I posted too quickly. A little performance testing told me that has_key is somewhat slower than "in". I used a large number of string keys in my test.
See my other post, but the reason has_key is slower is almost certainly that it has to do a LOAD_ATTR:
$ python -m timeit -s "m = dict.fromkeys(xrange(100, 200))" "[i in m for i in xrange(300)]"
10000 loops, best of 3: 102 usec per loop
$ python -m timeit -s "m = dict.fromkeys(xrange(100, 200)); has_key=m.has_key" "[has_key(i) for i in xrange(300)]"
10000 loops, best of 3: 107 usec per loop
For this data at least, the difference is negligible. (I actually found has_key to be faster in some other datasets.)
STeVe -- http://mail.python.org/mailman/listinfo/python-list
