BUG: Unwanted conversion from timedelta to float (#18493)

doc/source/whatsnew/v0.21.1.txt

@@ -74,6 +74,7 @@ Indexing
 - Bug where a ``MultiIndex`` with more than a million records was not raising ``AttributeError`` when trying to access a missing attribute (:issue:`18165`)
 - Bug in :class:`IntervalIndex` constructor when a list of intervals is passed with non-default ``closed`` (:issue:`18334`)
 - Bug in ``Index.putmask`` when an invalid mask passed (:issue:`18368`)
+- Bug in masked assignment of a ``timedelta64[ns]`` dtype ``Series``, incorrectly coerced to float (:issue:`18493`)
 -
 
 I/O

pandas/core/internals.py

@@ -1956,7 +1956,8 @@ def _can_hold_element(self, element):
         tipo = maybe_infer_dtype_type(element)
         if tipo is not None:
             return issubclass(tipo.type, np.timedelta64)
-        return isinstance(element, (timedelta, np.timedelta64))
+        return is_integer(element) or isinstance(
+            element, (timedelta, np.timedelta64))
 
     def fillna(self, value, **kwargs):
 

pandas/tests/indexing/test_timedelta.py

@@ -2,6 +2,7 @@
 
 import pandas as pd
 from pandas.util import testing as tm
+import numpy as np
 
 
 class TestTimedeltaIndexing(object):
@@ -47,3 +48,23 @@ def test_string_indexing(self):
         expected = df.iloc[0]
         sliced = df.loc['0 days']
         tm.assert_series_equal(sliced, expected)
+
+    @pytest.mark.parametrize(
+        "value",
+        [None, pd.NaT, np.nan])
+    def test_masked_setitem(self, value):
+        # issue (#18586)
+        series = pd.Series([0, 1, 2], dtype='timedelta64[ns]')
+        series[series == series[0]] = value
+        expected = pd.Series([pd.NaT, 1, 2], dtype='timedelta64[ns]')
+        tm.assert_series_equal(series, expected)
+
+    @pytest.mark.parametrize(
+        "value",
+        [None, pd.NaT, np.nan])
+    def test_listlike_setitem(self, value):
+        # issue (#18586)
+        series = pd.Series([0, 1, 2], dtype='timedelta64[ns]')
+        series.iloc[0] = value
+        expected = pd.Series([pd.NaT, 1, 2], dtype='timedelta64[ns]')
+        tm.assert_series_equal(series, expected)