The numpy.nanargmin() function returns indices of the min element of the array in a particular axis ignoring NaNs.
The results cannot be trusted if a slice contains only NaNs and Infs.
Syntax:
numpy.nanargmin(array, axis = None)
Parameters :
array : Input array to work on axis : [int, optional]Along a specified axis like 0 or 1
Return :
Array of indices into the array with same shape as array.shape. with the dimension along axis removed.
Code 1 :
Python
# Python Program illustrating # working of nanargmin() import numpy as geek
# Working on 1D array array = [geek.nan, 4 , 2 , 3 , 1 ]
print ( "INPUT ARRAY 1 : \n" , array)
array2 = geek.array([[geek.nan, 4 ], [ 1 , 3 ]])
# returning Indices of the min element # as per the indices ingnoring NaN print ( "\nIndices of min in array1 : " ,
geek.nanargmin(array))
# Working on 2D array print ( "\nINPUT ARRAY 2 : \n" , array2)
print ( "\nIndices of min in array2 : " ,
geek.nanargmin(array2))
print ( "\nIndices at axis 1 of array2 : " ,
geek.nanargmin(array2, axis = 1 ))
|
Output :
INPUT ARRAY 1 : [nan, 4, 2, 3, 1] Indices of min in array1 : 4 INPUT ARRAY 2 : [[ nan 4.] [ 1. 3.]] Indices of min in array2 : 2 Indices at axis 1 of array2 : [1 0]
Code 2 : Comparing working of argmin and nanargmin
Python
# Python Program illustrating # working of nanargmin() import numpy as geek
# Working on 2D array array = ( [[ 8 , 13 , 5 , 0 ],
[ geek.nan, geek.nan, 5 , 3 ],
[ 10 , 7 , 15 , 15 ],
[ 3 , 11 , 4 , 12 ]])
print ( "INPUT ARRAY : \n" , array)
# returning Indices of the min element # as per the indices ''' [[ 8 13 5 0]
[ 0 2 5 3]
[10 7 15 15]
[ 3 11 4 12]]
^ ^ ^ ^
0 2 4 0 - element
1 1 3 0 - indices
''' print ( "\nIndices of min using argmin : " ,
geek.argmin(array, axis = 0 ))
print ( "\nIndices of min using nanargmin : : " ,
geek.nanargmin(array, axis = 0 ))
|
Output :
INPUT ARRAY : [[ 8 13 5 0] [ 0 2 5 3] [10 7 15 15] [ 3 11 4 12]] Indices of min element : [1 1 3 0]
Note :
These codes won’t run on online IDE’s. So please, run them on your systems to explore the working.