclass:
__prepare__, and
__new__
__prepare__ lets you supply a custom mapping (such as an OrderedDict) to be used as the namespace while the class is being created. You must return an instance of whatever namespace you choose. If you don't implement __prepare__ a normal dict is used.
__new__ is responsible for the actual creation/modification of the final class.
A bare-bones, do-nothing-extra metaclass would like:
class Meta(type):
def __prepare__(metaclass, cls, bases):
return dict()
def __new__(metacls, cls, bases, clsdict):
return super().__new__(metacls, cls, bases, clsdict)
A simple example:
Say you want some simple validation code to run on your attributes -- like it must always be an int or a str. Without a metaclass, your class would look something like:
class Person:
weight = ValidateType('weight', int)
age = ValidateType('age', int)
name = ValidateType('name', str)
As you can see, you have to repeat the name of the attribute twice. This makes typos possible along with irritating bugs.
A simple metaclass can address that problem:
class Person(metaclass=Validator):
weight = ValidateType(int)
age = ValidateType(int)
name = ValidateType(str)
This is what the metaclass would look like (not using __prepare__ since it is not needed):
class Validator(type):
def __new__(metacls, cls, bases, clsdict):
# search clsdict looking for ValidateType descriptors
for name, attr in clsdict.items():
if isinstance(attr, ValidateType):
attr.name = name
attr.attr = '_' + name
# create final class and return it
return super().__new__(metacls, cls, bases, clsdict)
A sample run of:
p = Person()
p.weight = 9
print(p.weight)
p.weight = '9'
produces:
9
Traceback (most recent call last):
File "simple_meta.py", line 36, in <module>
p.weight = '9'
File "simple_meta.py", line 24, in __set__
(self.name, self.type, value))
TypeError: weight must be of type(s) <class 'int'> (got '9')
Note: This example is simple enough it could have also been accomplished with a class decorator, but presumably an actual metaclass would be doing much more.
The 'ValidateType' class for reference:
class ValidateType:
def __init__(self, type):
self.name = None # will be set by metaclass
self.attr = None # will be set by metaclass
self.type = type
def __get__(self, inst, cls):
if inst is None:
return self
else:
return inst.__dict__[self.attr]
def __set__(self, inst, value):
if not isinstance(value, self.type):
raise TypeError('%s must be of type(s) %s (got %r)' %
(self.name, self.type, value))
else:
inst.__dict__[self.attr] = value

Role of a metaclass' __call__() method when creating a class instance
If you've done Python programming for more than a few months you'll eventually stumble upon code that looks like this:
# define a class
class SomeClass(object):
# ...
# some definition here ...
# ...
# create an instance of it
instance = SomeClass()
# then call the object as if it's a function
result = instance('foo', 'bar')
The latter is possible when you implement the __call__() magic method on the class.
class SomeClass(object):
# ...
# some definition here ...
# ...
def __call__(self, foo, bar):
return bar + foo
The __call__() method is invoked when an instance of a class is used as a callable. But as we've seen from previous answers a class itself is an instance of a metaclass, so when we use the class as a callable (i.e. when we create an instance of it) we're actually calling its metaclass' __call__() method. At this point most Python programmers are a bit confused because they've been told that when creating an instance like this instance = SomeClass() you're calling its __init__() method. Some who've dug a bit deeper know that before __init__() there's __new__(). Well, today another layer of truth is being revealed, before __new__() there's the metaclass' __call__().
Let's study the method call chain from specifically the perspective of creating an instance of a class.
This is a metaclass that logs exactly the moment before an instance is created and the moment it's about to return it.
class Meta_1(type):
def __call__(cls):
print "Meta_1.__call__() before creating an instance of ", cls
instance = super(Meta_1, cls).__call__()
print "Meta_1.__call__() about to return instance."
return instance
This is a class that uses that metaclass
class Class_1(object):
__metaclass__ = Meta_1
def __new__(cls):
print "Class_1.__new__() before creating an instance."
instance = super(Class_1, cls).__new__(cls)
print "Class_1.__new__() about to return instance."
return instance
def __init__(self):
print "entering Class_1.__init__() for instance initialization."
super(Class_1,self).__init__()
print "exiting Class_1.__init__()."
And now let's create an instance of Class_1
instance = Class_1()
# Meta_1.__call__() before creating an instance of <class '__main__.Class_1'>.
# Class_1.__new__() before creating an instance.
# Class_1.__new__() about to return instance.
# entering Class_1.__init__() for instance initialization.
# exiting Class_1.__init__().
# Meta_1.__call__() about to return instance.
Observe that the code above doesn't actually do anything more than logging the tasks. Each method delegates the actual work to its parent's implementation, thus keeping the default behavior. Since type is Meta_1's parent class (type being the default parent metaclass) and considering the ordering sequence of the output above, we now have a clue as to what would be the pseudo implementation of type.__call__():
class type:
def __call__(cls, *args, **kwarg):
# ... maybe a few things done to cls here
# then we call __new__() on the class to create an instance
instance = cls.__new__(cls, *args, **kwargs)
# ... maybe a few things done to the instance here
# then we initialize the instance with its __init__() method
instance.__init__(*args, **kwargs)
# ... maybe a few more things done to instance here
# then we return it
return instance
We can see that the metaclass' __call__() method is the one that's called first. It then delegates creation of the instance to the class's __new__() method and initialization to the instance's __init__(). It's also the one that ultimately returns the instance.
From the above it stems that the metaclass' __call__() is also given the opportunity to decide whether or not a call to Class_1.__new__() or Class_1.__init__() will eventually be made. Over the course of its execution it could actually return an object that hasn't been touched by either of these methods. Take for example this approach to the singleton pattern:
class Meta_2(type):
singletons = {}
def __call__(cls, *args, **kwargs):
if cls in Meta_2.singletons:
# we return the only instance and skip a call to __new__()
# and __init__()
print ("{} singleton returning from Meta_2.__call__(), "
"skipping creation of new instance.".format(cls))
return Meta_2.singletons[cls]
# else if the singleton isn't present we proceed as usual
print "Meta_2.__call__() before creating an instance."
instance = super(Meta_2, cls).__call__(*args, **kwargs)
Meta_2.singletons[cls] = instance
print "Meta_2.__call__() returning new instance."
return instance
class Class_2(object):
__metaclass__ = Meta_2
def __new__(cls, *args, **kwargs):
print "Class_2.__new__() before creating instance."
instance = super(Class_2, cls).__new__(cls)
print "Class_2.__new__() returning instance."
return instance
def __init__(self, *args, **kwargs):
print "entering Class_2.__init__() for initialization."
super(Class_2, self).__init__()
print "exiting Class_2.__init__()."
Let's observe what happens when repeatedly trying to create an object of type Class_2
a = Class_2()
# Meta_2.__call__() before creating an instance.
# Class_2.__new__() before creating instance.
# Class_2.__new__() returning instance.
# entering Class_2.__init__() for initialization.
# exiting Class_2.__init__().
# Meta_2.__call__() returning new instance.
b = Class_2()
# <class '__main__.Class_2'> singleton returning from Meta_2.__call__(), skipping creation of new instance.
c = Class_2()
# <class '__main__.Class_2'> singleton returning from Meta_2.__call__(), skipping creation of new instance.
a is b is c # True

A metaclass is a class that tells how (some) other class should be created.
This is a case where I saw metaclass as a solution to my problem:
I had a really complicated problem, that probably could have been solved differently, but I chose to solve it using a metaclass. Because of the complexity, it is one of the few modules I have written where the comments in the module surpass the amount of code that has been written. Here it is...
#!/usr/bin/env python
# Copyright (C) 2013-2014 Craig Phillips. All rights reserved.
# This requires some explaining. The point of this metaclass excercise is to
# create a static abstract class that is in one way or another, dormant until
# queried. I experimented with creating a singlton on import, but that did
# not quite behave how I wanted it to. See now here, we are creating a class
# called GsyncOptions, that on import, will do nothing except state that its
# class creator is GsyncOptionsType. This means, docopt doesn't parse any
# of the help document, nor does it start processing command line options.
# So importing this module becomes really efficient. The complicated bit
# comes from requiring the GsyncOptions class to be static. By that, I mean
# any property on it, may or may not exist, since they are not statically
# defined; so I can't simply just define the class with a whole bunch of
# properties that are #property #staticmethods.
#
# So here's how it works:
#
# Executing 'from libgsync.options import GsyncOptions' does nothing more
# than load up this module, define the Type and the Class and import them
# into the callers namespace. Simple.
#
# Invoking 'GsyncOptions.debug' for the first time, or any other property
# causes the __metaclass__ __getattr__ method to be called, since the class
# is not instantiated as a class instance yet. The __getattr__ method on
# the type then initialises the class (GsyncOptions) via the __initialiseClass
# method. This is the first and only time the class will actually have its
# dictionary statically populated. The docopt module is invoked to parse the
# usage document and generate command line options from it. These are then
# paired with their defaults and what's in sys.argv. After all that, we
# setup some dynamic properties that could not be defined by their name in
# the usage, before everything is then transplanted onto the actual class
# object (or static class GsyncOptions).
#
# Another piece of magic, is to allow command line options to be set in
# in their native form and be translated into argparse style properties.
#
# Finally, the GsyncListOptions class is actually where the options are
# stored. This only acts as a mechanism for storing options as lists, to
# allow aggregation of duplicate options or options that can be specified
# multiple times. The __getattr__ call hides this by default, returning the
# last item in a property's list. However, if the entire list is required,
# calling the 'list()' method on the GsyncOptions class, returns a reference
# to the GsyncListOptions class, which contains all of the same properties
# but as lists and without the duplication of having them as both lists and
# static singlton values.
#
# So this actually means that GsyncOptions is actually a static proxy class...
#
# ...And all this is neatly hidden within a closure for safe keeping.
def GetGsyncOptionsType():
class GsyncListOptions(object):
__initialised = False
class GsyncOptionsType(type):
def __initialiseClass(cls):
if GsyncListOptions._GsyncListOptions__initialised: return
from docopt import docopt
from libgsync.options import doc
from libgsync import __version__
options = docopt(
doc.__doc__ % __version__,
version = __version__,
options_first = True
)
paths = options.pop('<path>', None)
setattr(cls, "destination_path", paths.pop() if paths else None)
setattr(cls, "source_paths", paths)
setattr(cls, "options", options)
for k, v in options.iteritems():
setattr(cls, k, v)
GsyncListOptions._GsyncListOptions__initialised = True
def list(cls):
return GsyncListOptions
def __getattr__(cls, name):
cls.__initialiseClass()
return getattr(GsyncListOptions, name)[-1]
def __setattr__(cls, name, value):
# Substitut option names: --an-option-name for an_option_name
import re
name = re.sub(r'^__', "", re.sub(r'-', "_", name))
listvalue = []
# Ensure value is converted to a list type for GsyncListOptions
if isinstance(value, list):
if value:
listvalue = [] + value
else:
listvalue = [ None ]
else:
listvalue = [ value ]
type.__setattr__(GsyncListOptions, name, listvalue)
# Cleanup this module to prevent tinkering.
import sys
module = sys.modules[__name__]
del module.__dict__['GetGsyncOptionsType']
return GsyncOptionsType
# Our singlton abstract proxy class.
class GsyncOptions(object):
__metaclass__ = GetGsyncOptionsType()

The tl;dr version
The type(obj) function gets you the type of an object.
The type() of a class is its metaclass.
To use a metaclass:
class Foo(object):
__metaclass__ = MyMetaClass
type is its own metaclass. The class of a class is a metaclass-- the body of a class is the arguments passed to the metaclass that is used to construct the class.
Here you can read about how to use metaclasses to customize class construction.

type is actually a metaclass -- a class that creates another classes.
Most metaclass are the subclasses of type. The metaclass receives the new class as its first argument and provide access to class object with details as mentioned below:
>>> class MetaClass(type):
... def __init__(cls, name, bases, attrs):
... print ('class name: %s' %name )
... print ('Defining class %s' %cls)
... print('Bases %s: ' %bases)
... print('Attributes')
... for (name, value) in attrs.items():
... print ('%s :%r' %(name, value))
...
>>> class NewClass(object, metaclass=MetaClass):
... get_choch='dairy'
...
class name: NewClass
Bases <class 'object'>:
Defining class <class 'NewClass'>
get_choch :'dairy'
__module__ :'builtins'
__qualname__ :'NewClass'
Note:
Notice that the class was not instantiated at any time; the simple act of creating the class triggered execution of the metaclass.

Python classes are themselves objects - as in instance - of their meta-class.
The default metaclass, which is applied when when you determine classes as:
class foo:
...
meta class are used to apply some rule to an entire set of classes. For example, suppose you're building an ORM to access a database, and you want records from each table to be of a class mapped to that table (based on fields, business rules, etc..,), a possible use of metaclass is for instance, connection pool logic, which is share by all classes of record from all tables. Another use is logic to to support foreign keys, which involves multiple classes of records.
when you define metaclass, you subclass type, and can overrided the following magic methods to insert your logic.
class somemeta(type):
__new__(mcs, name, bases, clsdict):
"""
mcs: is the base metaclass, in this case type.
name: name of the new class, as provided by the user.
bases: tuple of base classes
clsdict: a dictionary containing all methods and attributes defined on class
you must return a class object by invoking the __new__ constructor on the base metaclass.
ie:
return type.__call__(mcs, name, bases, clsdict).
in the following case:
class foo(baseclass):
__metaclass__ = somemeta
an_attr = 12
def bar(self):
...
#classmethod
def foo(cls):
...
arguments would be : ( somemeta, "foo", (baseclass, baseofbase,..., object), {"an_attr":12, "bar": <function>, "foo": <bound class method>}
you can modify any of these values before passing on to type
"""
return type.__call__(mcs, name, bases, clsdict)
def __init__(self, name, bases, clsdict):
"""
called after type has been created. unlike in standard classes, __init__ method cannot modify the instance (cls) - and should be used for class validaton.
"""
pass
def __prepare__():
"""
returns a dict or something that can be used as a namespace.
the type will then attach methods and attributes from class definition to it.
call order :
somemeta.__new__ -> type.__new__ -> type.__init__ -> somemeta.__init__
"""
return dict()
def mymethod(cls):
""" works like a classmethod, but for class objects. Also, my method will not be visible to instances of cls.
"""
pass
anyhow, those two are the most commonly used hooks. metaclassing is powerful, and above is nowhere near and exhaustive list of uses for metaclassing.

The type() function can return the type of an object or create a new type,
for example, we can create a Hi class with the type() function and do not need to use this way with class Hi(object):
def func(self, name='mike'):
print('Hi, %s.' % name)
Hi = type('Hi', (object,), dict(hi=func))
h = Hi()
h.hi()
Hi, mike.
type(Hi)
type
type(h)
__main__.Hi
In addition to using type() to create classes dynamically, you can control creation behavior of class and use metaclass.
According to the Python object model, the class is the object, so the class must be an instance of another certain class.
By default, a Python class is instance of the type class. That is, type is metaclass of most of the built-in classes and metaclass of user-defined classes.
class ListMetaclass(type):
def __new__(cls, name, bases, attrs):
attrs['add'] = lambda self, value: self.append(value)
return type.__new__(cls, name, bases, attrs)
class CustomList(list, metaclass=ListMetaclass):
pass
lst = CustomList()
lst.add('custom_list_1')
lst.add('custom_list_2')
lst
['custom_list_1', 'custom_list_2']
Magic will take effect when we passed keyword arguments in metaclass, it indicates the Python interpreter to create the CustomList through ListMetaclass. new (), at this point, we can modify the class definition, for example, and add a new method and then return the revised definition.

In addition to the published answers I can say that a metaclass defines the behaviour for a class. So, you can explicitly set your metaclass. Whenever Python gets a keyword class then it starts searching for the metaclass. If it's not found – the default metaclass type is used to create the class's object. Using the __metaclass__ attribute, you can set metaclass of your class:
class MyClass:
__metaclass__ = type
# write here other method
# write here one more method
print(MyClass.__metaclass__)
It'll produce the output like this:
class 'type'
And, of course, you can create your own metaclass to define the behaviour of any class that are created using your class.
For doing that, your default metaclass type class must be inherited as this is the main metaclass:
class MyMetaClass(type):
__metaclass__ = type
# you can write here any behaviour you want
class MyTestClass:
__metaclass__ = MyMetaClass
Obj = MyTestClass()
print(Obj.__metaclass__)
print(MyMetaClass.__metaclass__)
The output will be:
class '__main__.MyMetaClass'
class 'type'

Note that in python 3.6 a new dunder method __init_subclass__(cls, **kwargs) was introduced to replace a lot of common use cases for metaclasses. Is is called when a subclass of the defining class is created. See python docs.

Here's another example of what it can be used for:
You can use the metaclass to change the function of its instance (the class).
class MetaMemberControl(type):
__slots__ = ()
#classmethod
def __prepare__(mcs, f_cls_name, f_cls_parents, # f_cls means: future class
meta_args=None, meta_options=None): # meta_args and meta_options is not necessarily needed, just so you know.
f_cls_attr = dict()
if not "do something or if you want to define your cool stuff of dict...":
return dict(make_your_special_dict=None)
else:
return f_cls_attr
def __new__(mcs, f_cls_name, f_cls_parents, f_cls_attr,
meta_args=None, meta_options=None):
original_getattr = f_cls_attr.get('__getattribute__')
original_setattr = f_cls_attr.get('__setattr__')
def init_getattr(self, item):
if not item.startswith('_'): # you can set break points at here
alias_name = '_' + item
if alias_name in f_cls_attr['__slots__']:
item = alias_name
if original_getattr is not None:
return original_getattr(self, item)
else:
return super(eval(f_cls_name), self).__getattribute__(item)
def init_setattr(self, key, value):
if not key.startswith('_') and ('_' + key) in f_cls_attr['__slots__']:
raise AttributeError(f"you can't modify private members:_{key}")
if original_setattr is not None:
original_setattr(self, key, value)
else:
super(eval(f_cls_name), self).__setattr__(key, value)
f_cls_attr['__getattribute__'] = init_getattr
f_cls_attr['__setattr__'] = init_setattr
cls = super().__new__(mcs, f_cls_name, f_cls_parents, f_cls_attr)
return cls
class Human(metaclass=MetaMemberControl):
__slots__ = ('_age', '_name')
def __init__(self, name, age):
self._name = name
self._age = age
def __getattribute__(self, item):
"""
is just for IDE recognize.
"""
return super().__getattribute__(item)
""" with MetaMemberControl then you don't have to write as following
#property
def name(self):
return self._name
#property
def age(self):
return self._age
"""
def test_demo():
human = Human('Carson', 27)
# human.age = 18 # you can't modify private members:_age <-- this is defined by yourself.
# human.k = 18 # 'Human' object has no attribute 'k' <-- system error.
age1 = human._age # It's OK, although the IDE will show some warnings. (Access to a protected member _age of a class)
age2 = human.age # It's OK! see below:
"""
if you do not define `__getattribute__` at the class of Human,
the IDE will show you: Unresolved attribute reference 'age' for class 'Human'
but it's ok on running since the MetaMemberControl will help you.
"""
if __name__ == '__main__':
test_demo()
The metaclass is powerful, there are many things (such as monkey magic) you can do with it, but be careful this may only be known to you.

The top answer is correct.
But readers may be coming here searching answers about similarly named inner classes. They are present in popular libraries, such as Django and WTForms.
As DavidW points out in the comments beneath this answer, these are library-specific features and are not to be confused with the advanced, unrelated Python language feature with a similar name.
Rather, these are namespaces within classes' dicts. They are constructed using inner classes for sake of readability.
In this example special field, abstract is visibly separate from fields of Author model.
from django.db import models
class Author(models.Model):
name = models.CharField(max_length=50)
email = models.EmailField()
class Meta:
abstract = True
Another example is from the documentation for WTForms:
from wtforms.form import Form
from wtforms.csrf.session import SessionCSRF
from wtforms.fields import StringField
class MyBaseForm(Form):
class Meta:
csrf = True
csrf_class = SessionCSRF
name = StringField("name")
This syntax does not get special treatment in the python programming language. Meta is not a keyword here, and does not trigger metaclass behavior. Rather, third-party library code in packages like Django and WTForms reads this property in the constructors of certain classes, and elsewhere.
The presence of these declarations modifies the behavior of the classes that have these declarations. For example, WTForms reads self.Meta.csrf to determine if the form needs a csrf field.

In object-oriented programming, a metaclass is a class whose instances are classes. Just as an ordinary class defines the behavior of certain objects, a metaclass defines the behavior of certain class and their instances
The term metaclass simply means something used to create classes. In other words, it is the class of a class. The metaclass is used to create the class so like the object being an instance of a class, a class is an instance of a metaclass. In python classes are also considered objects.

A class, in Python, is an object, and just like any other object, it is an instance of "something". This "something" is what is termed as a Metaclass. This metaclass is a special type of class that creates other class's objects. Hence, metaclass is responsible for making new classes. This allows the programmer to customize the way classes are generated.
To create a metaclass, overriding of new() and init() methods is usually done. new() can be overridden to change the way objects are created, while init() can be overridden to change the way of initializing the object. Metaclass can be created by a number of ways. One of the ways is to use type() function. type() function, when called with 3 parameters, creates a metaclass. The parameters are :-
Class Name
Tuple having base classes inherited by class
A dictionary having all class methods and class variables
Another way of creating a metaclass comprises of 'metaclass' keyword. Define the metaclass as a simple class. In the parameters of inherited class, pass metaclass=metaclass_name
Metaclass can be specifically used in the following situations :-
when a particular effect has to be applied to all the subclasses
Automatic change of class (on creation) is required
By API developers

I saw an interesting use case for metaclasses in a package called classutilities. It checks if all class variables are in upper case format (it is convenient to have unified logic for configuration classes), and checks if there are no instance level methods in class.
Another interesting example for metaclases was deactivation of unittests based on complex conditions (checking values of multiple environmental variables).

In Python, a metaclass is a subclass of a subclass that determines how a subclass behaves. A class is an instance of another metaclass. In Python, a class specifies how the class's instance will behave.
Since metaclasses are in charge of class generation, you can write your own custom metaclasses to change how classes are created by performing additional actions or injecting code. Custom metaclasses aren't always important, but they can be.

look this:
Python 3.10.0rc2 (tags/v3.10.0rc2:839d789, Sep 7 2021, 18:51:45) [MSC v.1929 64 bit (AMD64)] on win32
Type "help", "copyright", "credits" or "license" for more information.
>>> class Object:
... pass
...
>>> class Meta(type):
... test = 'Worked!!!'
... def __repr__(self):
... return 'This is "Meta" metaclass'
...
>>> class ObjectWithMetaClass(metaclass=Meta):
... pass
...
>>> Object or type(Object())
<class '__main__.Object'>
>>> ObjectWithMetaClass or type(ObjectWithMetaClass())
This is "Meta" metaclass
>>> Object.test
AttributeError: ...
>>> ObjectWithMetaClass.test
'Worked!!!'
>>> type(Object)
<class 'type'>
>>> type(ObjectWithMetaClass)
<class '__main__.Meta'>
>>> type(type(ObjectWithMetaClass))
<class 'type'>
>>> Object.__bases__
(<class 'object'>,)
>>> ObjectWithMetaClass.__bases__
(<class 'object'>,)
>>> type(ObjectWithMetaClass).__bases__
(<class 'type'>,)
>>> Object.__mro__
(<class '__main__.Object'>, <class 'object'>)
>>> ObjectWithMetaClass.__mro__
(This is "Meta" metaclass, <class 'object'>)
>>>
In other words, when an object was not created (type of object), we looking MetaClass.

i want to add a little on why type.__new__() over type()
first, take a look at these classes
In [1]: class MyMeta(type):
...: def __new__(cls, cls_name, bases, attrs):
...: print(cls, cls_name, bases, attrs)
...: return super().__new__(cls, cls_name, bases, attrs)
...:
In [2]: class AClass(metaclass=MyMeta):
...: pass
...:
<class '__main__.MyMeta'> AClass () {'__module__': '__main__', '__qualname__': 'AClass'}
In [3]: class BClass:
...: pass
...:
In [4]: AClass.__class__
Out[4]: __main__.MyMeta
In [5]: BClass.__class__
Out[5]: type
In [6]: class SubAClass(AClass):
...: pass
...:
<class '__main__.MyMeta'> SubAClass (<class '__main__.AClass'>,) {'__module__': '__main__', '__qualname__': 'SubAClass'}
when we were trying to create SubAClass a subclass of AClass, Python would take a look at the metaclass we designated to be used to create SubAClass
and in this case, we did not pass a metaclass for SubAClass, so Python got a None for metaclass.
and Python would try to pick up the first base class of SubAClass, which is AClass, has a metaclass, which is MyMeta, then it would call MyMeta.__new__ to create SubAClass
for simplicity, we can say
type.__new__ just assigned our metaclass to the parent.__class__(AClass.__class__).
and when a subclass was defined(SubAClass) by class statement, Python
would just use the parent.__class__ to create that subclass unless
we've manually passed a metaclass to our subclass
apparently, if you called type() instead of type.__new__, the class
would have type as its __class__ attr.
that means AClass would be equivalent of BClass, both of them have type
as their __class__ attr
how the searching of metaclass works in C code?
it works pretty much like what we've just mentioned
the function builtin___build_class__ would be called when you defined a class
and code is just so straightforward
static PyObject *
builtin___build_class__(PyObject *self, PyObject *const *args, Py_ssize_t nargs,
PyObject *kwnames){
if (meta == NULL) {
/* if there are no bases, use type: */
if (PyTuple_GET_SIZE(bases) == 0) {
meta = (PyObject *) (&PyType_Type);
}
/* else get the type of the first base */
else {
PyObject *base0 = PyTuple_GET_ITEM(bases, 0);
meta = (PyObject *)Py_TYPE(base0);
}
Py_INCREF(meta);
isclass = 1; /* meta is really a class */
}
PyObject *margs[3] = {name, bases, ns};
cls = PyObject_VectorcallDict(meta, margs, 3, mkw);
}
basically, meta = (PyObject *)Py_TYPE(base0); is everything we want to know
it can be translated to be
meta = MyMeta = AClass.__class__ = Py_TYPE(AClass)

In Python or in any other language we have a type for every variable or object we declare. For getting type of anything(variable,object,etc.) in Python we can use type() function.
Bypassing the metaclass keyword in the class definition we can customize the class creation process.
class meta(type):
pass
class baseclass(metaclass=meta): # This is Mestaclass
pass
class derivedclass(baseclass):
pass
print(type(meta))
print(type(baseclass))
print(type(derivedclass))
When defining a new class if no metaclass is defined the default type metaclass is used. If a given metaclass is not the object(instance) of type(), in that situation it is used directly as a metaclass.

Python: Assigning staticmethod to class variable gives error

I want to assign a static method to a class variable in Python, and below is what my code looks like.
class Klass:
classVariable = None
#staticmethod
def method():
print "method called"
Klass.classVariable = Klass.method
Klass.method()
Klass.classVariable()
This gave me an error at the last line,
TypeError: unbound method method() must be called with Klass instance as first argument (got nothing instead).
But when I change the static method to class method it works. Can anyone give me any idea of why this is the case?

Backstory (descriptor protocol)
First, we need to know a little about python descriptors...
For this answer, it should be enough to know the following:
Functions are descriptors.
Binding behavior of methods (i.e. how a method knows what self to pass) is implemented via the function's __get__ method and the built-in descriptor protocol.
When you put a descriptor foo on a class, accessing the descriptor actually calls the .__get__ method. (This is really just a generalization of statement 2)
In other words:
class Foo(object):
val = some_descriptor
When I do:
result = Foo.val
Python actually does:
Foo.val.__get__(None, Foo)
When I do:
f = Foo()
f.val
python does:
f = Foo()
type(f).val.__get__(f, type(f))
Now the good stuff.
It looks like (on python2.x), staticmethod is implemented such that it's __get__ method returns a regular function. You can see this by printing the type of Klass.method:
print type(Klass.method) # <type 'function'>
So what we've learned is that the method returned by Klass.method.__get__ is just a regular function.
When you put that regular function onto a class, it's __get__ method returns an instancemethod (which expects a self argument). This isn't surprising ... We do it all the time:
class Foo(object):
def bar(self):
print self
Is no different to python than:
def bar(self):
print self
class Foo(object):
pass
Foo.bar = bar
except that the first version is a lot easier to read and doesn't clutter your module namespace.
So now we've explained how your staticmethod turned into an instance method. Is there anything we can do about it?
Solution
When you put the method onto the class, designate it as a staticmethod again and it will work out Ok.
class Klass(object): # inheriting from object is a good idea.
classVariable = None
#staticmethod
def method():
print("method called")
Klass.classVariable = staticmethod(Klass.method) # Note extra staticmethod
Klass.method()
Klass.classVariable()
Appendix -- Re-implementation of #staticmethod
If you're a little but curious how you might implement staticmethod to not have this problem -- Here's an example:
class StaticMethod(object):
def __init__(self, fn):
self.fn = fn
def __get__(self, inst, cls):
return self
def __call__(self, *args, **kwargs):
return self.fn(*args, **kwargs)
class Klass(object):
classVariable = None
#StaticMethod
def method():
print("method called")
Klass.classVariable = Klass.method
Klass.method()
Klass.classVariable()
Klass().method()
Klass().classVariable()
The trick here is that my __get__ doesn't return a function. It returns itself. When you put it on a different class (or the same class), it's __get__ will still just return itself. Since it is returning itself from __get__, it needs to pretend to be a function (so it can be called after "__gotten__") so I implement a custom __call__ method to do the right thing (pass through to the delegate function and return the result).
Please note, I'm not advocating that you use this StaticMethod instead of staticmethod. It'll be less efficient and not as introspectible (and probably confusing for your code readers). This is only for educational purposes.

Can I get a reference to a Python property?

If I have this:
class foo(object):
#property
def bar(self):
return 0
f = foo()
How do I get a reference to f.bar without actually invoking the method, if this is even possible?
Edited to add: What I want to do is write a function that iterates over the members of f and does something with them (what is not important). Properties are tripping me up because merely naming them in getattr() invokes their __get__() method.

get_dict_attr (below) looks up attr in a given object's __dict__, and returns the associated value if its there. If attr is not a key in that __dict__, the object's MRO's __dict__s are searched. If the key is not found, an AttributeError is raised.
def get_dict_attr(obj, attr):
for obj in [obj] + obj.__class__.mro():
if attr in obj.__dict__:
return obj.__dict__[attr]
raise AttributeError
For example,
class Foo(object):
x=1
def bar(self):
pass
#property
def baz(self):
return 0
foo=Foo()
print(get_dict_attr(foo,'x'))
# 1
print(get_dict_attr(foo,'bar'))
# <unbound method Foo.bar>
print(get_dict_attr(foo,'baz'))
# <property object at 0xb77c0dc4>
print(get_dict_attr(foo,'y'))
# AttributeError
Note that this is very different than the normal rules of attribute lookup.
For one thing, data-descriptors in obj.__class__.__dict__ (descriptors with both __get__ and __set__ methods) normally have precedence over values in obj.__dict__. In get_dict_attr, obj.__dict__ has precedence.
get_dict_attr does not try calling __getattr__.
Finally, get_dict_attr will only work with objects obj which are instances of new-style classes.
Nevertheless, I hope it is of some help.
class Foo(object):
#property
def bar(self):
return 0
f = Foo()
This references the property bar:
print(Foo.bar)
# <property object at 0xb76d1d9c>
You see bar is a key in Foo.__dict__:
print(Foo.__dict__['bar'])
# <property object at 0xb775dbbc>
All properties are descriptors, which implies it has a __get__ method:
print(Foo.bar.__get__)
# <method-wrapper '__get__' of property object at 0xb76d7d74>
You can call the method by passing the object f, and the class of f as arguments:
print(Foo.bar.__get__(f,Foo))
# 0
I am fond of the following diagram. Vertical lines show the relationship between an object and the object's class.
When you have this situation:
Foo B
| Foo.__dict__={'bar':b} | B.__dict__={'__get__':...}
| \ |
f `--------> b
f.bar causes b.__get__(f,Foo) to be called.
This is explained in detail here.

Short answer:
Properties return its self when they called from class: MyClass.my_prop
Also, they have fields that contain a link to the actual methods: fget, fset and fdel.
Description:
So, my_class.my_prop (where my_class = MyClass()) returns the value, but MyClass.my_prop returns the property object and MyClass.my_prop.fget returns the getter method of this property. The self is not linked to it, so it should be populated during the call: MyClass.my_prop.fget(my_class)
Example:
class MyClass:
my_prop = property(lambda self: 'get', lambda self, x: print('set', x))
setter = MyClass.my_prop.fset
getter = MyClass.my_prop.fget
my_class = MyClass()
setter(my_class, 5) # equals my_class.my_prop = 5
print(getter(my_class)) # equals print(my_class.my_prop)

I ran into a similar situation to this and none of the other answers helped me, so here's an alternate approach.
My situation was slightly different, as instead of just having a #property, I was mixing in a custom decorator that adds attributes to the wrapped method. So eg normally I'd have something like this:
class Foo:
#my_decorator
def bar(self):
return do_stuff() # Pretend this is non-trivial
My #my_decorator would then give me access to self.bar.my_attribute that had some data I need to access sometimes. This works great for functions and methods, but I hit a snag when trying to mix it with #property because the property hides the reference to the method (self.bar.my_attribute fails because self.bar returns the return value of the method, which doesn't have the my_attribute that I want).
I'd use #property as the outer decorator like so:
class Foo:
#property
#my_decorator
def bar(self):
return do_stuff() # Pretend this is non-trivial
And then the solution is to access my_attribute as Foo.bar.fget.my_attribute (the important detail here is that accessing the property from the class returns the property object, whereas accessing the property from an instance just calls the method and returns the value, without access to the reference to the original method).
TL;DR: If you need a reference to the method that provides your #property, you need to use YourClassName.your_property.fget.

In your case,
class Foo(object):
#property
def bar(self):
return 0
f = Foo()
You can access the property through the class Foo. These are all equivalent:
Foo.bar
Foo.__dict__['bar']
f.__class__.__dict__['bar']
=> <property at 0x4920c28>
As you can see, this is a property instance. On a property you have a method called fget which returns the 'getter' value.
isinstance(Foo.bar, property)
=> True
Foo.bar.fget
=> <function Foo.bar at 0x0000014DC0CF21F0>
You can call this function (an instance method) directly.
Foo.bar.fget(f)
=> 0
Note: you have to supply the object f (the self argument) to fget yourself, because when accessed through the class, the class does not know the instance f yet. In other words, instance method Foo.bar.fget is not yet 'bound' to f.

One thing you should know is that data descriptors (i.e., properties) only work when they are applied to (new-style) classes (see http://docs.python.org/reference/datamodel.html#implementing-descriptors). Copying them to an object will not create the property on that object. You need to copy them to a (new-style) class to take effect.

Why not just lambdify it?
bar_getter = lambda: f.bar
Done.

I'm going to say ignore the other answers because they are bad.
You can get a reference to the property simply by foo.bar
As for what you're trying to do iterating over the members of f, see below for specifics.
The long answer: what you have to understand are that methods in Python do not belong to instances, but are attributes of the class object. For example,
class Foo:
def bar(self):
pass
foo = Foo()
if you call foo.bar(), Python first checks to see if foo has bar (it doesn't), then it checks if Foo has bar, which it does. Python then "binds" foo.bar to Foo.bar(foo), and calls that.
The same is true for properties. I don't know the exact process (and I imagine it differs between implementations), but essentially Python looks for foo.bar, then Foo.bar, sees that it is a property, so evaluates its getter and returns that.

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