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As a test, I tried the following (taken mostly from http://codesnipers.com/?q=python-flyweights):
class Foo(object):
 _Foo = weakref.WeakValueDictionary()
 def __new__(cls, addr):
 obj = Foo._Foo.get(addr, None)
 if obj is None:
 obj = object.__new__(cls)
 Foo._Foo[addr] = obj
 obj.addr = addr
 return obj
I created 10 million instances of Foo(34) in an array. Total space taken: ~80 MB. Times: CPU times: user 6.93 s, sys: 48.7 ms, total: 6.98 s
Wall time: 6.98 s
I then created 10 million instances of a non-flyweight object, assigning an int to an instance variable:
class Bar(object):
 pass
Total space taken: ~1.4 GB. Times:
CPU times: user 7.64 s, sys: 794 ms, total: 8.44 s
Wall time: 8.44 s
This corresponds (roughly) to the space taken by 10 million IPAddr objects.
So it appears, informally, that caching / flyweight results in modest time and significant memory savings.
I understand that the ship has sailed for a stdlib implementation, but these results are compelling enough for me to create a separate package.