Getting the mean of multiple axis of a numpy array

Question

In numpy is there a fast way of calculating the mean across multiple axis? I am calculating the mean on all but the 0 axis of an n-dimensional array.

I am currently doing this;

for i in range(d.ndim - 1):     d = d.mean(axis=1) 

I'm wondering if there is a solution that doesn't use a python loop.

Answer 1

In numpy 1.7 you can give multiple axis to np.mean:

d.mean(axis=tuple(range(1, d.ndim))) 

I am guessing this will perform similarly to the other proposed solutions, unless reshaping the array to flatten all dimensions triggers a copy of the data, in which case this should be much faster. So this is probably going to give a more consistent performance.