Numpy mean and std over every terms of arrays
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.
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
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