Create new 2d array of the argmax from two 2d np.arrays element-wise

Question

Say I have, as an arbitrary example, two 2d numpy arrays, X and Y where

    X = np.array([[0,1,2,3],
                  [4,5,6,7]]) 
    Y = np.array([[1,2,3,4],
                  [1,1,7,3]])

I want to create a new 2d numpy array, Z, that is the argmax of X,Y element-wise so Z would be, in this example:

    Z = np.array([[1,2,3,4],
                  [4,5,7,7]])

I've tried variations of the following none return the intended result

    np.array([(np.argmax(X,Y))]) --> error

I know I can do this simply by using a nested for loop but that isn't very efficient for very large datasets. Is there an efficient, numpy-specific, way to create a new 2d array (Z, in the example above) composed of the argmax by element from two 2d arrays (X and Y, in the example above)?

score 2 · Accepted Answer · answered Jun 25 '21 at 20:55

2

You're looking for np.maximum:

>>> np.maximum(X, Y)

array([[1, 2, 3, 4],
       [4, 5, 7, 7]])

which compares the arrays element-wise and returns the maximum for each of them.

answered Jun 25 '21 at 20:55

Mustafa Aydın

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1

Thank you! This is very helpful to know. – Zuckerbrenner Jun 25 '21 at 21:46

score 1 · Answer 2 · answered Jun 25 '21 at 20:49

Use numpy.where:

Z = np.where(X > Y, X, Y)

Here, the first argument X > Y compares X and Y element by element, and returns a boolean array of the comparison. Then we use the boolean array to build Z: if the element at an index is True, it uses the value from X, and if it is False it uses the value from Y.

Create new 2d array of the argmax from two 2d np.arrays element-wise

2 Answers2