Backport PR pandas-dev#25182: BUG: Fix Series.is_unique with single o…

…ccurrence of NaN (pandas-dev#25223)

doc/source/whatsnew/v0.24.2.rst

@@ -87,7 +87,7 @@ Bug Fixes
 
 **Other**
 
--
+- Bug in :meth:`Series.is_unique` where single occurrences of ``NaN`` were not considered unique (:issue:`25180`)
 -
 -
 

pandas/core/base.py

@@ -1345,7 +1345,7 @@ def is_unique(self):
         -------
         is_unique : boolean
         """
-        return self.nunique() == len(self)
+        return self.nunique(dropna=False) == len(self)
 
     @property
     def is_monotonic(self):

pandas/tests/indexes/common.py

@@ -905,3 +905,24 @@ def test_astype_category(self, copy, name, ordered):
             result = index.astype('category', copy=copy)
             expected = CategoricalIndex(index.values, name=name)
             tm.assert_index_equal(result, expected)
+
+    def test_is_unique(self):
+        # initialize a unique index
+        index = self.create_index().drop_duplicates()
+        assert index.is_unique is True
+
+        # empty index should be unique
+        index_empty = index[:0]
+        assert index_empty.is_unique is True
+
+        # test basic dupes
+        index_dup = index.insert(0, index[0])
+        assert index_dup.is_unique is False
+
+        # single NA should be unique
+        index_na = index.insert(0, np.nan)
+        assert index_na.is_unique is True
+
+        # multiple NA should not be unique
+        index_na_dup = index_na.insert(0, np.nan)
+        assert index_na_dup.is_unique is False

pandas/tests/indexes/interval/test_interval.py

@@ -242,12 +242,10 @@ def test_take(self, closed):
             [0, 0, 1], [1, 1, 2], closed=closed)
         tm.assert_index_equal(result, expected)
 
-    def test_unique(self, closed):
-        # unique non-overlapping
-        idx = IntervalIndex.from_tuples(
-            [(0, 1), (2, 3), (4, 5)], closed=closed)
-        assert idx.is_unique is True
-
+    def test_is_unique_interval(self, closed):
+        """
+        Interval specific tests for is_unique in addition to base class tests
+        """
         # unique overlapping - distinct endpoints
         idx = IntervalIndex.from_tuples([(0, 1), (0.5, 1.5)], closed=closed)
         assert idx.is_unique is True
@@ -261,15 +259,6 @@ def test_unique(self, closed):
         idx = IntervalIndex.from_tuples([(-1, 1), (-2, 2)], closed=closed)
         assert idx.is_unique is True
 
-        # duplicate
-        idx = IntervalIndex.from_tuples(
-            [(0, 1), (0, 1), (2, 3)], closed=closed)
-        assert idx.is_unique is False
-
-        # empty
-        idx = IntervalIndex([], closed=closed)
-        assert idx.is_unique is True
-
     def test_monotonic(self, closed):
         # increasing non-overlapping
         idx = IntervalIndex.from_tuples(

pandas/tests/indexes/multi/test_duplicates.py

@@ -143,6 +143,18 @@ def test_has_duplicates(idx, idx_dup):
     assert mi.is_unique is False
     assert mi.has_duplicates is True
 
+    # single instance of NaN
+    mi_nan = MultiIndex(levels=[['a', 'b'], [0, 1]],
+                        codes=[[-1, 0, 0, 1, 1], [-1, 0, 1, 0, 1]])
+    assert mi_nan.is_unique is True
+    assert mi_nan.has_duplicates is False
+
+    # multiple instances of NaN
+    mi_nan_dup = MultiIndex(levels=[['a', 'b'], [0, 1]],
+                            codes=[[-1, -1, 0, 0, 1, 1], [-1, -1, 0, 1, 0, 1]])
+    assert mi_nan_dup.is_unique is False
+    assert mi_nan_dup.has_duplicates is True
+
 
 def test_has_duplicates_from_tuples():
     # GH 9075

pandas/tests/indexes/period/test_indexing.py

@@ -441,18 +441,6 @@ def test_is_monotonic_decreasing(self):
         assert idx_dec1.is_monotonic_decreasing is True
         assert idx.is_monotonic_decreasing is False
 
-    def test_is_unique(self):
-        # GH 17717
-        p0 = pd.Period('2017-09-01')
-        p1 = pd.Period('2017-09-02')
-        p2 = pd.Period('2017-09-03')
-
-        idx0 = pd.PeriodIndex([p0, p1, p2])
-        assert idx0.is_unique is True
-
-        idx1 = pd.PeriodIndex([p1, p1, p2])
-        assert idx1.is_unique is False
-
     def test_contains(self):
         # GH 17717
         p0 = pd.Period('2017-09-01')

pandas/tests/indexes/test_category.py

@@ -611,15 +611,6 @@ def test_is_monotonic(self, data, non_lexsorted_data):
         assert c.is_monotonic_increasing is True
         assert c.is_monotonic_decreasing is False
 
-    @pytest.mark.parametrize('values, expected', [
-        ([1, 2, 3], True),
-        ([1, 3, 1], False),
-        (list('abc'), True),
-        (list('aba'), False)])
-    def test_is_unique(self, values, expected):
-        ci = CategoricalIndex(values)
-        assert ci.is_unique is expected
-
     def test_has_duplicates(self):
 
         idx = CategoricalIndex([0, 0, 0], name='foo')

pandas/tests/series/test_duplicates.py

@@ -59,12 +59,18 @@ def test_unique_data_ownership():
     Series(Series(["a", "c", "b"]).unique()).sort_values()
 
 
-def test_is_unique():
-    # GH11946
-    s = Series(np.random.randint(0, 10, size=1000))
-    assert s.is_unique is False
-    s = Series(np.arange(1000))
-    assert s.is_unique is True
+@pytest.mark.parametrize('data, expected', [
+    (np.random.randint(0, 10, size=1000), False),
+    (np.arange(1000), True),
+    ([], True),
+    ([np.nan], True),
+    (['foo', 'bar', np.nan], True),
+    (['foo', 'foo', np.nan], False),
+    (['foo', 'bar', np.nan, np.nan], False)])
+def test_is_unique(data, expected):
+    # GH11946 / GH25180
+    s = Series(data)
+    assert s.is_unique is expected
 
 
 def test_is_unique_class_ne(capsys):