Steven Bethard wrote: > Ok, lets get comparable functions by writing them both in Python:
First of all, your functions aren't quite comparable. The first index takes the value to locate as a variable, while the second has both values hard-coded as literals. Changing the second one to index2(L, a, b) makes a slight but not significant difference in the timings. Secondly, the lists you tested with are artificially short. As you increase the size of the list, the difference lessens to unimportance: $ python -m timeit -s "import temp; L = ['x'] * 500 + ['C', 'A', 'D', 'B']" "a, d = temp.index2(L,'A','D'); a < d" 1000 loops, best of 3: 227 usec per loop $ python -m timeit -s "import temp; L = ['x'] * 500 + ['C', 'A', 'D', 'B']" "a = temp.index1(L, 'A'); d = temp.index1(L, 'D'); a < d" 1000 loops, best of 3: 236 usec per loop Third, the target values are very close together in those lists. If there's a large difference in their positions, then the "two-in-one-pass" algorithm becomes much slower: $ python -m timeit -s "import temp; L = ['C','A'] + ['x'] * 500 + ['D', 'B']" "a = temp.index1(L, 'A'); d = temp.index1(L, 'D'); a < d" 10000 loops, best of 3: 120 usec per loop $ python -m timeit -s "import temp; L = ['C','A'] + ['x'] * 500 + ['D', 'B']" "a, d = temp.index2(L,'A','D'); a < d" 1000 loops, best of 3: 267 usec per loop Remember kids: 1. Numbers can show anything 2. Know your data set 3. Premature optimizations are evil -- http://mail.python.org/mailman/listinfo/python-list
