# values - python mean of 3d array

## Using `numpy.average`

Also `numpy.average` can be used with the same syntax:

``````import numpy as np
a = np.array([np.arange(0,9).reshape(3,3),np.arange(9,18).reshape(3,3)])
averaged_array = np.average(a,axis=0)
``````

The advantage of numpy.average compared to `numpy.mean` is the possibility to use also the weights parameter as an array of the same shape:

``````weighta = np.empty((3,3))
weightb = np.empty((3,3))
weights = np.array([weighta.fill(0.5),weightb.fill(0.8) ])
np.average(a,axis=0,weights=weights)
``````

If you use masked arrays consider also using `numpy.ma.average` because `numpy.average` don't deal with them.

Given two ndarrays

``````old_set = [[0, 1], [4, 5]]
new_set = [[2, 7], [0, 1]]
``````

I'm looking to get the mean of the respective values between the two arrays so that the data ends up something like:

``````end_data = [[1, 4], [2, 3]]
``````

basically it would apply something like

``````for i in len(old_set):
end_data[i] = (old_set[i]+new_set[i])/2
``````

But I'm unsure what syntax to use.. Thanks for the help in advance!

You can create a 3D array containing your 2D arrays to be averaged, then average along `axis=0` using `np.mean` or `np.average` (the latter allows for weighted averages):

``````np.mean( np.array([ old_set, new_set ]), axis=0 )
``````

This averaging scheme can be applied to any `(n)`-dimensional array, because the created `(n+1)`-dimensional array will always contain the original arrays to be averaged along its `axis=0`. 