REGR: allow reindexing datetimelike with upcast / raise deprecation warning

doc/source/whatsnew/v1.4.0.rst

@@ -748,6 +748,7 @@ Datetimelike
 - Bug in adding a ``np.timedelta64`` object to a :class:`BusinessDay` or :class:`CustomBusinessDay` object incorrectly raising (:issue:`44532`)
 - Bug in :meth:`Index.insert` for inserting ``np.datetime64``, ``np.timedelta64`` or ``tuple`` into :class:`Index` with ``dtype='object'`` with negative loc adding ``None`` and replacing existing value (:issue:`44509`)
 - Bug in :meth:`Series.mode` with ``DatetimeTZDtype`` incorrectly returning timezone-naive and ``PeriodDtype`` incorrectly raising (:issue:`41927`)
+- Fixed regression in :meth:`~Series.reindex` raising an error when using an incompatible fill value with a datetime-like dtype (or not raising a deprecation warning for using a ``datetime.date`` as fill value) (:issue:`42921`)
 - Bug in :class:`DateOffset`` addition with :class:`Timestamp` where ``offset.nanoseconds`` would not be included in the result (:issue:`43968`, :issue:`36589`)
 - Bug in :meth:`Timestamp.fromtimestamp` not supporting the ``tz`` argument (:issue:`45083`)
 - Bug in :class:`DataFrame` construction from dict of :class:`Series` with mismatched index dtypes sometimes raising depending on the ordering of the passed dict (:issue:`44091`)

pandas/core/array_algos/take.py

@@ -94,6 +94,12 @@ def take_nd(
     """
     if fill_value is lib.no_default:
         fill_value = na_value_for_dtype(arr.dtype, compat=False)
+    elif isinstance(arr.dtype, np.dtype) and arr.dtype.kind in "mM":
+        dtype, fill_value = maybe_promote(arr.dtype, fill_value)
+        if arr.dtype != dtype:
+            # EA.take is strict about returning a new object of the same type
+            # so for that case cast upfront
+            arr = arr.astype(dtype)
 
     if not isinstance(arr, np.ndarray):
         # i.e. ExtensionArray,

pandas/core/dtypes/cast.py

@@ -575,7 +575,7 @@ def _maybe_promote(dtype: np.dtype, fill_value=np.nan):
             except (ValueError, TypeError):
                 pass
             else:
-                if fv.tz is None:
+                if isna(fv) or fv.tz is None:
                     return dtype, fv.asm8
 
         return np.dtype("object"), fill_value

pandas/tests/frame/methods/test_reindex.py

@@ -8,6 +8,8 @@
 import numpy as np
 import pytest
 
+import pandas.util._test_decorators as td
+
 import pandas as pd
 from pandas import (
     Categorical,
@@ -97,6 +99,7 @@ def test_reindex_copies(self):
         result2 = df.reindex(columns=cols, index=df.index, copy=True)
         assert not np.shares_memory(result2[0]._values, df[0]._values)
 
+    @td.skip_array_manager_not_yet_implemented
     def test_reindex_date_fill_value(self):
         # passing date to dt64 is deprecated
         arr = date_range("2016-01-01", periods=6).values.reshape(3, 2)
@@ -113,6 +116,11 @@ def test_reindex_date_fill_value(self):
         )
         tm.assert_frame_equal(res, expected)
 
+        # only reindexing rows
+        with tm.assert_produces_warning(FutureWarning):
+            res = df.reindex(index=range(4), fill_value=fv)
+        tm.assert_frame_equal(res, expected[["A", "B"]])
+
         # same with a datetime-castable str
         res = df.reindex(
             index=range(4), columns=["A", "B", "C"], fill_value="2016-01-01"

pandas/tests/series/methods/test_reindex.py

@@ -9,6 +9,8 @@
     Period,
     PeriodIndex,
     Series,
+    Timedelta,
+    Timestamp,
     date_range,
     isna,
 )
@@ -300,6 +302,21 @@ def test_reindex_fill_value():
     tm.assert_series_equal(result, expected)
 
 
+@pytest.mark.parametrize("dtype", ["datetime64[ns]", "timedelta64[ns]"])
+@pytest.mark.parametrize("fill_value", ["string", 0, Timedelta(0)])
+def test_reindex_fill_value_datetimelike_upcast(dtype, fill_value):
+    # https://github.com/pandas-dev/pandas/issues/42921
+    if dtype == "timedelta64[ns]" and fill_value == Timedelta(0):
+        # use the scalar that is not compatible with the dtype for this test
+        fill_value = Timestamp(0)
+
+    ser = Series([NaT], dtype=dtype)
+
+    result = ser.reindex([0, 1], fill_value=fill_value)
+    expected = Series([None, fill_value], index=[0, 1], dtype=object)
+    tm.assert_series_equal(result, expected)
+
+
 def test_reindex_datetimeindexes_tz_naive_and_aware():
     # GH 8306
     idx = date_range("20131101", tz="America/Chicago", periods=7)