在numpy中为二维数组选择索引 [英] Selecting indices for a 2d array in numpy

问题描述

这在1维上效果很好:

# This will sort bar by the order of the values in foo
(Pdb) bar = np.array([1,2,3])
(Pdb) foo = np.array([5,4,6])
(Pdb) bar[np.argsort(foo)]
array([2, 1, 3])

但是我该如何在两个维度上做到这一点? Argsort效果很好，但是选择不再起作用:

But how do I do that in two dimensions? Argsort works nicely, but the select no longer works:

(Pdb) foo = np.array([[5,4,6], [9,8,7]])
(Pdb) bar = np.array([[1,2,3], [1,2,3]])
(Pdb)  bar[np.argsort(foo)]
*** IndexError: index (2) out of range (0<=index<=1) in dimension 0
(Pdb) 

我希望它能输出:

array([[2, 1, 3], [3, 2, 1]])

有什么线索怎么做?

谢谢！ /YGA

take()似乎做对了，但实际上它只从第一行中提取元素(超级混乱).

take() would seem to do the right thing, but it really only takes elements from the first row (super confusing).

您可以看到，如果我更改bar的值:

You can see that if I change the values of bar:

(Pdb) bar = np.array([["1","2","3"], ["A", "B", "C"]])
(Pdb) bar.take(np.argsort(foo))
array([['2', '1', '3'],
       ['3', '2', '1']], 
      dtype='|S1')
(Pdb) 

推荐答案

bar.take(np.argsort(foo))产生了所需的输出，因此您应该查看其

bar.take(np.argsort(foo)) produced your desired output, so you should take a look at its documentation to make sure it actually does what you think you want.

尝试一下:bar.take(np.argsort(foo.ravel()).reshape(foo.shape))

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