Remove 2d slice from 3d numpy array

user_12178469 0
Viewed 1048

I need to remove the last arrays from a 3D numpy cube. I have:

a = np.array(
[[[1,2,3],
 [4,5,6],
 [7,8,9]],

[[9,8,7],
 [6,5,4],
 [3,2,1]],

[[0,0,0],
 [0,0,0],
 [0,0,0]],

[[0,0,0],
 [0,0,0],
 [0,0,0]]])

How do I remove the arrays with zero sub-arrays like at the bottom side of the cube, using np.delete?

(I cannot simply remove all zero values, because there will be zeros in the data on the top side)

cube multidimensional-array numpy python
5 Answers

For a 3D cube, you might check all against the last two axes 

a = np.asarray(a)
a[~(a==0).all((2,1))]

array([[[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]],

       [[9, 8, 7],
        [6, 5, 4],
        [3, 2, 1]]])
user_25356110

If you know where they are already, the easiest thing to do is slice them off:

a[:-2]

Results in: 

array([[[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]],

       [[9, 8, 7],
        [6, 5, 4],
        [3, 2, 1]]])
user_33813050

Here's one way to remove trailing all zeros slices, as mentioned in the question that we want to keep the all zeros slices in the data on the top side -

a[:-(a==0).all((1,2))[::-1].argmin()]

Sample run -

In [80]: a
Out[80]: 
array([[[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[9, 8, 7],
        [6, 5, 4],
        [3, 2, 1]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]]])

In [81]: a[:-(a==0).all((1,2))[::-1].argmin()]
Out[81]: 
array([[[0, 0, 0],
        [0, 0, 0],
        [0, 0, 0]],

       [[9, 8, 7],
        [6, 5, 4],
        [3, 2, 1]]])
user_32938810

Hope this helps,

a_new=[] #Create a empty list
for item in a:
        if not (np.count_nonzero(item) == 0): #check if inner matrix is empty or not
            a_new.append(item) #appending to inner matrix to the list

a_new=np.array(a_new) #creating numpy matrix with removed zero elements

Output:

array([[[1, 2, 3],
        [4, 5, 6],
        [7, 8, 9]],

       [[9, 8, 7],
        [6, 5, 4],
        [3, 2, 1]]])
user_116237110

Use any and select :)

a=np.array([[[1,2,3],
 [4,5,6],
 [7,8,9]],

[[9,8,7],
 [6,5,4],
 [3,2,1]],

[[0,0,0],
 [0,0,0],
 [0,0,0]],

[[0,0,0],
 [0,0,0],
 [0,0,0]]])
a[a.any(axis=2).any(axis=1)]
user_48503430
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