argsort for a multidimensional ndarray

Solution:

>>> a[np.arange(np.shape(a)[0])[:,np.newaxis], np.argsort(a)]array([[1, 2, 3],       [2, 8, 9]])

You got it right, though I wouldn't describe it as cheating the indexing.

Maybe this will help make it clearer:

In [544]: i=np.argsort(a,axis=1)In [545]: iOut[545]: array([[1, 2, 0],       [2, 0, 1]])

i is the order that we want, for each row. That is:

In [546]: a[0, i[0,:]]Out[546]: array([1, 2, 3])In [547]: a[1, i[1,:]]Out[547]: array([2, 8, 9])

To do both indexing steps at once, we have to use a 'column' index for the 1st dimension.

In [548]: a[[[0],[1]],i]Out[548]: array([[1, 2, 3],       [2, 8, 9]])

Another array that could be paired with i is:

In [560]: j=np.array([[0,0,0],[1,1,1]])In [561]: jOut[561]: array([[0, 0, 0],       [1, 1, 1]])In [562]: a[j,i]Out[562]: array([[1, 2, 3],       [2, 8, 9]])

If i identifies the column for each element, then j specifies the row for each element. The [[0],[1]] column array works just as well because it can be broadcasted against i.

I think of

np.array([[0],          [1]])

as 'short hand' for j. Together they define the source row and column of each element of the new array. They work together, not sequentially.

The full mapping from a to the new array is:

[a[0,1]  a[0,2]  a[0,0] a[1,2]  a[1,0]  a[1,1]]

def foo(a):    i = np.argsort(a, axis=1)    return (np.arange(a.shape[0])[:,None], i)In [61]: foo(a)Out[61]: (array([[0],        [1]]), array([[1, 2, 0],        [2, 0, 1]], dtype=int32))In [62]: a[foo(a)]Out[62]: array([[1, 2, 3],       [2, 8, 9]])

I found the answer here, with someone having the same problem. They key is just cheating the indexing to work properly...

>>> a[np.arange(np.shape(a)[0])[:,np.newaxis], np.argsort(a)]array([[1, 2, 3],       [2, 8, 9]])

The above answers are now a bit outdated, since new functionality was added in numpy 1.15 to make it simpler; take_along_axis (https://docs.scipy.org/doc/numpy-1.15.1/reference/generated/numpy.take_along_axis.html) allows you to do:

>>> a = np.array([[3,1,2],[8,9,2]])>>> np.take_along_axis(a, a.argsort(axis=-1), axis=-1)array([[1 2 3]       [2 8 9]])