[FIXED] Combine array of indices with array of values

Issue

I have an array in the following form where the first two columns are supposed to be indices of a 2-dimensional array and the following columns are arbitrary values.

data = np.array([[ 0. ,  1. , 48. ,  4. ],
                 [ 1. ,  2. , 44. ,  4.4],
                 [ 1. ,  1. , 34. ,  2.3],
                 [ 0. ,  2. , 55. ,  2.2],
                 [ 0. ,  0. , 42. ,  2. ],
                 [ 1. ,  0. , 22. ,  1. ]])

How do I combine the indices data[:,:2] with their values data[:,2:] such that the resulting array is accessible by the indices in the first two columns.

In my example that would be:

result = np.array([[[42. ,  2. ], [48. ,  4. ], [55. ,  2.2]],
                   [[22. ,  1. ], [34. ,  2.3], [44. ,  4.4]]])

I know that there is a trivial solution using python loops. But performance is a concern since I'm dealing with a huge amount of data. Specifically it's output of another program that I need to process.

Maybe there is a relatively trivial numpy solution as well. But I'm kind of stuck.

If it helps the following can be safely assumed:

• All numbers in the first two columns are whole numbers (although the array consists of floats).
• Every possible index (or rather combinations of indices) in the original array is used exactly once. I.e. there is guaranteed to be exactly one entry of the form [i, j, ...].
• The indices start at 0 and I know the highest indices beforehand.

Edit:

Hmm. I see now how my example is misleading. The truth is that some of my input arrays are sorted, but that's unreliable. So I shouldn't assume anything about the order. I reordered some rows in my example to make it clearer. In case anyone wants to make sense of the answer and comment below: In my original question the array appeared to be sorted by the first two columns.

Solution

find row, column, depth base your data array, then fill like below:

import numpy as np
data = np.array([[ 0. ,  0. , 42. ,  2. ],
                 [ 0. ,  1. , 48. ,  4. ],
                 [ 0. ,  2. , 55. ,  2.2],
                 [ 1. ,  0. , 22. ,  1. ],
                 [ 1. ,  1. , 34. ,  2.3],
                 [ 1. ,  2. , 44. ,  4.4]])

row = int(max(data[:,0]))+1
col = int(max(data[:,1]))+1
depth = len(data[0, 2:])

out = np.zeros([row, col, depth])

out = data[:, 2:].reshape(row,col,depth)
print(out)

Output:

[[[42.   2. ]
  [48.   4. ]
  [55.   2.2]]

 [[22.   1. ]
  [34.   2.3]
  [44.   4.4]]]

Answered By - I'mahdi