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. So please, run them on your systems to explore the working. Comment More infoAdvertise with us Next Article numpy.nanargmin() in Python M Mohit Gupta Improve Article Tags : Python Python-numpy Python numpy-Sorting Searching Practice Tags : python Similar Reads numpy.nanmin() in Python numpy.nanmin()function is used when to returns minimum value of an array or along any specific mentioned axis of the array, ignoring any Nan value. Syntax : numpy.nanmin(arr, axis=None, out=None) Parameters : arr :Input array. axis :Axis along which we want the min value. Otherwise, it will consider 2 min read numpy.nanargmax() in Python 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]Al 2 min read numpy.nanvar() in Python numpy.nanvar(arr, axis = None) : Compute the variance of the given data (array elements) along the specified axis(if any), while ignoring NaN values. Example : x = 1 1 1 1 1 Standard Deviation = 0 . Variance = 0 y = 9, 2, 5, 4, 12, 7, 8, 11, 9, 3, 7, 4, 12, 5, 4, 10, 9, 6, 9, 4 Step 1 : Mean of dist 3 min read numpy.nansum() in Python numpy.nansum()function is used when we want to compute the sum of array elements over a given axis treating Not a Numbers (NaNs) as zero. Syntax : numpy.nansum(arr, axis=None, dtype=None, out=None, keepdims='no value') Parameters : arr : [array_like] Array containing numbers whose sum is desired. If 3 min read numpy.nanprod() in Python numpy.nanprod() function is used when we want to compute the product of array elements over a given axis treating NaNs as ones. One is returned for slices that are all-NaN or empty. Syntax : numpy.nanprod(arr, axis=None, dtype=None, out=None, keepdims='class numpy._globals._NoValue'). Parameters : a 2 min read Like