diff --git a/cyberpandas/ip_array.py b/cyberpandas/ip_array.py
index 587b8b7..59ab389 100644
--- a/cyberpandas/ip_array.py
+++ b/cyberpandas/ip_array.py
@@ -452,28 +452,20 @@ def index_type(self):
 
     def unique(self):
         # type: () -> ExtensionArray
-        pass
+        # https://github.com/pandas-dev/pandas/pull/19869
+        _, indices = np.unique(self.data, return_index=True)
+        data = self.data.take(np.sort(indices))
+        return self._from_ndarray(data)
 
-    def _factorize(self, sort=False):
+    def factorize(self, sort=False):
         # XXX: Verify this, check for better algo
-        # astype to avoid endianness issues in pd.factorize
-        a, _ = pd.factorize(self.data['lo'].astype('u8'))
-        b, _ = pd.factorize(self.data['hi'].astype('u8'))
-
-        labels = np.bitwise_xor.reduce(
-            np.concatenate([a.reshape(-1, 1),
-                            b.reshape(-1, 1)], axis=1),
-            axis=1
-        )
-
-        # TODO: refactor into a .unique
-        # TODO: Handle empty, scalar, etc.
-        mask = np.zeros(len(labels), dtype=bool)
-        mask[0] = True
-        inner_mask = (labels[1:] - labels[:-1]) != 0
-        mask[1:] = inner_mask
-
-        uniques = self[mask]
+        uniques, indices, labels = np.unique(self.data,
+                                             return_index=True,
+                                             return_inverse=True)
+        if not sort:
+            # Unsort, since np.unique sorts
+            uniques = self._from_ndarray(self.data.take(np.sort(indices)))
+            labels = np.argsort(uniques.data).take(labels)
         return labels, uniques
 
 
diff --git a/cyberpandas/test_ip.py b/cyberpandas/test_ip.py
index 435e042..dc6061b 100644
--- a/cyberpandas/test_ip.py
+++ b/cyberpandas/test_ip.py
@@ -278,3 +278,30 @@ def test_bytes_roundtrip():
 
     result = ip.IPArray.from_bytes(bytestring)
     assert result.equals(arr)
+
+
+def test_unique():
+    arr = ip.IPArray([3, 3, 1, 2, 3, _U8_MAX + 1])
+    result = arr.unique()
+    assert isinstance(result, ip.IPArray)
+
+    result = result.astype(object)
+    expected = pd.unique(arr.astype(object))
+    tm.assert_numpy_array_equal(result, expected)
+
+
+@pytest.mark.parametrize('sort', [
+    pytest.param(True, marks=pytest.mark.xfail(reason="Upstream sort_values")),
+    False
+])
+def test_factorize(sort):
+    arr = ip.IPArray([3, 3, 1, 2, 3, _U8_MAX + 1])
+    labels, uniques = arr.factorize(sort=sort)
+    expected_labels, expected_uniques = pd.factorize(arr.astype(object),
+                                                     sort=sort)
+
+    assert isinstance(uniques, ip.IPArray)
+
+    uniques = uniques.astype(object)
+    tm.assert_numpy_array_equal(labels, expected_labels)
+    tm.assert_numpy_array_equal(uniques, expected_uniques)