Iterating numpy array to find max value within subarray leaving the row index

Question

I wanted to find the max of 2D array along the axis=0 and I don't want to include the value at the row-index. I'm not happy with this solution because I need to run this on a million of rows and I don't want to use for-loop here. I tried numpy.argmax but it calculates the max value of the row including the value at the row index.

My 2D Array

Arry=([[1,   0.5, 0.3,   0,    0.2],
       [0,   1,   0.2,   0.8,  0],
       [0,   1,   1,     0.3,  0],
       [0,   0,   0,     1,    0]])

Expected Output

[1, 3, 1]

The first row [1, 0.5, 0.3, 0, 0.2] has max value at index 1 i.e. 0.5 since value 1 is at row index 0, Similarly in the second row max value is 0.8 i.e. index 3 and the 4th row doesn't have any max value since all are zero

My Code

import numpy as np

for idx,subarry in enumerate(Arry):
    newlist=np.delete(subarry, idx)
    idx_min=min(np.where(subarry==np.max(newlist))[0])
    if idx_min != 0: min_elem_idx.append(idx_min)

print(min_elem_idx)
[1, 3, 1]

I am looking for a Pythonic way to achieve this without using the for loop

Nathaniel · Accepted Answer · 2019-03-19 07:00:04Z

3

This should do the trick:

a = np.array([[1,   0.5, 0.3,   0,    0.2],
              [0,   1,   0.2,   0.8,  0],
              [0,   1,   1,     0.3,  0],
              [0,   0,   0,     1,    0]])

# Create an array of ones the same size as a
b = np.ones_like(a)

# Fill the diagonal of b with NaN
np.fill_diagonal(b, np.nan)

# Multiply the arrays in order to remove the index column from the max
c = a*b

# Find the index of the max value of every row (excluding the index value)
np.nanargmax(c, axis=1)

Output:

array([1, 3, 1, 0])

In order to filter out cases where every value is zero (and therefore "doesn't have a max," as you define it), you would have to do some additional work.

edited Mar 19, 2019 at 7:00

answered Mar 19, 2019 at 6:53

Nathaniel

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