numpy.nanargmin() in Python Last Updated : 08 Mar, 2024 Comments Improve Suggest changes Like Article Like Report 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. 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