Numpy mean and std over every terms of arrays

numpy

Solution

As Divikar points out, you can pass the list of arrays to `np.mean` and specify `axis=0` to average over corresponding values from each array in the list:

In [13]: np.mean([x,y], axis=0)
Out[13]: 
array([[ 1.5,  2.5],
       [ 3.5,  4.5]])

This works for lists of arbitrary length. For just two arrays, `(x+y)/2.0` is faster:

In [20]: %timeit (x+y)/2.0
100000 loops, best of 3: 1.96 µs per loop

In [21]: %timeit np.mean([x,y], axis=0)
10000 loops, best of 3: 21.6 µs per loop

Problem

I have a list of 2 dimensional arrays (same shape), and would like to get the mean and deviation for all terms, in a result array of the same shape as the inputs. I have trouble understanding from the doc whether this is possible. All my attempts with axis and keepdims parameters produce results of different shapes. I would like for example to have: mean([x, x]) equal to x, and std([x, x]) zeroes shaped like x. Is this possible without reshaping the arrays ? If not, how to do it with reshaping ? Example: ``` >> x= np.array([[1,2],[3,4]]) >>> y= np.array([[2,3],[4,5]]) >>> np.mean([x,y]) 3.0 ``` I want `[[1.5,2.5],[3.5,4.5]]` instead.

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