numpy
diff --git a/‎numpy/lib/arraysetops.py
Lines changed: 21 additions & 15 deletions b/‎numpy/lib/arraysetops.py
Lines changed: 21 additions & 15 deletions
diff --git a/‎numpy/lib/tests/test_arraysetops.py
Lines changed: 9 additions & 0 deletions b/‎numpy/lib/tests/test_arraysetops.py
Lines changed: 9 additions & 0 deletions
@@ -135,16 +135,18 @@ def unique(ar, return_index=False, return_inverse=False,
     return_counts : bool, optional
         If True, also return the number of times each unique item appears
         in `ar`.
+
         .. versionadded:: 1.9.0
-    axis : int or None, optional
-        The axis to operate on. If None, `ar` will be flattened beforehand.
-        Otherwise, duplicate items will be removed along the provided axis,
-        with all the other axes belonging to the each of the unique elements.
-        Object arrays or structured arrays that contain objects are not
-        supported if the `axis` kwarg is used.
-        .. versionadded:: 1.13.0
 
+    axis : int or None, optional
+        The axis to operate on. If None, `ar` will be flattened. If an integer,
+        the subarrays indexed by the given axis will be flattened and treated
+        as the elements of a 1-D array with the dimension of the given axis,
+        see the notes for more details.  Object arrays or structured arrays
+        that contain objects are not supported if the `axis` kwarg is used. The
+        default is None.
 
+        .. versionadded:: 1.13.0
 
     Returns
     -------
@@ -166,6 +168,17 @@ def unique(ar, return_index=False, return_inverse=False,
     numpy.lib.arraysetops : Module with a number of other functions for
                             performing set operations on arrays.
 
+    Notes
+    -----
+    When an axis is specified the subarrays indexed by the axis are sorted.
+    This is done by making the specified axis the first dimension of the array
+    and then flattening the subarrays in C order. The flattened subarrays are
+    then viewed as a structured type with each element given a label, with the
+    effect that we end up with a 1-D array of structured types that can be
+    treated in the same way as any other 1-D array. The result is that the
+    flattened subarrays are sorted in lexicographic order starting with the
+    first element.
+
     Examples
     --------
     >>> np.unique([1, 1, 2, 2, 3, 3])
@@ -217,14 +230,7 @@ def unique(ar, return_index=False, return_inverse=False,
     ar = ar.reshape(orig_shape[0], -1)
     ar = np.ascontiguousarray(ar)
 
-    if ar.dtype.char in (np.typecodes['AllInteger'] +
-                         np.typecodes['Datetime'] + 'S'):
-        # Optimization: Creating a view of your data with a np.void data type of
-        # size the number of bytes in a full row. Handles any type where items
-        # have a unique binary representation, i.e. 0 is only 0, not +0 and -0.
-        dtype = np.dtype((np.void, ar.dtype.itemsize * ar.shape[1]))
-    else:
-        dtype = [('f{i}'.format(i=i), ar.dtype) for i in range(ar.shape[1])]
+    dtype = [('f{i}'.format(i=i), ar.dtype) for i in range(ar.shape[1])]
 
     try:
         consolidated = ar.view(dtype)
 
@@ -453,6 +453,15 @@ def test_unique_masked(self):
         assert_array_equal(v.data, v2.data, msg)
         assert_array_equal(v.mask, v2.mask, msg)
 
+    def test_unique_sort_order_with_axis(self):
+        # These tests fail if sorting along axis is done by treating subarrays
+        # as unsigned byte strings.  See gh-10495.
+        fmt = "sort order incorrect for integer type '%s'"
+        for dt in 'bhilq':
+            a = np.array([[-1],[0]], dt)
+            b = np.unique(a, axis=0)
+            assert_array_equal(a, b, fmt % dt)
+
     def _run_axis_tests(self, dtype):
         data = np.array([[0, 1, 0, 0],
                          [1, 0, 0, 0],