numpy.argmax() in Python Last Updated : 08 Mar, 2024 Comments Improve Suggest changes Like Article Like Report The numpy.argmax() function returns indices of the max element of the array in a particular axis. Syntax : numpy.argmax(array, axis = None, out = None) Parameters : array : Input array to work on axis : [int, optional]Along a specified axis like 0 or 1 out : [array optional]Provides a feature to insert output to the out array and it should be of appropriate shape and dtype 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 argmax() import numpy as geek # Working on 2D array array = geek.arange(12).reshape(3, 4) print("INPUT ARRAY : \n", array) # No axis mentioned, so works on entire array print("\nMax element : ", geek.argmax(array)) # returning Indices of the max element # as per the indices print("\nIndices of Max element : ", geek.argmax(array, axis=0)) print("\nIndices of Max element : ", geek.argmax(array, axis=1)) Output : INPUT ARRAY : [[ 0 1 2 3] [ 4 5 6 7] [ 8 9 10 11]] Max element : 11 Indices of Max element : [2 2 2 2] Indices of Max element : [3 3 3] Code 2 : Python # Python Program illustrating # working of argmax() import numpy as geek # Working on 2D array array = geek.random.randint(16, size=(4, 4)) print("INPUT ARRAY : \n", array) # No axis mentioned, so works on entire array print("\nMax element : ", geek.argmax(array)) # returning Indices of the max element # as per the indices ''' [[ 0 3 8 13] [12 11 2 11] [ 5 13 8 3] [12 15 3 4]] ^ ^ ^ ^ 12 15 8 13 - element 1 3 0 0 - indices ''' print("\nIndices of Max element : ", geek.argmax(array, axis = 0)) ''' ELEMENT INDEX ->[[ 0 3 8 13] 13 3 ->[12 11 2 11] 12 0 ->[ 5 13 8 3] 13 1 ->[12 15 3 4]] 15 1 ''' print("\nIndices of Max element : ", geek.argmax(array, axis = 1)) Output : INPUT ARRAY : [[ 0 3 8 13] [12 11 2 11] [ 5 13 8 3] [12 15 3 4]] Max element : 15 Indices of Max element : [1 3 0 0] Indices of Max element : [3 0 1 1] Code 3 : Python # Python Program illustrating # working of argmax() import numpy as geek # Working on 2D array array = geek.arange(10).reshape(2, 5) print("array : \n", array) array[0][1] = 6 print("\narray : \n", array) # Returns max element print("\narray : ", geek.argmax(array)) # First occurrence of an max element is given print("\nMAX ELEMENT INDICES : ", geek.argmax(array, axis = 0)) Output : array : [[0 1 2 3 4] [5 6 7 8 9]] array : [[0 6 2 3 4] [5 6 7 8 9]] array : 9 MAX ELEMENT INDICES : [1 0 1 1 1] Note : These codes won’t run on online IDE’s. Please run them on your systems to explore the working. Comment More infoAdvertise with us Next Article numpy.argmax() in Python M Mohit Gupta_OMG Improve Article Tags : Python Python-numpy Python numpy-Sorting Searching Practice Tags : python Similar Reads numpy.amax() in Python The numpy.amax() method returns the maximum of an array or maximum along the axis(if mentioned). Syntax: numpy.amax(arr, axis = None, out = None, keepdims = <class numpy._globals._NoValue>) Parameters - arr : [array_like] input dataaxis : [int or tuples of int] axis along which we want the ma 2 min read numpy.argmin() in Python The numpy.argmin() method returns indices of the min element of the array in a particular axis. Syntax : numpy.argmin(array, axis = None, out = None) Parameters : array : Input array to work on axis : [int, optional]Along a specified axis like 0 or 1 out : [array optional]Provides a feature to inser 2 min read numpy.fmax() in Python numpy.fmax() function is used to compute element-wise maximum of array elements. This function compare two arrays and returns a new array containing the element-wise maxima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first 2 min read numpy.amin() in Python The numpy.amin() function returns minimum of an array or minimum along axis(if mentioned). Syntax : numpy.amin(arr, axis = None, out = None, keepdims = <class numpy._globals._NoValue>) Parameters : arr : [array_like]input dataaxis : [int or tuples of int]axis along which we want the min value. 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.nanargmin() in Python 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]A 2 min read numpy.fmin() in Python numpy.fmin() function is used to compute element-wise minimum of array elements. This function compare two arrays and returns a new array containing the element-wise minima. If one of the elements being compared is a NaN, then the non-nan element is returned. If both elements are NaNs then the first 2 min read numpy.greater() in Python The numpy.greater() checks whether x1 is greater than x2 or not. Syntax : numpy.greater(x1, x2[, out]) Parameters : x1, x2 : [array_like]Input arrays. If x1.shape != x2.shape, they must be broadcastable to a common shape out : [ndarray, boolean]Array of bools, or a single bool if x1 and x2 are scala 2 min read numpy.floor() in Python The numpy.floor() function returns the largest integer less than or equal to each element in the input array. It effectively rounds numbers down to the nearest whole number. Let's understand with an example:Pythonimport numpy as np a = [0.5, 1.5, 2.5, 3, 4.5, 10.1] res = np.floor(a) print("Floored:" 1 min read 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 Like