DEPR: maybe_promote with pydate (#49487)

* DEPR: maybe_promote with pydate

* fix whatsnew

Author: jbrockmendel

doc/source/whatsnew/v2.0.0.rst

@@ -423,6 +423,7 @@ Removal of prior version deprecations/changes
 - Changed behavior of empty data passed into :class:`Series`; the default dtype will be ``object`` instead of ``float64`` (:issue:`29405`)
 - Changed the behavior of :meth:`DatetimeIndex.union`, :meth:`DatetimeIndex.intersection`, and :meth:`DatetimeIndex.symmetric_difference` with mismatched timezones to convert to UTC instead of casting to object dtype (:issue:`39328`)
 - Changed the behavior of :func:`to_datetime` with argument "now" with ``utc=False`` to match ``Timestamp("now")`` (:issue:`18705`)
+- Changed the behavior of :meth:`Index.reindex`, :meth:`Series.reindex`, and :meth:`DataFrame.reindex` with a ``datetime64`` dtype and a ``datetime.date`` object for ``fill_value``; these are no longer considered equivalent to ``datetime.datetime`` objects so the reindex casts to object dtype (:issue:`39767`)
 - Changed behavior of :meth:`SparseArray.astype` when given a dtype that is not explicitly ``SparseDtype``, cast to the exact requested dtype rather than silently using a ``SparseDtype`` instead (:issue:`34457`)
 - Changed behavior of :meth:`Index.ravel` to return a view on the original :class:`Index` instead of a ``np.ndarray`` (:issue:`36900`)
 - Changed behavior of :meth:`Index.to_frame` with explicit ``name=None`` to use ``None`` for the column name instead of the index's name or default ``0`` (:issue:`45523`)

pandas/core/dtypes/cast.py

@@ -5,7 +5,6 @@
 from __future__ import annotations
 
 from datetime import (
-    date,
     datetime,
     timedelta,
 )
@@ -615,41 +614,14 @@ def _maybe_promote(dtype: np.dtype, fill_value=np.nan):
         if inferred == dtype:
             return dtype, fv
 
-        # TODO(2.0): once this deprecation is enforced, this whole case
-        # becomes equivalent to:
-        #  dta = DatetimeArray._from_sequence([], dtype="M8[ns]")
-        #  try:
-        #      fv = dta._validate_setitem_value(fill_value)
-        #      return dta.dtype, fv
-        #  except (ValueError, TypeError):
-        #      return _dtype_obj, fill_value
-        if isinstance(fill_value, date) and not isinstance(fill_value, datetime):
-            # deprecate casting of date object to match infer_dtype_from_scalar
-            #  and DatetimeArray._validate_setitem_value
-            try:
-                fv = Timestamp(fill_value).to_datetime64()
-            except OutOfBoundsDatetime:
-                pass
-            else:
-                warnings.warn(
-                    "Using a `date` object for fill_value with `datetime64[ns]` "
-                    "dtype is deprecated. In a future version, this will be cast "
-                    "to object dtype. Pass `fill_value=Timestamp(date_obj)` instead.",
-                    FutureWarning,
-                    stacklevel=find_stack_level(),
-                )
-                return dtype, fv
-        elif isinstance(fill_value, str):
-            try:
-                # explicitly wrap in str to convert np.str_
-                fv = Timestamp(str(fill_value))
-            except (ValueError, TypeError):
-                pass
-            else:
-                if isna(fv) or fv.tz is None:
-                    return dtype, fv.asm8
+        from pandas.core.arrays import DatetimeArray
 
-        return np.dtype("object"), fill_value
+        dta = DatetimeArray._from_sequence([], dtype="M8[ns]")
+        try:
+            fv = dta._validate_setitem_value(fill_value)
+            return dta.dtype, fv
+        except (ValueError, TypeError):
+            return _dtype_obj, fill_value
 
     elif issubclass(dtype.type, np.timedelta64):
         inferred, fv = infer_dtype_from_scalar(fill_value, pandas_dtype=True)

pandas/tests/dtypes/cast/test_promote.py

@@ -25,7 +25,6 @@
 from pandas.core.dtypes.missing import isna
 
 import pandas as pd
-import pandas._testing as tm
 
 
 @pytest.fixture(
@@ -403,15 +402,12 @@ def test_maybe_promote_any_with_datetime64(any_numpy_dtype_reduced, fill_value):
         expected_dtype = np.dtype(object)
         exp_val_for_scalar = fill_value
 
-    warn = None
-    msg = "Using a `date` object for fill_value"
     if type(fill_value) is datetime.date and dtype.kind == "M":
-        # Casting date to dt64 is deprecated
-        warn = FutureWarning
+        # Casting date to dt64 is deprecated, in 2.0 enforced to cast to object
+        expected_dtype = np.dtype(object)
+        exp_val_for_scalar = fill_value
 
-    with tm.assert_produces_warning(warn, match=msg):
-        # stacklevel is chosen to make sense when called from higher-level functions
-        _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
+    _check_promote(dtype, fill_value, expected_dtype, exp_val_for_scalar)
 
 
 @pytest.mark.parametrize(

pandas/tests/frame/methods/test_reindex.py

@@ -136,30 +136,32 @@ def test_reindex_copies(self):
 
     @td.skip_array_manager_not_yet_implemented
     def test_reindex_date_fill_value(self):
-        # passing date to dt64 is deprecated
+        # passing date to dt64 is deprecated; enforced in 2.0 to cast to object
         arr = date_range("2016-01-01", periods=6).values.reshape(3, 2)
         df = DataFrame(arr, columns=["A", "B"], index=range(3))
 
         ts = df.iloc[0, 0]
         fv = ts.date()
 
-        with tm.assert_produces_warning(FutureWarning):
-            res = df.reindex(index=range(4), columns=["A", "B", "C"], fill_value=fv)
+        res = df.reindex(index=range(4), columns=["A", "B", "C"], fill_value=fv)
 
         expected = DataFrame(
-            {"A": df["A"].tolist() + [ts], "B": df["B"].tolist() + [ts], "C": [ts] * 4}
+            {"A": df["A"].tolist() + [fv], "B": df["B"].tolist() + [fv], "C": [fv] * 4},
+            dtype=object,
         )
         tm.assert_frame_equal(res, expected)
 
         # only reindexing rows
-        with tm.assert_produces_warning(FutureWarning):
-            res = df.reindex(index=range(4), fill_value=fv)
+        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"
         )
+        expected = DataFrame(
+            {"A": df["A"].tolist() + [ts], "B": df["B"].tolist() + [ts], "C": [ts] * 4},
+        )
         tm.assert_frame_equal(res, expected)
 
     def test_reindex_with_multi_index(self):