Hey John, I think broadcasting is your best bet. Here is a snippet using scipy (Numeric will be pretty much the same).

...

...

from scipy import * a = stats.random((4,3)) a array([[ 0.94058263, 0.24342623, 0.74673623], [ 0.53151542, 0.07523929, 0.49730805], [ 0.5161854 , 0.51049614, 0.70360875], [ 0.09470515, 0.60604334, 0.64941102]]) stats.mean(a) # axis=-1 by default in scipy array([ 0.6435817 , 0.36802092, 0.57676343, 0.45005317]) a-stats.mean(a)[:,NewAxis] array([[ 0.29700093, -0.40015546, 0.10315453], [ 0.1634945 , -0.29278163, 0.12928713], [-0.06057803, -0.06626729, 0.12684532], [-0.35534802, 0.15599017, 0.19935785]])

eric John Hunter <jdhunter@ace.bsd.uchicago.edu> wrote ..

I have a large (40,000 x 128) Numeric array, X, with typecode Float. In some cases the number of rows may be approx 10x greater.

I want to create an array Y with the same dimensions as X, where each element of Y is the corresponding element of X with the mean of the row on which it occurs subtracted away. Ie,

Y = X - transpose(resize(mean(X,1), (X.shape[1],X.shape[0])))

I am wondering if this is the most efficient way (speed and memory).

Thanks for any suggestions, John Hunter

------------------------------------------------------- This sf.net email is sponsored by:ThinkGeek Welcome to geek heaven. http://thinkgeek.com/sf _______________________________________________ Numpy-discussion mailing list Numpy-discussion@lists.sourceforge.net https://lists.sourceforge.net/lists/listinfo/numpy-discussion