Numpy Index Of Maximum

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Numpy 2

Def max indices arr k Returns the indices of the k first largest elements of arr in descending order in values assert k Use the axis keyword to get the indices of maximum and minimum values along a specific axis: >>> np . argmax ( a , axis = 0 ) array([[1, 1, 1, 1, 1], [1, 1, 1, 1, 1], [1, 1, 1, 1, 1]]) >>> np . argmax ( a , axis = 1 ) array([[2, 2, 2, 2, 2], [2, 2, 2, 2, 2]]) >>> np . argmax ( a , axis = 2 ) array([[4, 4, 4], [4, 4, 4]]) >>> np . argmin ( a .

numpy

NumPy

Numpy Index Of MaximumNumpy has an argmax function that returns just that, although you will have to deal with the nan s manually. nan s always get sorted to the end of an array, so with that in mind you can do: a = np.random.rand(10000) a[np.random.randint(10000, size=(10,))] = np.nan. a = a.reshape(100, 100) def nanargmax(a): idx = np.argmax(a, axis=None) Axis 1 the index of the max element per row will be returned The following code will help you to better understand import numpy as np a np array 1 2 3 4 3 1 This will print the index of the max value for each column print a argmax axis 0 output 1 1 0 This will print the index of the max value for each row print a argmax