[FIXED] How to find the maximum value in one array, which corresponds to unique values/points in another array?

Issue

I have a 2D matrix, such as:

import numpy as np
arr = np.array([[0,0,1],[0,1,0], [0,1,0], [0,0,1]])

Where arr is:

[[0 0 1]
 [0 1 0]
 [0 1 0]
 [0 0 1]]

I need to find the largest row number in each column where the value is 1. Or if there is none, then I would need to know that as well.

In the above example, through some array or encoding, I would need to know that:

 - Column 0 : no match
 - Column 1 : Row 2
 - Column 2 : Row 3

I have done:

result = np.where(arr == 1)

Where result is:

(array([0, 1, 2, 3], dtype=int64), array([2, 1, 1, 2], dtype=int64))

The first array indicates the row and the second array indicates the column.

So, this tells me that:

• column 2 has a 1 at row 0 and row 3
• column 1 has a 1 at row 1 and row 2

I can also infer that column 0 has no 1's so that is fine.

So, I would need a way to find the largest value (row number) in array 0 corresponding to unique values in array 1.

So, an example of what I would need to return is:

 (array([2,3], dtype=int64), array([1,2], dtype=int64))

or something like that, where I can know that column 1 had a value of 1 occurring at a max row of 2. Column 2 had a value of 1 occurring at a max row of 3 etc. The first array in that example is the maximum row number corresponding (by index) to the second array which indicates the column.

Solution

The columns where array have 1 in it can be achieved by np.where and np.any(). Then we can mask columns and rows where 1 existed. So, by reversing the masked array as below you can get the result:

cols = np.where((arr == 1).any(0))
mask = (arr == 1)[:, cols[0]]
rows = mask.shape[0] - np.argmax(mask[::-1, :], axis=0) - 1

# cols --> [1 2]
# rows --> [2 3]

Answered By - Ali_Sh