how to find minimum/maximum values axis by axis in numpy array

Question

I have a NumPy array with a shape of (3,1,2):

A=np.array([[[1,4]], [[2,5]], [[3,2]]]).

I'd like to get the min in each column.

In this case, they are 1 and 2. I tried to use np.amin but it returns an array and that is not what I wanted. Is there a way to do this in just one or two lines of python code without using loops?

Possible duplicate of numpy max vs amax vs maximum

ivan_pozdeev
– ivan_pozdeev

2016-03-11 01:39:56 +00:00
Commented Mar 11, 2016 at 1:39 — ivan_pozdeev
– ivan_pozdeev, Commented Mar 11, 2016 at 1:39

Akavall · Accepted Answer · 2016-03-11 01:33:14Z

9

You can specify axis as parameter to numpy.min function.

In [10]: A=np.array([[[1,4]], [[2,5]], [[3,6]]]) In [11]: np.min(A) Out[11]: 1 In [12]: np.min(A, axis=0) Out[12]: array([[1, 4]]) In [13]: np.min(A, axis=1) Out[13]: array([[1, 4], [2, 5], [3, 6]]) In [14]: np.min(A, axis=2) Out[14]: array([[1], [2], [3]])

answered Mar 11, 2016 at 1:33

Akavall

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2 Comments

Akavall Over a year ago

@mengmengxyz, I am not sure that I understand your question. np.min(np.array([[[1,4]],[[2,5]],[[3,2]]]), axis=0) produces: array([[1, 2]]), isn't that what you are looking for?

mengmengxyz Over a year ago

That works.. Sorry for confusing you. I messed up my code while doing the testing. Thanks a lot

Shaan · Accepted Answer · 2022-05-17 14:37:59Z

You need to specify the axes along which you need to find the minimum. In your case, it is along the first two dimensions. So:

>>> np.min(A, axis=(0, 1)) array([1, 2])

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