The numpy.nanargmax() function returns indices of the max 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.nanargmax(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
Output :
INPUT ARRAY 1 : [nan, 4, 2, 3, 1] Indices of max in array1 : 1 INPUT ARRAY 2 : [[ nan 4.] [ 1. 3.]] Indices of max in array2 : 1 Indices at axis 1 of array2 : [1 1]
Code 2: Comparing working of argmax and nanargmax
Python
# Python Program illustrating # working of nanargmax() import numpy as geek
# Working on 2D array array = ( [[ 8 , 13 , 5 , 0 ],
[ 16 , geek.nan, 5 , 3 ],
[geek.nan, 7 , 15 , 15 ],
[ 3 , 11 , 4 , 12 ]])
print ( "INPUT ARRAY : \n" , array)
# returning Indices of the max element # as per the indices ''' [[ 8 13 5 0]
[ 16 2 5 3]
[10 7 15 15]
[ 3 11 4 12]]
^ ^ ^ ^
''' print ( "\nIndices of max using argmax : " , geek.argmax(array, axis = 0 ))
print ( "\nIndices of max using nanargmax : : " , geek.nanargmax(array, axis = 0 ))
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Output :
INPUT ARRAY : [[8, 13, 5, 0], [16, nan, 5, 3], [nan, 7, 15, 15], [3, 11, 4, 12]] Indices of max using argmax : [2 1 2 2] Indices of max using nanargmax : : [1 0 2 2]
Note :
These codes won’t run on online IDE’s. So please, run them on your systems to explore the working.