I have an n*n array, and I want to find the min in the array, and get the index of the min in [x,y] format
Of course, this can be done using for loops and using temporary variables, but I am looking for a more sophisticated process to do this.
Example -
[[1,2,8],
[7,4,2],
[9,1,7],
[0,1,5],
[6,-4,3]]
I should get the following output -
Output-
Min = -4
Index = [4,1]
Can I implement something similar?
TIA.
Global minimum value and index
Flatten the array, get the argmin index. Get the corresponding row-col indices from it with np.unravel_index. Also, index into the flattened array with the earlier obtained flattened argmin index for the minimum value.
def smallest_val_index(a):
idx = a.ravel().argmin()
return a.ravel()[idx], np.unravel_index(idx, a.shape)
Sample run -
In [182]: a
Out[182]:
array([[ 1, 2, 8],
[ 7, 4, 2],
[ 9, 1, 7],
[ 0, 1, 5],
[ 6, -4, 3]])
In [183]: val, indx = smallest_val_index(a)
In [184]: val
Out[184]: -4
In [185]: indx
Out[185]: (4, 1)
Global maximum value and index
Similarly, to get the global maximum value, use argmax -
def largest_val_index(a):
idx = a.ravel().argmax()
return a.ravel()[idx], np.unravel_index(idx, a.shape)
Sample run -
In [187]: a
Out[187]:
array([[ 1, 2, 8],
[ 7, 4, 2],
[ 9, 1, 7],
[ 0, 1, 5],
[ 6, -4, 3]])
In [188]: largest_val_index(a)
Out[188]: (9, (2, 0))
