On Thu, Aug 19, 2010 at 11:29 AM, Joe Harrington <[email protected]> wrote: > On Thu, 19 Aug 2010 09:06:32 -0500, G?khan Sever <[email protected]> > wrote: > >>On Thu, Aug 19, 2010 at 9:01 AM, greg whittier <[email protected]> wrote: >> >>> I frequently deal with 3D data and would like to sum (or find the >>> mean, etc.) over the last two axes. I.e. sum a[i,j,k] over j and k. >>> I find using .sum() really convenient for 2d arrays but end up >>> reshaping 2d arrays to do this. I know there has to be a more >>> convenient way. Here's what I'm doing >>> >>> a = np.arange(27).reshape(3,3,3) >>> >>> # sum over axis 1 and 2 >>> result = a.reshape((a.shape[0], a.shape[1]*a.shape[2])).sum(axis=1) >>> >>> Is there a cleaner way to do this? I'm sure I'm missing something obvious. >>> >>> Thanks, >>> Greg >>> >> >>Using two sums >> >>np.sum(np.sum(a, axis=-2), axis=1) > > Be careful. This works for sums, but not for operations like median; > the median of the row medians may not be the global median. So, you > need to do the medians in one step. I'm not aware of a method cleaner > than manually reshaping first. There may also be speed reasons to do > things in one step. But, two steps may look cleaner in code.
I think, two .sums() are the most accurate, if precision matters. One big summation is often not very precise. Josef > > --jh-- > _______________________________________________ > NumPy-Discussion mailing list > [email protected] > http://mail.scipy.org/mailman/listinfo/numpy-discussion > _______________________________________________ NumPy-Discussion mailing list [email protected] http://mail.scipy.org/mailman/listinfo/numpy-discussion
