Dennis Lee Bieber <[EMAIL PROTECTED]> wrote: > Something like (pseudo-code): > > > cnt = 0 > for rw in cursor(): > if cnt: > for i,v in enumerate(rw): > sum[i] += v #accumulate next row > else: > sum = rw #initialize to first row > cnt += 1 > avg = [ float(v) / cnt for v in sum] > > Don't know if this is faster than numpy operations, but it > definitely reduces the amount of memory consumed by that list of > lists...
A lot of course depends on the input data, and the example given wasn't even syntactically valid so I can't tell if he started with integers or floats, but if the lists are long and the OP is at all worried about accuracy, this code may be a bad idea. Adding up a long list of values and then dividing by the number of values is the classic computer science example of how to get an inaccurate answer from a floating point calculation. I wouldn't have mentioned it, but since the OP said numpy it might just be that he cares about the result. What I really don't understand though is why, if he wants the final result in NumPy, he doesn't just use it for the calculation? >>> from numpy import array >>> lst = [[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]] >>> array(lst).mean(axis=0) array([ 3.5, 4.5, 5.5, 6.5, 7.5]) What I haven't tried to figure out is whether, having said all that, NumPy's mean is any more accurate than the naive sum and divide calculation? So far as I can tell, it isn't any more accurate although it does give different results. -- http://mail.python.org/mailman/listinfo/python-list
