Python: generator expression vs. yield
There are only slight differences in the two. You can use the dis module to examine this sort of thing for yourself.
Edit: My first version decompiled the generator expression created at module-scope in the interactive prompt. That's slightly different from the OP's version with it used inside a function. I've modified this to match the actual case in the question.
As you can see below, the "yield" generator (first case) has three extra instructions in the setup, but from the first FOR_ITER they differ in only one respect: the "yield" approach uses a LOAD_FAST in place of a LOAD_DEREF inside the loop. The LOAD_DEREF is "rather slower" than LOAD_FAST, so it makes the "yield" version slightly faster than the generator expression for large enough values of x (the outer loop) because the value of y is loaded slightly faster on each pass. For smaller values of x it would be slightly slower because of the extra overhead of the setup code.
It might also be worth pointing out that the generator expression would usually be used inline in the code, rather than wrapping it with the function like that. That would remove a bit of the setup overhead and keep the generator expression slightly faster for smaller loop values even if LOAD_FAST gave the "yield" version an advantage otherwise.
In neither case would the performance difference be enough to justify deciding between one or the other. Readability counts far more, so use whichever feels most readable for the situation at hand.
>>> def Generator(x, y):... for i in xrange(x):... for j in xrange(y):... yield(i, j)...>>> dis.dis(Generator) 2 0 SETUP_LOOP 54 (to 57) 3 LOAD_GLOBAL 0 (xrange) 6 LOAD_FAST 0 (x) 9 CALL_FUNCTION 1 12 GET_ITER >> 13 FOR_ITER 40 (to 56) 16 STORE_FAST 2 (i) 3 19 SETUP_LOOP 31 (to 53) 22 LOAD_GLOBAL 0 (xrange) 25 LOAD_FAST 1 (y) 28 CALL_FUNCTION 1 31 GET_ITER >> 32 FOR_ITER 17 (to 52) 35 STORE_FAST 3 (j) 4 38 LOAD_FAST 2 (i) 41 LOAD_FAST 3 (j) 44 BUILD_TUPLE 2 47 YIELD_VALUE 48 POP_TOP 49 JUMP_ABSOLUTE 32 >> 52 POP_BLOCK >> 53 JUMP_ABSOLUTE 13 >> 56 POP_BLOCK >> 57 LOAD_CONST 0 (None) 60 RETURN_VALUE>>> def Generator_expr(x, y):... return ((i, j) for i in xrange(x) for j in xrange(y))...>>> dis.dis(Generator_expr.func_code.co_consts[1]) 2 0 SETUP_LOOP 47 (to 50) 3 LOAD_FAST 0 (.0) >> 6 FOR_ITER 40 (to 49) 9 STORE_FAST 1 (i) 12 SETUP_LOOP 31 (to 46) 15 LOAD_GLOBAL 0 (xrange) 18 LOAD_DEREF 0 (y) 21 CALL_FUNCTION 1 24 GET_ITER >> 25 FOR_ITER 17 (to 45) 28 STORE_FAST 2 (j) 31 LOAD_FAST 1 (i) 34 LOAD_FAST 2 (j) 37 BUILD_TUPLE 2 40 YIELD_VALUE 41 POP_TOP 42 JUMP_ABSOLUTE 25 >> 45 POP_BLOCK >> 46 JUMP_ABSOLUTE 6 >> 49 POP_BLOCK >> 50 LOAD_CONST 0 (None) 53 RETURN_VALUE
In this example, not really. But yield can be used for more complex constructs - for example it can accept values from the caller as well and modify the flow as a result. Read PEP 342 for more details (it's an interesting technique worth knowing).
Anyway, the best advice is use whatever is clearer for your needs.
P.S. Here's a simple coroutine example from Dave Beazley:
def grep(pattern): print "Looking for %s" % pattern while True: line = (yield) if pattern in line: print line,# Example useif __name__ == '__main__': g = grep("python") g.next() g.send("Yeah, but no, but yeah, but no") g.send("A series of tubes") g.send("python generators rock!")
There is no difference for the kind of simple loops that you can fit into a generator expression. However yield can be used to create generators that do much more complex processing. Here is a simple example for generating the fibonacci sequence:
>>> def fibgen():... a = b = 1... while True:... yield a... a, b = b, a+b>>> list(itertools.takewhile((lambda x: x<100), fibgen()))[1, 1, 2, 3, 5, 8, 13, 21, 34, 55, 89]