Get mean of 2D slice of a 3D array in numpy

arrays numpy multidimensional-array slice mean
robintw picture robintw · Aug 21, 2013 · Viewed 14.8k times · Source

I have a numpy array with a shape of:

(11L, 5L, 5L)

I want to calculate the mean over the 25 elements of each 'slice' of the array [0, :, :], [1, :, :] etc, returning 11 values.

It seems silly, but I can't work out how to do this. I've thought the mean(axis=x) function would do this, but I've tried all possible combinations of axis and none of them give me the result I want.

I can obviously do this using a for loop and slicing, but surely there is a better way?

Answer

J. Martinot-Lagarde picture J. Martinot-Lagarde · Aug 21, 2013

Use a tuple for axis :

>>> a = np.arange(11*5*5).reshape(11,5,5)
>>> a.mean(axis=(1,2))
array([  12.,   37.,   62.,   87.,  112.,  137.,  162.,  187.,  212.,
        237.,  262.])

Edit: This works only with numpy version 1.7+.

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