Delete multiple elements from a 3-D numpy array

Question

I have a 3D array:

array([[[ 0.61795117,  0.34560273],
        [ 0.60095554,  0.313913  ],
        [ 0.56190875,  0.27450137],
        [ 0.53021642,  0.26212656],
        [ 0.49034335,  0.25858353],
        [ 0.44450648,  0.26146402],
        [ 0.41882064,  0.26448245],
        [ 0.39206795,  0.26703482],
        [ 0.37275912,  0.27370546]],

       [[ 0.62495791,  0.3120504 ],
        [ 0.60739456,  0.2823137 ],
        [ 0.58004256,  0.25910831],
        [ 0.54361132,  0.25084742],
        [ 0.49346121,  0.24739456],
        [ 0.44806806,  0.24990211],
        [ 0.40776521,  0.25205095],
        [ 0.37056546,  0.256433  ],
        [ 0.34548124,  0.26043473]],

       [[ 0.65452639,  0.26129899],
        [ 0.62969364,  0.25388184],
        [ 0.59575798,  0.24279791],
        [ 0.56249134,  0.23879938],
        [ 0.50719102,  0.23659991],
        [ 0.4391442 ,  0.23682689],
        [ 0.39447801,  0.23831357],
        [ 0.35186027,  0.24433696],
        [ 0.31920393,  0.25020494]]])

I have a list of indexes:

[(0, 0),(0, 1),(0, 2),(0, 3),(0, 4),(0, 5),(0, 6),(0, 7),(0, 8),(1, 0),(1, 3),(1, 6),(2, 0),(1, 1)]

I am trying to delete the rows in the 3D array that each tuple points to.

I have tried, but unsuccessful

np.delete(array, indexes, axis=0)

Any suggestions?

do you want something like delete (0,0) should only delete [ 0.61795117, 0.34560273] and nothing else? — Pratik Kumar
– Pratik Kumar, Commented Feb 2, 2018 at 18:47

Banana · Accepted Answer · 2018-02-02 19:53:27Z

3

Probably not the most beautiful solution, but you can use boolean indexing (that is, if those lists always have a similar shape to what you showed). I explicitly assume that the elements you want to delete correspond to the subarrays of size two here, then:

indices = [(0, 0),(0, 1),(0, 2),(0, 3),(0, 4),(0, 5),(0, 6),(0, 7),(0, 8),(1, 0),(1, 3),(1, 6),(2, 0),(1, 1)]
ind = np.array(indices)
idx = np.ones(arr.shape[0:2],dtype= bool) # here I assume the shape
idx[ind[:,0],ind[:,1]] = False
print(arr[idx])

Will print the array you want

Edit: The main problem is that, even with a nice mask like

indices = [(0, 0),(0, 1),(0, 2),(0, 3),(0, 4),(0, 5),(0, 6),(0, 7),(0, 8),(1, 0),(1, 3),(1, 6),(2, 0),(1, 1)]
im,jm = zip(*indices)
idx = np.ones_like(arr,dtype=bool)
idx[im,jm] = False

The number of "rows" you want to take out varies in the different entries of the first dimension. At first your array is of dimension (3,7,2). Then, along the first dimensionyou want to delete different numbers of rows for all 3 subarrays, and thus you can't reshape it into something of similar shape ( like (3,5,2) or whatever). That is also the main problem with trying to use delete here, I believe. So even when you apply:

newarr = arr[idx]

the array will contain the elements you want, but will be flattened. If you can make out a rule for proper reshaping, for example you always know the size of the innermost arrays (and those are the ones that will be deleted) which is in this case 2, you could use the mask and do

newarr = arr[idx].reshape(-1,2)

for a more general solution

edited Feb 2, 2018 at 19:53

answered Feb 2, 2018 at 18:38

Banana

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3 Comments

RPT Over a year ago

Thanks, for the answer. It works in my case. But since it is not the healthiest way, I will wait for maybe another answer or two before accepting it.

Banana Over a year ago

well sure, but what do you mean by healthiest?

RPT Over a year ago

I mean scalability. Maybe I would have different array shapes

Pratik Kumar · Accepted Answer · 2018-02-02 19:06:43Z

0

why don't you convert your array arr into a list first then delete the elements:

a=arr.tolist()
for i in indexes:
  x,y=i
  del(a[x][y])

answered Feb 2, 2018 at 19:06

Pratik Kumar

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