如何从二维数组+最大值索引中获取每一列的最大值

Question

I have for example

A = [[1 2 3 4 5]
     [2 4 5 8 7]
     [9 8 4 5 2]
     [1 2 4 7 2]
     [5 9 8 7 6]
     [1 2 5 4 3]]

So the shape of A = (5,6) What I want is now the max of each column and return the result as eg:

A = [[9 9 8 8 7]] with as shape (5,1)

And at the same time I would like to receive the index of the max value from each column.

Is this possible? I don't find immediatly the sollution within the np.array basic doc.

Answer 1

You could use ndarray.max() .

The axis keyword argument describes what axis you want to find the maximum along.

keepdims=True lets you keep the input's dimensions.

To get the indizes of the maxima in the columns, you can use the ndarray.argmax() function. You can also pass the axis argument ot this function, but there is no keepdims option.

In both commands axis=0 describes the columns, axis=1 describes the rows. The standard value axis=None would search the maximum in the entire flattened array.

Example:

import numpy as np

A = np.asarray(
    [[1, 2, 3, 4, 5],
     [2, 4, 5, 8, 7],
     [9, 8, 4, 5, 2],
     [1, 2, 4, 7, 2],
     [5, 9, 8, 7, 6],
     [1, 2, 5, 4, 3]])
print(A)

max = A.max(axis=0, keepdims=True)
max_index = A.argmax(axis=0)

print('Max:', max)
print('Max Index:', max_index)

This prints:

[[1 2 3 4 5]
 [2 4 5 8 7]
 [9 8 4 5 2]
 [1 2 4 7 2]
 [5 9 8 7 6]
 [1 2 5 4 3]]
Max: [[9 9 8 8 7]]
Max Index: [2 4 4 1 1]

Answer 2

you can use numpy as well.

Example:

import numpy as np

A = [[1, 2, 3, 4, 5],
 [2, 4, 5, 8, 7],
 [9, 8, 4, 5, 2],
 [1, 2, 4, 7, 2],
 [5, 9, 8, 7, 6],
 [1, 2, 5, 4, 3]]

print(A)

A=np.array(A)

print(A.max(axis=0))