How to apply a "mixin" class to an old-style base class

How to apply a "mixin" class to an old-style base class - python

I've written a mixin class that's designed to be layered on top of a new-style class, for example via
class MixedClass(MixinClass, BaseClass):
pass
What's the smoothest way to apply this mixin to an old-style class? It is using a call to super in its __init__ method, so this will presumably (?) have to change, but otherwise I'd like to make as few changes as possible to MixinClass. I should be able to derive a subclass that makes the necessary changes.
I'm considering using a class decorator on top of a class derived from BaseClass, e.g.
#old_style_mix(MixinOldSchoolRemix)
class MixedWithOldStyleClass(OldStyleClass)
where MixinOldSchoolRemix is derived from MixinClass and just re-implements methods that use super to instead use a class variable that contains the class it is mixed with, in this case OldStyleClass. This class variable would be set by old_style_mix as part of the mixing process.
old_style_mix would just update the class dictionary of e.g. MixedWithOldStyleClass with the contents of the mixin class (e.g. MixinOldSchoolRemix) dictionary.
Is this a reasonable strategy? Is there a better way? It seems like this would be a common problem, given that there are numerous available modules still using old-style classes.

This class variable would be set by
old_style_mix as part of the mixing
process.
...I assume you mean: "...on the class it's decorating..." as opposed to "on the class that is its argument" (the latter would be a disaster).
old_style_mix would just update the
class dictionary of e.g.
MixedWithOldStyleClass with the
contents of the mixin class (e.g.
MixinOldSchoolRemix) dictionary.
No good -- the information that MixinOldSchoolRemix derives from MixinClass, for example, is not in the former's dictionary. So, old_style_mix must take a different strategy: for example, build a new class (which I believe has to be a new-style one, because old-style ones do not accept new-style ones as __bases__) with the appropriate sequence of bases, as well as a suitably tweaked dictionary.
Is this a reasonable strategy?
With the above provisos.
It seems like this would be a common
problem, given that there are numerous
available modules still using
old-style classes.
...but mixins with classes that were never designed to take mixins are definitely not a common design pattern, so the problem isn't common at all (I don't remember seeing it even once in the many years since new-style classes were born, and I was actively consulting, teaching advanced classes, and helping people with Python problems for many of those years, as well as doing a lot of software development myself -- I do tend to have encountered any "reasonably common" problem that people may have with features which have been around long enough!-).
Here's example code for what your class decorator could do (if you prefer to have it in a class decorator rather than directly inline...):
>>> class Mixo(object):
... def foo(self):
... print 'Mixo.foo'
... self.thesuper.foo(self)
...
>>> class Old:
... def foo(self):
... print 'Old.foo'
...
>>> class Mixed(Mixo, Old):
... thesuper = Old
...
>>> m = Mixed()
>>> m.foo()
Mixo.foo
Old.foo
If you want to build Mixed under the assumed name/binding of Mixo in your decorator, you could do it with a call to type, or by setting Mixed.__name__ = cls.__name__ (where cls is the class you're decorating). I think the latter approach is simpler (warning, untested code -- the above interactive shell session is a real one, but I have not tested the following code):
def oldstylemix(mixin):
def makemix(cls):
class Mixed(mixin, cls):
thesuper = cls
Mixed.__name__ = cls.__name__
return Mixed
return makemix

Related

What is the difference between a mixin classes and standard multiple inheritance in python [duplicate]

In Programming Python, Mark Lutz mentions the term mixin. I am from a C/C++/C# background and I have not heard the term before. What is a mixin?
Reading between the lines of this example (which I have linked to because it is quite long), I am presuming it is a case of using multiple inheritance to extend a class as opposed to proper subclassing. Is this right?
Why would I want to do that rather than put the new functionality into a subclass? For that matter, why would a mixin/multiple inheritance approach be better than using composition?
What separates a mixin from multiple inheritance? Is it just a matter of semantics?

A mixin is a special kind of multiple inheritance. There are two main situations where mixins are used:
You want to provide a lot of optional features for a class.
You want to use one particular feature in a lot of different classes.
For an example of number one, consider werkzeug's request and response system. I can make a plain old request object by saying:
from werkzeug import BaseRequest
class Request(BaseRequest):
pass
If I want to add accept header support, I would make that
from werkzeug import BaseRequest, AcceptMixin
class Request(AcceptMixin, BaseRequest):
pass
If I wanted to make a request object that supports accept headers, etags, authentication, and user agent support, I could do this:
from werkzeug import BaseRequest, AcceptMixin, ETagRequestMixin, UserAgentMixin, AuthenticationMixin
class Request(AcceptMixin, ETagRequestMixin, UserAgentMixin, AuthenticationMixin, BaseRequest):
pass
The difference is subtle, but in the above examples, the mixin classes weren't made to stand on their own. In more traditional multiple inheritance, the AuthenticationMixin (for example) would probably be something more like Authenticator. That is, the class would probably be designed to stand on its own.

First, you should note that mixins only exist in multiple-inheritance languages. You can't do a mixin in Java or C#.
Basically, a mixin is a stand-alone base type that provides limited functionality and polymorphic resonance for a child class. If you're thinking in C#, think of an interface that you don't have to actually implement because it's already implemented; you just inherit from it and benefit from its functionality.
Mixins are typically narrow in scope and not meant to be extended.
[edit -- as to why:]
I suppose I should address why, since you asked. The big benefit is that you don't have to do it yourself over and over again. In C#, the biggest place where a mixin could benefit might be from the Disposal pattern. Whenever you implement IDisposable, you almost always want to follow the same pattern, but you end up writing and re-writing the same basic code with minor variations. If there were an extendable Disposal mixin, you could save yourself a lot of extra typing.
[edit 2 -- to answer your other questions]
What separates a mixin from multiple inheritance? Is it just a matter of semantics?
Yes. The difference between a mixin and standard multiple inheritance is just a matter of semantics; a class that has multiple inheritance might utilize a mixin as part of that multiple inheritance.
The point of a mixin is to create a type that can be "mixed in" to any other type via inheritance without affecting the inheriting type while still offering some beneficial functionality for that type.
Again, think of an interface that is already implemented.
I personally don't use mixins since I develop primarily in a language that doesn't support them, so I'm having a really difficult time coming up with a decent example that will just supply that "ahah!" moment for you. But I'll try again. I'm going to use an example that's contrived -- most languages already provide the feature in some way or another -- but that will, hopefully, explain how mixins are supposed to be created and used. Here goes:
Suppose you have a type that you want to be able to serialize to and from XML. You want the type to provide a "ToXML" method that returns a string containing an XML fragment with the data values of the type, and a "FromXML" that allows the type to reconstruct its data values from an XML fragment in a string. Again, this is a contrived example, so perhaps you use a file stream, or an XML Writer class from your language's runtime library... whatever. The point is that you want to serialize your object to XML and get a new object back from XML.
The other important point in this example is that you want to do this in a generic way. You don't want to have to implement a "ToXML" and "FromXML" method for every type that you want to serialize, you want some generic means of ensuring that your type will do this and it just works. You want code reuse.
If your language supported it, you could create the XmlSerializable mixin to do your work for you. This type would implement the ToXML and the FromXML methods. It would, using some mechanism that's not important to the example, be capable of gathering all the necessary data from any type that it's mixed in with to build the XML fragment returned by ToXML and it would be equally capable of restoring that data when FromXML is called.
And.. that's it. To use it, you would have any type that needs to be serialized to XML inherit from XmlSerializable. Whenever you needed to serialize or deserialize that type, you would simply call ToXML or FromXML. In fact, since XmlSerializable is a fully-fledged type and polymorphic, you could conceivably build a document serializer that doesn't know anything about your original type, accepting only, say, an array of XmlSerializable types.
Now imagine using this scenario for other things, like creating a mixin that ensures that every class that mixes it in logs every method call, or a mixin that provides transactionality to the type that mixes it in. The list can go on and on.
If you just think of a mixin as a small base type designed to add a small amount of functionality to a type without otherwise affecting that type, then you're golden.
Hopefully. :)

This answer aims to explain mixins with examples that are:
self-contained: short, with no need to know any libraries to understand the example.
in Python, not in other languages.
It is understandable that there were examples from other languages such as Ruby since the term is much more common in those languages, but this is a Python thread.
It shall also consider the controversial question:
Is multiple inheritance necessary or not to characterize a mixin?
Definitions
I have yet to see a citation from an "authoritative" source clearly saying what is a mixin in Python.
I have seen 2 possible definitions of a mixin (if they are to be considered as different from other similar concepts such as abstract base classes), and people don't entirely agree on which one is correct.
The consensus may vary between different languages.
Definition 1: no multiple inheritance
A mixin is a class such that some method of the class uses a method which is not defined in the class.
Therefore the class is not meant to be instantiated, but rather serve as a base class. Otherwise the instance would have methods that cannot be called without raising an exception.
A constraint which some sources add is that the class may not contain data, only methods, but I don't see why this is necessary. In practice however, many useful mixins don't have any data, and base classes without data are simpler to use.
A classic example is the implementation of all comparison operators from only <= and ==:
class ComparableMixin(object):
"""This class has methods which use `<=` and `==`,
but this class does NOT implement those methods."""
def __ne__(self, other):
return not (self == other)
def __lt__(self, other):
return self <= other and (self != other)
def __gt__(self, other):
return not self <= other
def __ge__(self, other):
return self == other or self > other
class Integer(ComparableMixin):
def __init__(self, i):
self.i = i
def __le__(self, other):
return self.i <= other.i
def __eq__(self, other):
return self.i == other.i
assert Integer(0) < Integer(1)
assert Integer(0) != Integer(1)
assert Integer(1) > Integer(0)
assert Integer(1) >= Integer(1)
# It is possible to instantiate a mixin:
o = ComparableMixin()
# but one of its methods raise an exception:
#o != o
This particular example could have been achieved via the functools.total_ordering() decorator, but the game here was to reinvent the wheel:
import functools
#functools.total_ordering
class Integer(object):
def __init__(self, i):
self.i = i
def __le__(self, other):
return self.i <= other.i
def __eq__(self, other):
return self.i == other.i
assert Integer(0) < Integer(1)
assert Integer(0) != Integer(1)
assert Integer(1) > Integer(0)
assert Integer(1) >= Integer(1)
Definition 2: multiple inheritance
A mixin is a design pattern in which some method of a base class uses a method it does not define, and that method is meant to be implemented by another base class, not by the derived like in Definition 1.
The term mixin class refers to base classes which are intended to be used in that design pattern (TODO those that use the method, or those that implement it?)
It is not easy to decide if a given class is a mixin or not: the method could be just implemented on the derived class, in which case we're back to Definition 1. You have to consider the author's intentions.
This pattern is interesting because it is possible to recombine functionalities with different choices of base classes:
class HasMethod1(object):
def method(self):
return 1
class HasMethod2(object):
def method(self):
return 2
class UsesMethod10(object):
def usesMethod(self):
return self.method() + 10
class UsesMethod20(object):
def usesMethod(self):
return self.method() + 20
class C1_10(HasMethod1, UsesMethod10): pass
class C1_20(HasMethod1, UsesMethod20): pass
class C2_10(HasMethod2, UsesMethod10): pass
class C2_20(HasMethod2, UsesMethod20): pass
assert C1_10().usesMethod() == 11
assert C1_20().usesMethod() == 21
assert C2_10().usesMethod() == 12
assert C2_20().usesMethod() == 22
# Nothing prevents implementing the method
# on the base class like in Definition 1:
class C3_10(UsesMethod10):
def method(self):
return 3
assert C3_10().usesMethod() == 13
Authoritative Python occurrences
At the official documentatiton for collections.abc the documentation explicitly uses the term Mixin Methods.
It states that if a class:
implements __next__
inherits from a single class Iterator
then the class gets an __iter__ mixin method for free.
Therefore at least on this point of the documentation, mixin does not not require multiple inheritance, and is coherent with Definition 1.
The documentation could of course be contradictory at different points, and other important Python libraries might be using the other definition in their documentation.
This page also uses the term Set mixin, which clearly suggests that classes like Set and Iterator can be called Mixin classes.
In other languages
Ruby: Clearly does not require multiple inheritance for mixin, as mentioned in major reference books such as Programming Ruby and The Ruby programming Language
C++: A virtual method that is set =0 is a pure virtual method.
Definition 1 coincides with the definition of an abstract class (a class that has a pure virtual method).
That class cannot be instantiated.
Definition 2 is possible with virtual inheritance: Multiple Inheritance from two derived classes

I think of them as a disciplined way of using multiple inheritance - because ultimately a mixin is just another python class that (might) follow the conventions about classes that are called mixins.
My understanding of the conventions that govern something you would call a Mixin are that a Mixin:
adds methods but not instance variables (class constants are OK)
only inherits from object (in Python)
That way it limits the potential complexity of multiple inheritance, and makes it reasonably easy to track the flow of your program by limiting where you have to look (compared to full multiple inheritance). They are similar to ruby modules.
If I want to add instance variables (with more flexibility than allowed for by single inheritance) then I tend to go for composition.
Having said that, I have seen classes called XYZMixin that do have instance variables.

What separates a mixin from multiple inheritance? Is it just a matter of semantics?
A mixin is a limited form of multiple inheritance. In some languages the mechanism for adding a mixin to a class is slightly different (in terms of syntax) from that of inheritance.
In the context of Python especially, a mixin is a parent class that provides functionality to subclasses but is not intended to be instantiated itself.
What might cause you to say, "that's just multiple inheritance, not really a mixin" is if the class that might be confused for a mixin can actually be instantiated and used - so indeed it is a semantic, and very real, difference.
Example of Multiple Inheritance
This example, from the documentation, is an OrderedCounter:
class OrderedCounter(Counter, OrderedDict):
'Counter that remembers the order elements are first encountered'
def __repr__(self):
return '%s(%r)' % (self.__class__.__name__, OrderedDict(self))
def __reduce__(self):
return self.__class__, (OrderedDict(self),)
It subclasses both the Counter and the OrderedDict from the collections module.
Both Counter and OrderedDict are intended to be instantiated and used on their own. However, by subclassing them both, we can have a counter that is ordered and reuses the code in each object.
This is a powerful way to reuse code, but it can also be problematic. If it turns out there's a bug in one of the objects, fixing it without care could create a bug in the subclass.
Example of a Mixin
Mixins are usually promoted as the way to get code reuse without potential coupling issues that cooperative multiple inheritance, like the OrderedCounter, could have. When you use mixins, you use functionality that isn't as tightly coupled to the data.
Unlike the example above, a mixin is not intended to be used on its own. It provides new or different functionality.
For example, the standard library has a couple of mixins in the socketserver library.
Forking and threading versions of each type of server can be created
using these mix-in classes. For instance, ThreadingUDPServer is
created as follows:
class ThreadingUDPServer(ThreadingMixIn, UDPServer):
pass
The mix-in class comes first, since it overrides a method defined in
UDPServer. Setting the various attributes also changes the behavior of
the underlying server mechanism.
In this case, the mixin methods override the methods in the UDPServer object definition to allow for concurrency.
The overridden method appears to be process_request and it also provides another method, process_request_thread. Here it is from the source code:
class ThreadingMixIn:
"""Mix-in class to handle each request in a new thread."""
# Decides how threads will act upon termination of the
# main process
daemon_threads = False
def process_request_thread(self, request, client_address):
"""Same as in BaseServer but as a thread.
In addition, exception handling is done here.
"""
try:
self.finish_request(request, client_address)
except Exception:
self.handle_error(request, client_address)
finally:
self.shutdown_request(request)
def process_request(self, request, client_address):
"""Start a new thread to process the request."""
t = threading.Thread(target = self.process_request_thread,
args = (request, client_address))
t.daemon = self.daemon_threads
t.start()
A Contrived Example
This is a mixin that is mostly for demonstration purposes - most objects will evolve beyond the usefulness of this repr:
class SimpleInitReprMixin(object):
"""mixin, don't instantiate - useful for classes instantiable
by keyword arguments to their __init__ method.
"""
__slots__ = () # allow subclasses to use __slots__ to prevent __dict__
def __repr__(self):
kwarg_strings = []
d = getattr(self, '__dict__', None)
if d is not None:
for k, v in d.items():
kwarg_strings.append('{k}={v}'.format(k=k, v=repr(v)))
slots = getattr(self, '__slots__', None)
if slots is not None:
for k in slots:
v = getattr(self, k, None)
kwarg_strings.append('{k}={v}'.format(k=k, v=repr(v)))
return '{name}({kwargs})'.format(
name=type(self).__name__,
kwargs=', '.join(kwarg_strings)
)
and usage would be:
class Foo(SimpleInitReprMixin): # add other mixins and/or extend another class here
__slots__ = 'foo',
def __init__(self, foo=None):
self.foo = foo
super(Foo, self).__init__()
And usage:
>>> f1 = Foo('bar')
>>> f2 = Foo()
>>> f1
Foo(foo='bar')
>>> f2
Foo(foo=None)

I think previous responses defined very well what MixIns are. However,
in order to better understand them, it might be useful to compare MixIns with Abstract Classes and Interfaces from the code/implementation perspective:
1. Abstract Class
Class that needs to contain one or more abstract methods
Abstract Class can contain state (instance variables) and non-abstract methods
2. Interface
Interface contains abstract methods only (no non-abstract methods and no internal state)
3. MixIns
MixIns (like Interfaces) do not contain internal state (instance variables)
MixIns contain one or more non-abstract methods (they can contain non-abstract methods unlike interfaces)
In e.g. Python these are just conventions, because all of the above are defined as classes. However, the common feature of both Abstract Classes, Interfaces and MixIns is that they should not exist on their own, i.e. should not be instantiated.

Mixins is a concept in Programming in which the class provides functionalities but it is not meant to be used for instantiation. Main purpose of Mixins is to provide functionalities which are standalone and it would be best if the mixins itself do not have inheritance with other mixins and also avoid state. In languages such as Ruby, there is some direct language support but for Python, there isn't. However, you could used multi-class inheritance to execute the functionality provided in Python.
I watched this video http://www.youtube.com/watch?v=v_uKI2NOLEM to understand the basics of mixins. It is quite useful for a beginner to understand the basics of mixins and how they work and the problems you might face in implementing them.
Wikipedia is still the best: http://en.wikipedia.org/wiki/Mixin

I think there have been some good explanations here but I wanted to provide another perspective.
In Scala, you can do mixins as has been described here but what is very interesting is that the mixins are actually 'fused' together to create a new kind of class to inherit from. In essence, you do not inherit from multiple classes/mixins, but rather, generate a new kind of class with all the properties of the mixin to inherit from. This makes sense since Scala is based on the JVM where multiple-inheritance is not currently supported (as of Java 8). This mixin class type, by the way, is a special type called a Trait in Scala.
It's hinted at in the way a class is defined:
class NewClass extends FirstMixin with SecondMixin with ThirdMixin
...
I'm not sure if the CPython interpreter does the same (mixin class-composition) but I wouldn't be surprised. Also, coming from a C++ background, I would not call an ABC or 'interface' equivalent to a mixin -- it's a similar concept but divergent in use and implementation.

I'd advise against mix-ins in new Python code, if you can find any other way around it (such as composition-instead-of-inheritance, or just monkey-patching methods into your own classes) that isn't much more effort.
In old-style classes you could use mix-ins as a way of grabbing a few methods from another class. But in the new-style world everything, even the mix-in, inherits from object. That means that any use of multiple inheritance naturally introduces MRO issues.
There are ways to make multiple-inheritance MRO work in Python, most notably the super() function, but it means you have to do your whole class hierarchy using super(), and it's considerably more difficult to understand the flow of control.

Perhaps a couple of examples will help.
If you're building a class and you want it to act like a dictionary, you can define all the various __ __ methods necessary. But that's a bit of a pain. As an alternative, you can just define a few, and inherit (in addition to any other inheritance) from UserDict.DictMixin (moved to collections.DictMixin in py3k). This will have the effect of automatically defining all the rest of the dictionary api.
A second example: the GUI toolkit wxPython allows you to make list controls with multiple columns (like, say, the file display in Windows Explorer). By default, these lists are fairly basic. You can add additional functionality, such as the ability to sort the list by a particular column by clicking on the column header, by inheriting from ListCtrl and adding appropriate mixins.

It's not a Python example but in the D programing language the term mixin is used to refer to a construct used much the same way; adding a pile of stuff to a class.
In D (which by the way doesn't do MI) this is done by inserting a template (think syntactically aware and safe macros and you will be close) into a scope. This allows for a single line of code in a class, struct, function, module or whatever to expand to any number of declarations.

OP mentioned that he/she never heard of mixin in C++, perhaps that is because they are called Curiously Recurring Template Pattern (CRTP) in C++. Also, #Ciro Santilli mentioned that mixin is implemented via abstract base class in C++. While abstract base class can be used to implement mixin, it is an overkill as the functionality of virtual function at run-time can be achieved using template at compile time without the overhead of virtual table lookup at run-time.
The CRTP pattern is described in detail here
I have converted the python example in #Ciro Santilli's answer into C++ using template class below:
#include <iostream>
#include <assert.h>
template <class T>
class ComparableMixin {
public:
bool operator !=(ComparableMixin &other) {
return ~(*static_cast<T*>(this) == static_cast<T&>(other));
}
bool operator <(ComparableMixin &other) {
return ((*(this) != other) && (*static_cast<T*>(this) <= static_cast<T&>(other)));
}
bool operator >(ComparableMixin &other) {
return ~(*static_cast<T*>(this) <= static_cast<T&>(other));
}
bool operator >=(ComparableMixin &other) {
return ((*static_cast<T*>(this) == static_cast<T&>(other)) || (*(this) > other));
}
protected:
ComparableMixin() {}
};
class Integer: public ComparableMixin<Integer> {
public:
Integer(int i) {
this->i = i;
}
int i;
bool operator <=(Integer &other) {
return (this->i <= other.i);
}
bool operator ==(Integer &other) {
return (this->i == other.i);
}
};
int main() {
Integer i(0) ;
Integer j(1) ;
//ComparableMixin<Integer> c; // this will cause compilation error because constructor is protected.
assert (i < j );
assert (i != j);
assert (j > i);
assert (j >= i);
return 0;
}
EDIT: Added protected constructor in ComparableMixin so that it can only be inherited and not instantiated. Updated the example to show how protected constructor will cause compilation error when an object of ComparableMixin is created.

The concept comes from Steve’s Ice Cream, an ice cream store founded by Steve Herrell in Somerville, Massachusetts, in 1973, where mix-ins (candies, cakes, etc.) were mixed into basic ice cream flavors (vanilla, chocolate, etc.).
Inspired by Steve’s Ice Cream, the designers of the Lisp object system Flavors included the concept in a programming language for the first time, where mix-ins were small helper classes designed for enhancing other classes, and flavors were large standalone classes.
So the main idea is that a mix-in is a reusable extension (’reusable’ as opposed to ‘exclusive’; ‘extension’ as opposed to ‘base’).
The concept is orthogonal to the concepts of single or multiple inheritance and abstract or concrete class. Mix-in classes can be used in single or multiple inheritance and can be abstract or concrete classes. Mix-in classes have incomplete interfaces while abstract classes have incomplete implementations and concrete classes have complete implementations.
Mix-in class names are conventionally suffixed with ‘-MixIn’, ‘-able’, or ‘-ible’ to emphasize their nature, like in the Python standard library with the ThreadingMixIn and ForkingMixIn classes of the socketserver module, and the Hashable, Iterable, Callable, Awaitable, AsyncIterable, and Reversible classes of the collections.abc module.
Here is an example of a mix-in class used for extending the Python built-in list and dict classes with logging capability:
import logging
class LoggingMixIn:
def __setitem__(self, key, value):
logging.info('Setting %r to %r', key, value)
super().__setitem__(key, value)
def __delitem__(self, key):
logging.info('Deleting %r', key)
super().__delitem__(key)
class LoggingList(LoggingMixIn, list):
pass
class LoggingDict(LoggingMixIn, dict):
pass
>>> logging.basicConfig(level=logging.INFO)
>>> l = LoggingList([False])
>>> d = LoggingDict({'a': False})
>>> l[0] = True
INFO:root:Setting 0 to True
>>> d['a'] = True
INFO:root:Setting 'a' to True
>>> del l[0]
INFO:root:Deleting 0
>>> del d['a']
INFO:root:Deleting 'a'

mixin gives a way to add functionality in a class, i.e you can interact with methods defined in a module by including the module inside the desired class. Though ruby doesn't supports multiple inheritance but provides mixin as an alternative to achieve that.
here is an example that explains how multiple inheritance is achieved using mixin.
module A # you create a module
def a1 # lets have a method 'a1' in it
end
def a2 # Another method 'a2'
end
end
module B # let's say we have another module
def b1 # A method 'b1'
end
def b2 #another method b2
end
end
class Sample # we create a class 'Sample'
include A # including module 'A' in the class 'Sample' (mixin)
include B # including module B as well
def S1 #class 'Sample' contains a method 's1'
end
end
samp = Sample.new # creating an instance object 'samp'
# we can access methods from module A and B in our class(power of mixin)
samp.a1 # accessing method 'a1' from module A
samp.a2 # accessing method 'a2' from module A
samp.b1 # accessing method 'b1' from module B
samp.b2 # accessing method 'a2' from module B
samp.s1 # accessing method 's1' inside the class Sample

I just used a python mixin to implement unit testing for python milters. Normally, a milter talks to an MTA, making unit testing difficult. The test mixin overrides methods that talk to the MTA, and create a simulated environment driven by test cases instead.
So, you take an unmodified milter application, like spfmilter, and mixin TestBase, like this:
class TestMilter(TestBase,spfmilter.spfMilter):
def __init__(self):
TestBase.__init__(self)
spfmilter.config = spfmilter.Config()
spfmilter.config.access_file = 'test/access.db'
spfmilter.spfMilter.__init__(self)
Then, use TestMilter in the test cases for the milter application:
def testPass(self):
milter = TestMilter()
rc = milter.connect('mail.example.com',ip='192.0.2.1')
self.assertEqual(rc,Milter.CONTINUE)
rc = milter.feedMsg('test1',sender='good#example.com')
self.assertEqual(rc,Milter.CONTINUE)
milter.close()
http://pymilter.cvs.sourceforge.net/viewvc/pymilter/pymilter/Milter/test.py?revision=1.6&view=markup

Maybe an example from ruby can help:
You can include the mixin Comparable and define one function "<=>(other)", the mixin provides all those functions:
<(other)
>(other)
==(other)
<=(other)
>=(other)
between?(other)
It does this by invoking <=>(other) and giving back the right result.
"instance <=> other" returns 0 if both objects are equal, less than 0 if instance is bigger than other and more than 0 if other is bigger.

I read that you have a c# background. So a good starting point might be a mixin implementation for .NET.
You might want to check out the codeplex project at http://remix.codeplex.com/
Watch the lang.net Symposium link to get an overview. There is still more to come on documentation on codeplex page.
regards
Stefan

Roughly summarizing all great answers above:
                States        /     Methods
Concrete Method
Abstract Method
Concrete State
Class
Abstract Class
Abstract State
Mixin
Interface

What's the best way to extend the functionality of factory-produced classes outside of the module in python?

I've been reading lots of previous SO discussions of factory functions, etc. and still don't know what the best (pythonic) approach is to this particular situation. I'll admit up front that i am imposing a somewhat artificial constraint on the problem in that i want my solution to work without modifying the module i am trying to extend: i could make modifications to it, but let's assume that it must remain as-is because i'm trying to understand best practice in this situation.
I'm working with the http://pypi.python.org/pypi/icalendar module, which handles parsing from and serializing to the Icalendar spec (hereafter ical). It parses the text into a hierarchy of dictionary-like "component" objects, where every "component" is an instance of a trivial derived class implementing the different valid ical types (VCALENDAR, VEVENT, etc.) and they are all spit out by a recursive factory from the common parent class:
class Component(...):
#classmethod
def from_ical(cls, ...)
I have created a 'CalendarFile' class that extends the ical 'Calendar' class, including in it generator function of its own:
class CalendarFile(Calendar):
#classmethod
def from_file(cls, ics):
which opens a file (ics) and passes it on:
instance = cls.from_ical(f.read())
It initializes and modifies some other things in instance and then returns it. The problem is that instance ends up being a Calendar object instead of a CalendarFile object, in spite of cls being CalendarFile. Short of going into the factory function of the ical module and fiddling around in there, is there any way to essentially "recast" that object as a 'CalendarFile'?
The alternatives (again without modifying the original module) that I have considered are:make the CalendarFile class a has-a Calendar class (each instance creates its own internal instance of a Calendar object), but that seems methodically stilted.
fiddle with the returned object to give it the methods it needs (i know there's a term for creating a customized object but it escapes me).
make the additional methods into functions and just have them work with instances of Calendar.
or perhaps the answer is that i shouldn't be trying to subclass from a module in the first place, and this type of code belongs in the module itself.
Again i'm trying to understand what the "best" approach is and also learn if i'm missing any alternatives. Thanks.

Normally, I would expect an alternative constructor defined as a classmethod to simply call the class's standard constructor, transforming the arguments that it receives into valid arguments to the standard constructor.
>>> class Toy(object):
... def __init__(self, x):
... self.x = abs(x)
... def __repr__(self):
... return 'Toy({})'.format(self.x)
... #classmethod
... def from_string(cls, s):
... return cls(int(s))
...
>>> Toy.from_string('5')
Toy(5)
In most cases, I would strongly recommend something like this approach; this is the gold standard for alternative constructors.
But this is a special case.
I've now looked over the source, and I think the best way to add a new class is to edit the module directly; otherwise, scrap inheritance and take option one (your "has-a" option). The different classes are all slightly differentiated versions of the same container class -- they shouldn't really even be separate classes. But if you want to add a new class in the idiom of the code as it it is written, you have to add a new class to the module itself. Furthermore, from_iter is deceptively named; it's not really a constructor at all. I think it should be a standalone function. It builds a whole tree of components linked together, and the code that builds the individual components is buried in a chain of calls to various factory functions that also should be standalone functions but aren't. IMO much of that code ought to live in __init__ where it would be useful to you for subclassing, but it doesn't.
Indeed, none of the subclasses of Component even add any methods. By adding methods to your subclass of Calendar, you're completely disregarding the actual idiom of the code. I don't like its idiom very much but by disregarding that idiom, you're making it even worse. If you don't want to modify the original module, then forget about inheritance here and give your object a has-a relationship to Calendar objects. Don't modify __class__; establish your own OO structure that follows standard OO practices.

python metaclasses at module level

I read What is a metaclass in Python?
and I tried to replicate the upper metaclass from the example and found that this doesn't work in all cases:
def upper(cls_name, cls_parents, cls_attr):
""" Make all class attributes uppper case """
attrs = ((name, value) for name, value in cls_attr.items()
if not name.startswith('__'))
upper_atts = dict((name.upper(), value) for name, value in attrs)
return type(cls_name, cls_parents, upper_atts)
__metaclass__ = upper #Module level
class Foo:
bar = 1
f = Foo()
print(f.BAR) #works in python2.6
The above fails (with an attribute error) in python3 which I think is natural because all classes in python3 already have object as their parent and metaclass resolution goes into the object class.
The question:
How do I make a module level metaclass in python3?

The module level metaclass isn't really "module level", it has to do with how class initialization worked. The class creation would look for the variable "__metaclass__" when creating the class, and if it wasn't in the local environment it would look in the global. Hence, if you had a "module level" __metaclass__ that would be used for every class afterwards, unless they had explicit metaclasses.
In Python 3, you instead specify the metaclass with a metaclass= in the class definition. Hence there is no module level metaclasses.
So what do you do? Easy: You specify it explicitly for each class.
It's really not much extra work, and you can even do it with a nice regexp search and replace if you really have hundreds of classes and don't want to do it manually.

If you want to change all the attributes to upper case, you should probably use the __init__ method to do so, than use a metaclass.
Metaclasses are deeper magic than 99% of users should ever worry about. If you wonder whether you need them, you don't (the people who actually need them know with certainty that they need them, and don't need an explanation about why).
-- Python Guru Tim Peters
If you need something deeper, you should also evaluate using Class Decorators.
Using MetaClasses and understanding how the classes are created is so unnecessary as long as you want to do something that you can do using class decorators or initialization.
That said, if you really want to use a Metaclass tho' pass that as a keyword argument to the class.
class Foo(object, metaclass=UpperCaseMetaClass)
where UpperCaseMetaClass is a class that extends type and not a method.
class UpperCaseMetaClass(type):
def __new__():
#Do your Magic here.

What is the correct way to extend a parent class method in modern Python

I frequently do this sort of thing:
class Person(object):
def greet(self):
print "Hello"
class Waiter(Person):
def greet(self):
Person.greet(self)
print "Would you like fries with that?"
The line Person.greet(self) doesn't seem right. If I ever change what class Waiter inherits from I'm going to have to track down every one of these and replace them all.
What is the correct way to do this is modern Python? Both 2.x and 3.x, I understand there were changes in this area in 3.
If it matters any I generally stick to single inheritance, but if extra stuff is required to accommodate multiple inheritance correctly it would be good to know about that.

You use super:
Return a proxy object that delegates
method calls to a parent or sibling
class of type. This is useful for
accessing inherited methods that have
been overridden in a class. The search
order is same as that used by
getattr() except that the type itself
is skipped.
In other words, a call to super returns a fake object which delegates attribute lookups to classes above you in the inheritance chain. Points to note:
This does not work with old-style classes -- so if you are using Python 2.x, you need to ensure that the top class in your hierarchy inherits from object.
You need to pass your own class and instance to super in Python 2.x. This requirement was waived in 3.x.
This will handle all multiple inheritance correctly. (When you have a multiple inheritance tree in Python, a method resolution order is generated and the lookups go through parent classes in this order.)
Take care: there are many places to get confused about multiple inheritance in Python. You might want to read super() Considered Harmful. If you are sure that you are going to stick to a single inheritance tree, and that you are not going to change the names of classes in said tree, you can hardcode the class names as you do above and everything will work fine.

Not sure if you're looking for this but you can call a parent without referring to it by doing this.
super(Waiter, self).greet()
This will call the greet() function in Person.

katrielalex's answer is really the answer to your question, but this wouldn't fit in a comment.
If you plan to go about using super everywhere, and you ever think in terms of multiple inheritance, definitely read the "super() Considered Harmful" link. super() is a great tool, but it takes understanding to use correctly. In my experience, for simple things that don't seem likely to get into complicated diamond inheritance tangles, it's actually easier and less tedious to just call the superclass directly and deal with the renames when you change the name of the base class.
In fact, in Python2 you have to include the current class name, which is usually more likely to change than the base class name. (And in fact sometimes it's very difficult to pass a reference to the current class if you're doing wacky things; at the point when the method is being defined the class isn't bound to any name, and at the point when the super call is executed the original name of the class may not still be bound to the class, such as when you're using a class decorator)

I'd like to make it more explicit in this answer with an example. It's just like how we do in JavaScript. The short answer is, do that like we initiate the constructor using super.
class Person(object):
def __init__(self, name):
self.name = name
def greet(self):
print(f"Hello, I'm {self.name}")
class Waiter(Person):
def __init__(self, name):
super().__init__(name)
# initiate the parent constructor
# or super(Waiter, self).__init__(name)
def greet(self):
super(Waiter, self).greet()
print("Would you like fries with that?")
waiter = Waiter("John")
waiter.greet()
# Hello, I'm John
# Would you like fries with that?

Difference between abstract class and interface in Python

What is the difference between abstract class and interface in Python?

What you'll see sometimes is the following:
class Abstract1:
"""Some description that tells you it's abstract,
often listing the methods you're expected to supply."""
def aMethod(self):
raise NotImplementedError("Should have implemented this")
Because Python doesn't have (and doesn't need) a formal Interface contract, the Java-style distinction between abstraction and interface doesn't exist. If someone goes through the effort to define a formal interface, it will also be an abstract class. The only differences would be in the stated intent in the docstring.
And the difference between abstract and interface is a hairsplitting thing when you have duck typing.
Java uses interfaces because it doesn't have multiple inheritance.
Because Python has multiple inheritance, you may also see something like this
class SomeAbstraction:
pass # lots of stuff - but missing something
class Mixin1:
def something(self):
pass # one implementation
class Mixin2:
def something(self):
pass # another
class Concrete1(SomeAbstraction, Mixin1):
pass
class Concrete2(SomeAbstraction, Mixin2):
pass
This uses a kind of abstract superclass with mixins to create concrete subclasses that are disjoint.

What is the difference between abstract class and interface in Python?
An interface, for an object, is a set of methods and attributes on that object.
In Python, we can use an abstract base class to define and enforce an interface.
Using an Abstract Base Class
For example, say we want to use one of the abstract base classes from the collections module:
import collections
class MySet(collections.Set):
pass
If we try to use it, we get an TypeError because the class we created does not support the expected behavior of sets:
>>> MySet()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Can't instantiate abstract class MySet with abstract methods
__contains__, __iter__, __len__
So we are required to implement at least __contains__, __iter__, and __len__. Let's use this implementation example from the documentation:
class ListBasedSet(collections.Set):
"""Alternate set implementation favoring space over speed
and not requiring the set elements to be hashable.
"""
def __init__(self, iterable):
self.elements = lst = []
for value in iterable:
if value not in lst:
lst.append(value)
def __iter__(self):
return iter(self.elements)
def __contains__(self, value):
return value in self.elements
def __len__(self):
return len(self.elements)
s1 = ListBasedSet('abcdef')
s2 = ListBasedSet('defghi')
overlap = s1 & s2
Implementation: Creating an Abstract Base Class
We can create our own Abstract Base Class by setting the metaclass to abc.ABCMeta and using the abc.abstractmethod decorator on relevant methods. The metaclass will be add the decorated functions to the __abstractmethods__ attribute, preventing instantiation until those are defined.
import abc
For example, "effable" is defined as something that can be expressed in words. Say we wanted to define an abstract base class that is effable, in Python 2:
class Effable(object):
__metaclass__ = abc.ABCMeta
#abc.abstractmethod
def __str__(self):
raise NotImplementedError('users must define __str__ to use this base class')
Or in Python 3, with the slight change in metaclass declaration:
class Effable(object, metaclass=abc.ABCMeta):
#abc.abstractmethod
def __str__(self):
raise NotImplementedError('users must define __str__ to use this base class')
Now if we try to create an effable object without implementing the interface:
class MyEffable(Effable):
pass
and attempt to instantiate it:
>>> MyEffable()
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
TypeError: Can't instantiate abstract class MyEffable with abstract methods __str__
We are told that we haven't finished the job.
Now if we comply by providing the expected interface:
class MyEffable(Effable):
def __str__(self):
return 'expressable!'
we are then able to use the concrete version of the class derived from the abstract one:
>>> me = MyEffable()
>>> print(me)
expressable!
There are other things we could do with this, like register virtual subclasses that already implement these interfaces, but I think that is beyond the scope of this question. The other methods demonstrated here would have to adapt this method using the abc module to do so, however.
Conclusion
We have demonstrated that the creation of an Abstract Base Class defines interfaces for custom objects in Python.

Python >= 2.6 has Abstract Base Classes.
Abstract Base Classes (abbreviated
ABCs) complement duck-typing by
providing a way to define interfaces
when other techniques like hasattr()
would be clumsy. Python comes with
many builtin ABCs for data structures
(in the collections module), numbers
(in the numbers module), and streams
(in the io module). You can create
your own ABC with the abc module.
There is also the Zope Interface module, which is used by projects outside of zope, like twisted. I'm not really familiar with it, but there's a wiki page here that might help.
In general, you don't need the concept of abstract classes, or interfaces in python (edited - see S.Lott's answer for details).

In a more basic way to explain:
An interface is sort of like an empty muffin pan.
It's a class file with a set of method definitions that have no code.
An abstract class is the same thing, but not all functions need to be empty. Some can have code. It's not strictly empty.
Why differentiate:
There's not much practical difference in Python, but on the planning level for a large project, it could be more common to talk about interfaces, since there's no code. Especially if you're working with Java programmers who are accustomed to the term.

Python doesn't really have either concept.
It uses duck typing, which removed the need for interfaces (at least for the computer :-))
Python <= 2.5:
Base classes obviously exist, but there is no explicit way to mark a method as 'pure virtual', so the class isn't really abstract.
Python >= 2.6:
Abstract base classes do exist (http://docs.python.org/library/abc.html). And allow you to specify methods that must be implemented in subclasses. I don't much like the syntax, but the feature is there. Most of the time it's probably better to use duck typing from the 'using' client side.

In general, interfaces are used only in languages that use the single-inheritance class model. In these single-inheritance languages, interfaces are typically used if any class could use a particular method or set of methods. Also in these single-inheritance languages, abstract classes are used to either have defined class variables in addition to none or more methods, or to exploit the single-inheritance model to limit the range of classes that could use a set of methods.
Languages that support the multiple-inheritance model tend to use only classes or abstract base classes and not interfaces. Since Python supports multiple inheritance, it does not use interfaces and you would want to use base classes or abstract base classes.
http://docs.python.org/library/abc.html

Abstract classes are classes that contain one or more abstract methods. Along with abstract methods, Abstract classes can have static, class and instance methods.
But in case of interface, it will only have abstract methods not other. Hence it is not compulsory to inherit abstract class but it is compulsory to inherit interface.

For completeness, we should mention PEP3119
where ABC was introduced and compared with interfaces,
and original Talin's comment.
The abstract class is not perfect interface:
belongs to the inheritance hierarchy
is mutable
But if you consider writing it your own way:
def some_function(self):
raise NotImplementedError()
interface = type(
'your_interface', (object,),
{'extra_func': some_function,
'__slots__': ['extra_func', ...]
...
'__instancecheck__': your_instance_checker,
'__subclasscheck__': your_subclass_checker
...
}
)
ok, rather as a class
or as a metaclass
and fighting with python to achieve the immutable object
and doing refactoring
...
you'll quite fast realize that you're inventing the wheel
to eventually achieve
abc.ABCMeta
abc.ABCMeta was proposed as a useful addition of the missing interface functionality,
and that's fair enough in a language like python.
Certainly, it was able to be enhanced better whilst writing version 3, and adding new syntax and immutable interface concept ...
Conclusion:
The abc.ABCMeta IS "pythonic" interface in python

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