获取多维 NumPy 数组中最大值的位置

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最佳答案

The argmax() method should help.

Update

(After reading comment) I believe the argmax() method would work for multi dimensional arrays as well. The linked documentation gives an example of this:

>>> a = array([[10,50,30],[60,20,40]])
>>> maxindex = a.argmax()
>>> maxindex
3

Update 2

(Thanks to KennyTM's comment) You can use unravel_index(a.argmax(), a.shape) to get the index as a tuple:

>>> from numpy import unravel_index
>>> unravel_index(a.argmax(), a.shape)
(1, 0)

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(edit) I was referring to an old answer which had been deleted. And the accepted answer came after mine. I agree that argmax is better than my answer.

Wouldn't it be more readable/intuitive to do like this?

numpy.nonzero(a.max() == a)
(array([1]), array([0]))

Or,

numpy.argwhere(a.max() == a)

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You can simply write a function (that works only in 2d):

def argmax_2d(matrix):
maxN = np.argmax(matrix)
(xD,yD) = matrix.shape
if maxN >= xD:
x = maxN//xD
y = maxN % xD
else:
y = maxN
x = 0
return (x,y)

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An alternative way is change numpy array to list and use max and index methods:

List = np.array([34, 7, 33, 10, 89, 22, -5])
_max = List.tolist().index(max(List))
_max
>>> 4