Hi all, I often want to use some kind of dimension-reducing function (like min(), max(), sum(), mean()) on an array without actually removing the last dimension, so that I can then do operations broadcasting the reduced array back to the size of the full array. Full example:
>> table.shape (47, 1814) >> table.min(axis=1).shape (47,) >> table - table.min(axis=1) ValueError: shape mismatch: objects cannot be broadcast to a single shape >> table - table.min(axis=1)[:, newaxis] I have to resort to ugly code with lots of stuff like "... axis=1)[:, newaxis]". Is there any way to get the reducing functions to leave a size-1 dummy dimension in place, to make this easier? Thanks! Dan _______________________________________________ Numpy-discussion mailing list Numpy-discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
