Argmax

import numpy as np

a = np.array(
    [
        [1,2,44,7], 
        [9,88,6,45], 
        [19,76,3,4]
    ]
)

a

array([[ 1,  2, 44,  7],
       [ 9, 88,  6, 45],
       [19, 76,  3,  4]])

a.size

12

a.shape

(3, 4)

a.ndim

2

np.argmax(a)

5

a.flatten()

array([ 1,  2, 44,  7,  9, 88,  6, 45, 19, 76,  3,  4])

Note:

argmax Returns the indices of the maximum values along an axis.

The np.argmax function by default works along the flattened array, unless you specify an axis

np.argmin(a) # returns the index of the minimum value

0

np.argmax(a, axis=0) # index of numbers 19, 88, 44, 45

array([2, 1, 0, 1])

Note

np.argmax(a, axis=0) returns the index of the maximum value in each of the four columns.

np.argmax(a, axis=1)

array([2, 1, 1])

Note:

That means np.argmax(a, axis=1) returns [2, 1, 1] because a has three rows. The index of the maximum value in the first row is 2 (44), the index of the maximum value of the second and third rows is 1 (88, 76)

b = np.array(
    [
        [2, 4], 
        [5, 3] 
       
    ]
)

b

array([[2, 4],
       [5, 3]])

b.size

4

b.shape

(2, 2)

b.flatten()

array([2, 4, 5, 3])

np.argmax(b)

2

np.argmax(b, axis=0)

array([1, 0])

np.argmax(b, axis=1)

array([1, 0])

More:

https://stackoverflow.com/questions/28697993/numpy-what-is-the-logic-of-the-argmin-and-argmax-functions