23.1 Extending Built-in Types

Besides implementing new kinds of objects, classes are sometimes used to extend the functionality of Python's built-in types in order to support more exotic data structures. For instance, to add queue insert and delete methods to lists, you can code classes that wrap (embed) a list object, and export insert and delete methods that process the list specially, like the delegation technique studied in Chapter 22. As of Python 2.2, you can also use inheritance to specialize built-in types. The next two sections show both techniques in action.

23.1.1 Extending Types by Embedding

Remember those set functions we wrote in Part IV? Here's what they look like brought back to life as a Python class. The following example, file setwrapper.py, implements a new set object type, by moving some of the set functions to methods, and adding some basic operator overloading. For the most part, this class just wraps a Python list with extra set operations. Because it's a class, it also supports multiple instances and customization by inheritance in subclasses.

class Set:    def __init__(self, value = [  ]):     # Constructor        self.data = [  ]                 # Manages a list        self.concat(value)    def intersect(self, other):        # other is any sequence.        res = [  ]                       # self is the subject.        for x in self.data:            if x in other:             # Pick common items.                res.append(x)        return Set(res)                # Return a new Set.    def union(self, other):            # other is any sequence.        res = self.data[:]             # Copy of my list        for x in other:                # Add items in other.            if not x in res:                res.append(x)        return Set(res)    def concat(self, value):           # value: list, Set...        for x in value:                # Removes duplicates           if not x in self.data:                self.data.append(x)    def __len__(self):          return len(self.data)        # len(self)    def __getitem__(self, key): return self.data[key]        # self[i]    def __and__(self, other):   return self.intersect(other) # self & other    def __or__(self, other):    return self.union(other)     # self | other    def __repr__(self):         return 'Set:' + `self.data`  # Print

By overloading indexing, the set class can often masquerade as a real list. Since you will interact with and extend this class in an exercise at the end of this chapter, we won't say much more about this code until Appendix B.

23.1.2 Extending Types by Subclassing

Beginning with Python 2.2, all the built-in types can now be subclassed directly. Type conversion functions such as list, str, dict, and tuple, have become built-in type names although transparent to your script, a type conversion call (e.g., list('spam')) is now really an invocation of a type's object constructor.

This change allows us to customize or extend the behavior of built-in types with user-defined class statements: simply subclass the new type names to customize them. Instances of your type subclasses can be used anywhere that the original built-in type can appear. For example, suppose you have trouble getting used to the fact that Python list offsets begin at 0 instead of 1. Not to worry you can always code your own subclass that customizes this core behavior of lists. File typesubclass.py shows how:

# Subclass built-in list type/class. # Map 1..N to 0..N-1; call back to built-in version. class MyList(list):     def __getitem__(self, offset):         print '(indexing %s at %s)' % (self, offset)           return list.__getitem__(self, offset - 1)  if __name__ == '__main__':     print list('abc')     x = MyList('abc')               # __init__ inherited from list     print x                         # __repr__ inherited from list     print x[1]                      # MyList.__getitem__     print x[3]                      # Customizes list superclass method     x.append('spam'); print x       # Attributes from list superclass     x.reverse(  );      print x

In this file, the MyList subclass extends the built-in list's __getitem__ indexing method only, in order to map indexes 1 to N back to the required 0 to N-1. All it really does is decrement the index submitted, and call back to the superclass's version of indexing, but it's enough to do the trick:

% python typesubclass.py ['a', 'b', 'c'] ['a', 'b', 'c'] (indexing ['a', 'b', 'c'] at 1) a (indexing ['a', 'b', 'c'] at 3) c ['a', 'b', 'c', 'spam'] ['spam', 'c', 'b', 'a']

This output also includes tracing text the class prints on indexing. Whether or not changing indexing this way is a good idea in general is another issue users of your MyList class may very well be confused by such a core departure from Python sequence behavior. The fact that you can customize built-in types this way can be a powerful tool in general, though.

For instance, this coding pattern gives rise to an alternative way to code sets as a subclass of the built-in list type, rather than a standalone class that manages an embedded list object. The following class, coded in file setsubclass.py, customizes lists, to add just methods and operators related to set processing; because all other behavior is inherited from the built-in list superclass, this makes for a shorter and simpler alternative:

class Set(list):     def __init__(self, value = [  ]):     # Constructor         list.__init__([  ])               # Customizes list         self.concat(value)               # Copies mutable defaults     def intersect(self, other):          # other is any sequence.         res = [  ]                       # self is the subject.         for x in self:             if x in other:               # Pick common items.                 res.append(x)         return Set(res)                  # Return a new Set.     def union(self, other):            # other is any sequence.         res = Set(self)                # Copy me and my list.         res.concat(other)         return res     def concat(self, value):           # value: list, Set...         for x in value:                # Removes duplicates             if not x in self:                 self.append(x)     def __and__(self, other): return self.intersect(other)     def __or__(self, other):  return self.union(other)     def __repr__(self):       return 'Set:' + list.__repr__(self) if __name__ == '__main__':     x = Set([1,3,5,7])     y = Set([2,1,4,5,6])     print x, y, len(x)     print x.intersect(y), y.union(x)     print x & y, x | y     x.reverse(  ); print x

Here is the output of this script's self-test code at the end of the file. Because subclassing core types is an advanced feature, we will omit further details here, but invite you to trace through these results in the code, to study its behavior:

% python setsubclass.py Set:[1, 3, 5, 7] Set:[2, 1, 4, 5, 6] 4 Set:[1, 5] Set:[2, 1, 4, 5, 6, 3, 7] Set:[1, 5] Set:[1, 3, 5, 7, 2, 4, 6] Set:[7, 5, 3, 1]

There are more efficient ways to implement sets with dictionaries in Python, that replace the linear scans in the set implementations we've shown, with dictionary index operations (hashing), and so run much quicker. (For more details, see Programming Python, Second Edition [O'Reilly]). If you're interested in sets, also see the new set module that was added in Python 2.3 release; this module provides a set object and set operations as built-in tools. Sets are fun to experiment with, but are no longer strictly required as of Python 2.3.

For another type subclass example, see the implementation of the new bool type in Python 2.3: as mentioned earlier, bool is a subclass of int, with two instances True and False that behave like integers 1 and 0, but inherit custom string reprresentation methods that display their names.