python - Sorting array of objects by row using custom dtype

Question

I am attempting to sort a some arrays lexicographically by rows. The integer case works perfectly:

>>> arr = np.random.choice(10, size=(5, 3))
>>> arr
array([[1, 0, 2],
       [8, 0, 8],
       [1, 8, 4],
       [1, 3, 9],
       [6, 1, 8]])
>>> np.ndarray(arr.shape[0], dtype=[('', arr.dtype, arr.shape[1])], buffer=arr).sort()
>>> arr
array([[1, 0, 2],
       [1, 3, 9],
       [1, 8, 4],
       [6, 1, 8],
       [8, 0, 8]])

I can also do the sorting with

np.ndarray(arr.shape[0], dtype=[('', arr.dtype)] * arr.shape[1], buffer=arr).sort()

In both cases, the results are the same. However, that is not the case for object arrays:

>>> selection = np.array(list(string.ascii_lowercase), dtype=object)
>>> arr = np.random.choice(selection, size=(5, 3))
>>> arr
array([['t', 'p', 'g'],
       ['n', 's', 'd'],
       ['g', 'g', 'n'],
       ['g', 'h', 'o'],
       ['f', 'j', 'x']], dtype=object)
>>> np.ndarray(arr.shape[0], dtype=[('', arr.dtype, arr.shape[1])], buffer=arr).sort()
>>> arr
array([['t', 'p', 'g'],
       ['n', 's', 'd'],
       ['g', 'h', 'o'],
       ['g', 'g', 'n'],
       ['f', 'j', 'x']], dtype=object)
>>> np.ndarray(arr.shape[0], dtype=[('', arr.dtype)] * arr.shape[1], buffer=arr).sort()
>>> arr
array([['f', 'j', 'x'],
       ['g', 'g', 'n'],
       ['g', 'h', 'o'],
       ['n', 's', 'd'],
       ['t', 'p', 'g']], dtype=object)

Clearly only the case with dtype=[('', arr.dtype)] * arr.shape[1] is working properly. Why is that? What is different about dtype=[('', arr.dtype, arr.shape[1])]? The sort is clearly doing something, but the order appears to be nonsensical at first glance. Is it using pointers as the sort keys?

For what it's worth, np.searchsorted appears to be doing the same sort of comparison as np.sort, as expected.

Answer 1

This actually works fine

In [16]: selection = np.array(list(string.ascii_lowercase))

In [17]: arr = np.random.choice(selection, size=(5, 3))

In [18]: arr
Out[18]:
array([['x', 'l', 'i'],
       ['k', 'h', 'b'],
       ['y', 'h', 'w'],
       ['i', 'u', 't'],
       ['v', 'u', 'k']], dtype='<U1')

In [19]: np.ndarray(arr.shape[0], dtype=[('', arr.dtype, arr.shape[1])], buffer=arr).sort()

In [20]: arr
Out[20]:
array([['i', 'u', 't'],
       ['k', 'h', 'b'],
       ['v', 'u', 'k'],
       ['x', 'l', 'i'],
       ['y', 'h', 'w']], dtype='<U1')

The issue is with using dtype object for selection.

In [21]: selection = np.array(list(string.ascii_lowercase), dtype = object)

In [22]: arr = np.random.choice(selection, size=(5, 3))

In [23]: arr
Out[23]:
array([['b', 'h', 'e'],
       ['o', 'z', 'c'],
       ['g', 'v', 'z'],
       ['r', 'n', 'k'],
       ['a', 'h', 't']], dtype=object)

In [24]: np.ndarray(arr.shape[0], dtype=[('', arr.dtype, arr.shape[1])], buffer=arr).sort()

In [25]: arr
Out[25]:
array([['o', 'z', 'c'],
       ['b', 'h', 'e'],
       ['r', 'n', 'k'],
       ['a', 'h', 't'],
       ['g', 'v', 'z']], dtype=object)

Note dtype = 'O' means numpy type of python object see here for more, which I don't think provides a comparison operator.

The two types that you've provided, normally, should still work.