Suppose I have a numpy array from which I want to remove a specific element.

# data = np.array([ 97  32  98  32  99  32 100  32 101])
# collect indices where the element locate 
indices = np.where(data==32)
without_32 = np.delete(data, indices)
# without_32 become [ 97  98  99 100 101]

Now, suppose I want to restore the array (As I already have the indices where I should put the value 32).

restore_data = np.insert(without_32, indices[0], 32)

But it gives IndexError: index 10 is out of bounds for axis 0 with size 9. IS there other way to implement that?

update

It seems after delete the element I need some adjust for the indices like

restore_data = np.insert(without_32, indices[0]-np.arange(len(indices[0])), 32)

But Can I generalize this? Like not only 32 but also trace 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47. I mean I want to trace the same way for 32-47 in a efficient way.

CodePudding user response：

My alternative:

#define a mask 
mask = data==32
mask #array([False,  True, False,  True, False,  True, False,  True, False])

#filter
without_32 = data[~mask]
without_32 #array([ 97,  98,  99, 100, 101])

Then if you want to have the original data:

restore_data = np.ones_like(mask, dtype=int)*32
restore_data[~mask] = without_32
restore_data

output:

array([ 97,  32,  98,  32,  99,  32, 100,  32, 101])

In practice you are generating a constant array of 32 with length equal to mask (and obviously to data) and then you are filling the position in which mask is False (the positions in which data!=32) with the without_32 array

UPDATE

In order to answer to your update:

data = np.random.randint(20, 60, size=20)
#array([47, 39, 29, 45, 21, 44, 48, 27, 21, 25, 47, 59, 58, 53, 46, 36, 34, 57, 36, 54])

mask = (data>=32)&(data<=47) #the values you want to remove

clean_array = data[~mask] #data you want to retain
removed_data = data[mask] #data you want to remove

Now you can del data, you can do whatever you want with clean_array, and when you need to reconstruct the original array you just:

restore_data = np.zeros_like(mask, dtype=int)
restore_data[~mask] = clean_array
restore_data[mask] = removed_data
#array([47, 39, 29, 45, 21, 44, 48, 27, 21, 25, 47, 59, 58, 53, 46, 36, 34, 57, 36, 54])