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
But How can I use numpy.mean to get the mean array.
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2 Answers
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))
answered Aug 31, 2013 at 6:32
Saullo G. P. Castro
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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 I believe this is a case of outdated documentation rather than something you're not supposed to rely on, since similar routines such as sum document the same feature.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)
4 Comments
Samuel
But I think your way is a little slower than mine. Beacause you have two time's division right
tacaswell
@Samuel Why don't you use
timeit and see ;)
user2357112
Huh, it does. Neither the 1.7 documentation nor the development version draft documentation say anything about that, and it doesn't look like there's any 1.7.1-specific documentation.
Saullo G. P. Castro
That's true... they should include this in the documentation since it's such an useful feature... I opened a ticket in GitHub...