On Mon, Mar 16, 2015 at 11:53 AM, Dave Hirschfeld <dave.hirschfeld@gmail.com> wrote:

I have a number of large arrays for which I want to compute the mean and
standard deviation over a particular axis - e.g. I want to compute the
statistics for axis=1 as if the other axes were combined so that in the
example below I get two values back

In [1]: a = randn(30, 2, 10000)

For the mean this can be done easily like:

In [2]: a.mean(0).mean(-1)
Out[2]: array([ 0.0007, -0.0009])


...but this won't work for the std. Using some transformations we can
come up with something which will work for either:

In [3]: a.transpose(2,0,1).reshape(-1, 2).mean(axis=0)
Out[3]: array([ 0.0007, -0.0009])

In [4]: a.transpose(1,0,2).reshape(2, -1).mean(axis=-1)
Out[4]: array([ 0.0007, -0.0009])

In [1]: import numpy as np

In [2]: a = np.random.randn(30, 2, 10000)

In [3]: a.mean(axis=(0,-1))

Out[3]: array([-0.00224589, 0.00230759])

In [4]: a.std(axis=(0,-1))

Out[4]: array([ 1.00062771, 1.0001258 ])

-Eric