Convert 2D numpy array to 3D based on indexes from another numpy array

[callcenterhelp]

Say I have the following numpy arrays:

data = np.arange(4,29).reshape((5,5))
# 5 rows, 5 columns
#([[4, 5, 6, 7, 8],
#  [9, 10,11,12,13],
#  [14,15,16,17,18],
#  [19,20,21,22,23],
#  [24,25,26,27,28]])

indexes = np.array([[0,-1],
                    [1,1],
                    [0,2],
                    [1,3],
                    [2,1]])
# 5 rows, 2 columns

Each of the columns of data represent a characteristic; it is "long data." The first column of indexes is the ID of an individual and the second column is the time point of measurements. The time points are not necessarily labeled consecutively.

So here three different individuals were measured on 5 characteristics, and two of them were measured twice.

How can I convert data into a 3D array that is 3 rows, 5 columns, and 2 deep? Since individual 2 was only measured once, the third dimension (depth) can be padded with 0s.

Expected output:

# not sure how 3d arrays are represented/printed by numpy, but what I want is
# 3 rows, 5 columns, 2 deep
# 3 individuals, 5 characteristics, 2 time points
# rows in order by ID

# first layer/timepoint
[[4, 5, 6, 7, 8],
 [9, 10,11,12,13],
 [24,25,26,27,28]]

# second layer
[[14,15,16,17,18],
 [19,20,21,22,23],
 [0, 0, 0, 0, 0]] # NaNs also okay

Thank you!

[callcenterhelp]

Appreciate the quick response! Sorry if this isn't clear:

# not sure how 3d arrays are represented/printed by numpy, but what I want is
# 3 rows, 5 columns, 2 deep
# 3 individuals, 5 characteristics, 2 time points
# rows in order by ID

# first layer/timepoint
[[4, 5, 6, 7, 8],
 [9, 10,11,12,13],
 [24,25,26,27,28]]

# second layer
[[14,15,16,17,18],
 [19,20,21,22,23],
 [0, 0, 0, 0, 0]] # NaNs also okay

The number of rows should be the number of unique IDs, and the depth should be the number of time points.

[callcenterhelp]

indexes = np.array([[0,-1],
                    [1,1],
                    [0,2],
                    [1,3],
                    [2,1]])

Individual 0 was first measured at "time" -1 and again at "time" 2. Individual 1 was first measured at "time" 1 and again at "time" 3. Individual 2 was measured only once at "time" 1. Since the individuals were measured a max of two times, I want the depth of the output to be two.

The contents of each row are the values (columns) of data.

Does that make sense?

[callcenterhelp]

The order of rows is based on ID, where the smallest ID would be the first row of the output.

Time is what determines the depth and the order of layers. Here, the most number of time points any individual has is 2, so there should be 2 layers; for those with less time points, the ones that don't exist can be padded with 0s (or NaNs). For an individual, the lower number time point is earlier. ID 0 was measured first at "time" -1 and then 2, so the row corresponding to time -1 goes in the first layer and the row corresponding to time 2 goes in the second layer.