Numpy: argsort does not work for multidimensional arrays

argsort() is working fine, but indexing doesn't work the way that you are expecting it to.

http://docs.scipy.org/doc/numpy/reference/arrays.indexing.html#advanced-indexing

Try this:

a = np.random.random([5,5])
i = np.arange(len(a))[:, np.newaxis]
j = np.argsort(a, axis=1)
a_sorted = a[i, j]
np.sort(a, axis=1)

I was confused in the same way. The documentation specifically states,

Returns
index_array : ndarray, int
Array of indices that sort a along the specified axis.
In other words, a[index_array] yields a sorted a.

Which only applies to a 1D array. Should the documentation be extended to specify that this is not the case for ND arrays?

@lzkelley Yes, a patch to improve the documentation would be very welcome.

@shoyer cool, I'll make a PR for it and update

It would be good if there were an example of how to actually use the output to sort the N-D array.

It would also be good if there were a more readable solution than a[np.arange(np.shape(a)[0])[:,np.newaxis], np.argsort(a)]

Maybe @seberg could make it one of his indexing function additions.

I don't think it fits indexing very obviously, a pick function or so is probably an easier match.

+1 for a new function.

Isn't the entire purpose of argsort (as apposed to sort) to be used for indexing?

argsort supplies the indexes to sort along an axis, but actually sorting along that axis using the indexes seems needlessly verbose. Unless there's a simpler way I'm not aware of.

https://github.com/numpy/numpy/blob/5becd12914d0402967205579d6f59a98151e0d98/doc/neps/indexing.rst#related-questions

a pick function or so is probably an easier match.

This is #8708