numpy 2d array max/argmax

Question

I have a numpy matrix:

>>> A = np.matrix('1 2 3; 5 1 6; 9 4 2') >>> A matrix([[1, 2, 3], [5, 1, 6], [9, 4, 2]])

I'd like to get the index of the maximum value in each row along with the value itself. I can get the indices for the maximums using A.argmax(axis=1), in that case I would get:

>>> indices = A.argmax(axis=1) >>> indices matrix([[2], [2], [0]])

How can I use the 'indices' array to get an array of maximum values for each row in the matrix? Is there any way I can do this more efficiently or in one operation? Is there a function that would return the values along with their row and column coordinates?

Related: Numpy selecting specific column index per row by using a list of indexes — Ashwini Chaudhary
– Ashwini Chaudhary, Commented May 10, 2014 at 17:25

shx2 · Accepted Answer · 2014-05-10 17:42:59Z

You can fancy-index using the indices np.arange(len(A)) on first dimension (since you want a value per row), and your indices (squeezed), which correspond to the index on each row, on the second dimension:

A[np.arange(len(A)) , indices.squeeze()] => matrix([[3, 6, 9]])

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