Temporary fix

Author: MarcoGorelli

doc/source/whatsnew/v1.0.0.rst

@@ -97,7 +97,7 @@ Datetimelike
 - Bug in :meth:`Series.__setitem__` incorrectly casting ``np.timedelta64("NaT")`` to ``np.datetime64("NaT")`` when inserting into a :class:`Series` with datetime64 dtype (:issue:`27311`)
 - Bug in :meth:`Series.dt` property lookups when the underlying data is read-only (:issue:`27529`)
 - Bug in ``HDFStore.__getitem__`` incorrectly reading tz attribute created in Python 2 (:issue:`26443`)
--
+- Bug in :meth:`pandas.core.groupby.SeriesGroupBy.nunique` where ``NaT`` values were interfering with the count of unique values (:issue:`27951`)
 
 
 Timedelta

pandas/core/groupby/generic.py

@@ -1147,6 +1147,10 @@ def nunique(self, dropna=True):
 
         val = self.obj._internal_get_values()
 
+        # GH 27951
+        # temporary fix while we wait for NumPy bug 12629 to be fixed
+        val[isna(val)] = np.datetime64("NaT")
+
         try:
             sorter = np.lexsort((val, ids))
         except TypeError:  # catches object dtypes

pandas/tests/groupby/test_function.py

@@ -2,14 +2,24 @@
 from io import StringIO
 from itertools import product
 from string import ascii_lowercase
+import datetime as dt
 
 import numpy as np
 import pytest
 
 from pandas.errors import UnsupportedFunctionCall
 
 import pandas as pd
-from pandas import DataFrame, Index, MultiIndex, Series, Timestamp, date_range, isna
+from pandas import (
+    DataFrame,
+    Index,
+    MultiIndex,
+    Series,
+    Timestamp,
+    date_range,
+    isna,
+    NaT,
+)
 import pandas.core.nanops as nanops
 from pandas.util import _test_decorators as td, testing as tm
 
@@ -1015,6 +1025,42 @@ def test_nunique_with_timegrouper():
     tm.assert_series_equal(result, expected)
 
 
+@pytest.mark.parametrize(
+    "key, data, dropna, expected",
+    [
+        (
+            ["x", "x", "x"],
+            [Timestamp("2019-01-01"), NaT, Timestamp("2019-01-01")],
+            True,
+            Series([1], index=pd.Index(["x"], name="key"), name="data"),
+        ),
+        (
+            ["x", "x", "x"],
+            [dt.date(2019, 1, 1), NaT, dt.date(2019, 1, 1)],
+            True,
+            Series([1], index=pd.Index(["x"], name="key"), name="data"),
+        ),
+        (
+            ["x", "x", "x", "y", "y"],
+            [dt.date(2019, 1, 1), NaT, dt.date(2019, 1, 1), NaT, dt.date(2019, 1, 1)],
+            False,
+            Series([2, 2], index=pd.Index(["x", "y"], name="key"), name="data"),
+        ),
+        (
+            ["x", "x", "x", "x", "y"],
+            [dt.date(2019, 1, 1), NaT, dt.date(2019, 1, 1), NaT, dt.date(2019, 1, 1)],
+            False,
+            Series([2, 1], index=pd.Index(["x", "y"], name="key"), name="data"),
+        ),
+    ],
+)
+def test_nunique_with_NaT(key, data, dropna, expected):
+    # GH 27951
+    df = pd.DataFrame({"key": key, "data": data})
+    result = df.groupby(["key"])["data"].nunique(dropna=dropna)
+    tm.assert_series_equal(result, expected)
+
+
 def test_nunique_preserves_column_level_names():
     # GH 23222
     test = pd.DataFrame([1, 2, 2], columns=pd.Index(["A"], name="level_0"))