Section 20.4. Subclassing Built-In Types
20.4. Subclassing Built-In TypesThere is one more twist in the stack and set story, before we move on to some more classical data structures. In recent Python releases, it is also possible to subclass built-in datatypes such as lists and dictionaries, in order to extend them. That is, because datatypes now look and feel just like customizable classes, we can code unique datatypes that are extensions of built-ins, with subclasses that inherit built-in tool sets. For instance, here are our stack and set objects coded in the prior sections, revised as customized lists (the set union method has been simplified slightly here to remove a redundant loop): class Stack(list): "a list with extra methods" def top(self): return self[-1] def push(self, item): list.append(self, item) def pop(self): if not self: return None # avoid exception else: return list.pop(self) class Set(list): " a list with extra methods and operators" def _ _init_ _(self, value=[]): # on object creation list._ _init_ _(self) self.concat(value) def intersect(self, other): # other is any sequence type res = [] # self is the instance subject for x in self: if x in other: res.append(x) return Set(res) # return a new Set def union(self, other): res = Set(self) # new set with a copy of my list res.concat(other) # insert uniques from other return res def concat(self, value): # value: a list, string, Set... for x in value: # filters out duplicates if not x in self: self.append(x) # len, getitem, iter inherited, use list repr def _ _and_ _(self, other): return self.intersect(other) def _ _or_ _(self, other): return self.union(other) def _ _str_ _(self): return '<Set:' + repr(self) + '>' class FastSet(dict): pass # this doesn't simplify much The stack and set implemented in this code are essentially like those we saw earlier, but instead of embedding and managing a list, these objects really are customized lists. They add a few additional methods, but they inherit all of the list object's functionality. This can reduce the amount of wrapper code required, but it can also expose functionality that might not be appropriate in some cases. In the following self-test code, for example, we're able to sort and insert into stacks and reverse a set, because we've inherited these methods from the list object. In most cases, such operations don't make sense for the data structures in question, and the wrapper class approach of the prior sections may still be preferred: def selfTest( ): # normal use cases stk = Stack( ) print stk for c in 'spam': stk.push(c) print stk, stk.top( ) while stk: print stk, stk.pop( ) print stk, stk.pop( ) print set = Set('spam') print set, 'p' in set print set & Set('slim') print set | 'slim' print Set('slim') | Set('spam') # downside? these work too print stk = Stack('spam') print stk stk.insert(1, 'X') # should only access top print stk stk.sort( ) # stack not usually sorted print stk set = Set('spam') set.reverse( ) # order should not matter print set, set[1] if _ _name_ _ == '_ _main_ _': selfTest( ) When run, this module produces the following results on standard output; we're able to treat the stack and set objects like lists, whether we should or not: [] ['s', 'p', 'a', 'm'] m ['s', 'p', 'a', 'm'] m ['s', 'p', 'a'] a ['s', 'p'] p ['s'] s [] None <Set:['s', 'p', 'a', 'm']> True <Set:['s', 'm']> <Set:['s', 'p', 'a', 'm', 'l', 'i']> <Set:['s', 'l', 'i', 'm', 'p', 'a']> ['s', 'p', 'a', 'm'] ['s', 'X', 'p', 'a', 'm'] ['X', 'a', 'm', 'p', 's'] <Set:['m', 'a', 'p', 's']> a Subclassing built-in types has other applications, which may be more useful than those demonstrated by the preceding code. Consider a queue, or ordered dictionary, for example. The queue could take the form of a list subclass with get and put methods to insert on one end and delete from the other; the dictionary could be coded as a dictionary subclass with an extra list of keys that is sorted on insertion or request. Similarly, the built-in Python bool Boolean datatype is implemented as a subclass that customizes the integer int with a specialized display format (true is like integer 1, but it prints itself as the word True). You can also use type subclassing to alter the way built-in types behave; a list subclass could map indexes 1..N to built-in indexes 0..N-1, for instance: # map 1..N to 0..N-1, by calling 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 % 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 works, but it is probably not a good idea in general. It would likely confuse its usersthey will expect Python's standard 0..N-1 indexing, unless they are familiar with the custom class. As a rule of thumb, type subclasses should probably adhere to the interface of the built-in types they customize.
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