How To Get A Mean Array With Numpy

I have a 4-D(d0,d1,d2,d3,d4) numpy array. I want to get a 2-D(d0,d1)mean array, Now my solution is as following: area=d3*d4 mean = numpy.sum(numpy.sum(data, axis=3), axis=2) / area

Solution 1:

You can reshape and then perform the average:

res = data.reshape(data.shape[0], data.shape[1], -1).mean(axis=2)

In NumPy 1.7.1 you can pass a tuple to the axis argument:

res = np.mean(data, axis=(2,3,4))

Solution 2:

In at least version 1.7.1, mean supports a tuple of axes for the axis parameter, though this feature isn't documented. I believe this is a case of outdated documentation rather than something you're not supposed to rely on, since similar routines such as sum document the same feature. Nevertheless, use at your own risk:

mean = data.mean(axis=(2, 3))

If you don't want to use undocumented features, you can just make two passes:

mean = data.mean(axis=3).mean(axis=2)