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Numpy: applying argsort to an array

The function argsort()returns an index matrix that can be used to index the original array so that the result matches the sort()result.

Is there any way to apply these indexes? I have two arrays: one is the array used to get the sort order, and the other is some related data.

I would like to calculate assoc_data[array1.argsort()], but this does not work.

Here is an example:

z=array([1,2,3,4,5,6,7])
z2=array([z,z*z-7])
i=z2.argsort()

z2=array([[ 1,  2,  3,  4,  5,  6,  7],
          [-6, -3,  2,  9, 18, 29, 42]])
i =array([[1, 1, 1, 0, 0, 0, 0],
          [0, 0, 0, 1, 1, 1, 1]])

I would like to apply to z2 (or another array with related data), but I'm not sure how to do this.

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3 answers

This is probably too much, but it will work in the following case:

import numpy as np
axis = 0
index = list(np.ix_(*[np.arange(i) for i in z2.shape]))
index[axis] = z2.argsort(axis)
z2[index]

# Or if you only need the 3d case you can use np.ogrid.

axis = 0
index = np.ogrid[:z2.shape[0], :z2.shape[1], :z2.shape[2]]
index[axis] = z2.argsort(axis)
z2[index]
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, numpyology.

>>> def apply_argsort(a, axis=-1):
...     i = list(np.ogrid[[slice(x) for x in a.shape]])
...     i[axis] = a.argsort(axis)
...     return a[i]
... 
>>> a = np.array([[1,2,3,4,5,6,7],[-6,-3,2,9,18,29,42]])
>>> apply_argsort(a,0)
array([[-6, -3,  2,  4,  5,  6,  7],
       [ 1,  2,  3,  9, 18, 29, 42]])

, , . .

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, .

In [274]: z2[i,range(z2.shape[1])]
Out[274]:
array([[-6, -3,  2,  4,  5,  6,  7],
       [ 1,  2,  3,  9, 18, 29, 42]])
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