BUG: Fixed pd.unique on array of tuples (#16543)

doc/source/whatsnew/v0.20.2.txt

@@ -44,8 +44,7 @@ Bug Fixes
 - Bug in ``DataFrame.update()`` with ``overwrite=False`` and ``NaN values`` (:issue:`15593`)
 - Passing an invalid engine to :func:`read_csv` now raises an informative
   ``ValueError`` rather than ``UnboundLocalError``. (:issue:`16511`)
-
-
+- Bug in :func:`unique` on an array of tuples (:issue:`16519`)
 
 
 - Fixed a compatibility issue with IPython 6.0's tab completion showing deprecation warnings on Categoricals (:issue:`16409`)

pandas/core/algorithms.py

@@ -163,7 +163,7 @@ def _ensure_arraylike(values):
                                ABCIndexClass, ABCSeries)):
         inferred = lib.infer_dtype(values)
         if inferred in ['mixed', 'string', 'unicode']:
-            values = np.asarray(values, dtype=object)
+            values = lib.list_to_object_array(values)
         else:
             values = np.asarray(values)
     return values
@@ -328,6 +328,11 @@ def unique(values):
     [b, a, c]
     Categories (3, object): [a < b < c]
 
+    An array of tuples
+
+    >>> pd.unique([('a', 'b'), ('b', 'a'), ('a', 'c'), ('b', 'a')])
+    array([('a', 'b'), ('b', 'a'), ('a', 'c')], dtype=object)
+
     See Also
     --------
     pandas.Index.unique

pandas/tests/test_algos.py

@@ -929,6 +929,22 @@ def test_unique_index(self):
             tm.assert_numpy_array_equal(case.duplicated(),
                                         np.array([False, False, False]))
 
+    @pytest.mark.parametrize('arr, unique', [
+        ([(0, 0), (0, 1), (1, 0), (1, 1), (0, 0), (0, 1), (1, 0), (1, 1)],
+         [(0, 0), (0, 1), (1, 0), (1, 1)]),
+        ([('b', 'c'), ('a', 'b'), ('a', 'b'), ('b', 'c')],
+         [('b', 'c'), ('a', 'b')]),
+        ([('a', 1), ('b', 2), ('a', 3), ('a', 1)],
+         [('a', 1), ('b', 2), ('a', 3)]),
+    ])
+    def test_unique_tuples(self, arr, unique):
+        # https://github.com/pandas-dev/pandas/issues/16519
+        expected = np.empty(len(unique), dtype=object)
+        expected[:] = unique
+
+        result = pd.unique(arr)
+        tm.assert_numpy_array_equal(result, expected)
+
 
 class GroupVarTestMixin(object):