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?
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]])
np.min(np.array([[[1,4]],[[2,5]],[[3,2]]]), axis=0) produces: array([[1, 2]]), isn't that what you are looking for?
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])
