将 numpy 数组行列值设置为另一个数组的列值

Question

I am trying to select rows with values in their first two columns matching with those of another array and set their last columns values to the last column values of that array.

I've specifically tried to do something like this:

import numpy as np

array_1 = np.zeros((10,3), order = "F", dtype = int)

array_2 = np.zeros((5,3), order = "F", dtype = int)

array_1[:5][:,0] = 1
array_1[:5][:,1] = 1

array_2[:,0] = 1
array_2[:,1] = 1
array_2[:,2] = 3

array_1[:,2][np.where((array_1[:,0] == array_2[:,0]) & (array_1[:,1] == array_2[:,1]))] = array_2[:,2]

The objective is to set the value of column 2 of rows matching to 3. (same as array_2)

I've also tried to do the same thing with masking.

It always complaints about a shape mismatch, so I assume I am doing something wrong since the objective is only to change the value of one particular column, for those row matches.

What is the way to do this?

Answer 1

I was able to solve it like this:

import numpy as np

array_1 = np.zeros((10,3), order = "F", dtype = int)

array_2 = np.zeros((5,3), order = "F", dtype = int)

array_1[:5][:,0] = 1
array_1[:5][:,1] = 1

array_2[:,0] = 1
array_2[:,1] = 1
array_2[:,2] = 3

m = (array_1[:,:2][:,None] == array_2[:,:2]).all(-1).any(1)

array_1[:,2][m] = array_2[:,2]