@Oddthinking's answer is not wrong, but I think it misses the real, practical reason Python has ABCs in a world of duck-typing.

Abstract methods are neat, but in my opinion they don't really fill any use-cases not already covered by duck typing. Abstract base classes' real power lies in the way they allow you to customise the behaviour of isinstance and issubclass. (__subclasshook__ is basically a friendlier API on top of Python's __instancecheck__ and __subclasscheck__ hooks.) Adapting built-in constructs to work on custom types is very much part of Python's philosophy.

Python's source code is exemplary. Here is how collections.Container is defined in the standard library (at time of writing):

class Container(metaclass=ABCMeta):
    __slots__ = ()

    @abstractmethod
    def __contains__(self, x):
        return False

    @classmethod
    def __subclasshook__(cls, C):
        if cls is Container:
            if any("__contains__" in B.__dict__ for B in C.__mro__):
                return True
        return NotImplemented

This definition of __subclasshook__ says that any class with a __contains__ attribute is considered to be a subclass of Container, even if it doesn't subclass it directly. So I can write this:

class ContainAllTheThings(object):
    def __contains__(self, item):
        return True

>>> issubclass(ContainAllTheThings, collections.Container)
True
>>> isinstance(ContainAllTheThings(), collections.Container)
True

In other words, if you implement the right interface, you're a subclass! ABCs provide a formal way to define interfaces in Python, while staying true to the spirit of duck-typing. Besides, this works in a way that honours the Open-Closed Principle.

Python's object model looks superficially similar to that of a more "traditional" OO system (by which I mean Java*) - we got yer classes, yer objects, yer methods - but when you scratch the surface you'll find something far richer and more flexible. Likewise, Python's notion of abstract base classes may be recognisable to a Java developer, but in practice they are intended for a very different purpose.

I sometimes find myself writing polymorphic functions that can act on a single item or a collection of items, and I find isinstance(x, collections.Iterable) to be much more readable than hasattr(x, '__iter__') or an equivalent try...except block. (If you didn't know Python, which of those three would make the intention of the code clearest?)

That said, I find that I rarely need to write my own ABC and I typically discover the need for one through refactoring. If I see a polymorphic function making a lot of attribute checks, or lots of functions making the same attribute checks, that smell suggests the existence of an ABC waiting to be extracted.

*without getting into the debate over whether Java is a "traditional" OO system...

Addendum: Even though an abstract base class can override the behaviour of isinstance and issubclass, it still doesn't enter the MRO of the virtual subclass. This is a potential pitfall for clients: not every object for which isinstance(x, MyABC) == True has the methods defined on MyABC.

class MyABC(metaclass=abc.ABCMeta):
    def abc_method(self):
        pass
    @classmethod
    def __subclasshook__(cls, C):
        return True

class C(object):
    pass

# typical client code
c = C()
if isinstance(c, MyABC):  # will be true
    c.abc_method()  # raises AttributeError

Unfortunately this one of those "just don't do that" traps (of which Python has relatively few!): avoid defining ABCs with both a __subclasshook__ and non-abstract methods. Moreover, you should make your definition of __subclasshook__ consistent with the set of abstract methods your ABC defines.

Use the abc module to create abstract classes. Use the abstractmethod decorator to declare a method abstract, and declare a class abstract using one of three ways, depending upon your Python version.

In Python 3.4 and above, you can inherit from ABC. In earlier versions of Python, you need to specify your class's metaclass as ABCMeta. Specifying the metaclass has different syntax in Python 3 and Python 2. The three possibilities are shown below:

# Python 3.4+
from abc import ABC, abstractmethod
class Abstract(ABC):
    @abstractmethod
    def foo(self):
        pass

# Python 3.0+
from abc import ABCMeta, abstractmethod
class Abstract(metaclass=ABCMeta):
    @abstractmethod
    def foo(self):
        pass

# Python 2
from abc import ABCMeta, abstractmethod
class Abstract:
    __metaclass__ = ABCMeta

    @abstractmethod
    def foo(self):
        pass

Whichever way you use, you won't be able to instantiate an abstract class that has abstract methods, but will be able to instantiate a subclass that provides concrete definitions of those methods:

>>> Abstract()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: Can't instantiate abstract class Abstract with abstract methods foo
>>> class StillAbstract(Abstract):
...     pass
... 
>>> StillAbstract()
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
TypeError: Can't instantiate abstract class StillAbstract with abstract methods foo
>>> class Concrete(Abstract):
...     def foo(self):
...         print('Hello, World')
... 
>>> Concrete()
<__main__.Concrete object at 0x7fc935d28898>