On Mon, Mar 16, 2015 at 11:53 AM, Dave Hirschfeld <dave.hirschf...@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]) > > Specify all of the axes you want to reduce over as a tuple. 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
_______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
