FIX: date comparison fails when series is all pd.NaT values #61188

Author: Mohit-Kundu

doc/source/whatsnew/v3.0.0.rst

@@ -642,6 +642,7 @@ Categorical
 Datetimelike
 ^^^^^^^^^^^^
 - Bug in :attr:`is_year_start` where a DateTimeIndex constructed via a date_range with frequency 'MS' wouldn't have the correct year or quarter start attributes (:issue:`57377`)
+- Bug in :attr:`Series.dt.date` where Series with all NaT values would raise an error when compared to a datetime.date (:issue:`61188`)
 - Bug in :class:`DataFrame` raising ``ValueError`` when ``dtype`` is ``timedelta64`` and ``data`` is a list containing ``None`` (:issue:`60064`)
 - Bug in :class:`Timestamp` constructor failing to raise when ``tz=None`` is explicitly specified in conjunction with timezone-aware ``tzinfo`` or data (:issue:`48688`)
 - Bug in :func:`date_range` where the last valid timestamp would sometimes not be produced (:issue:`56134`)
@@ -656,11 +657,9 @@ Datetimelike
 - Bug in :meth:`Series.dt.microsecond` producing incorrect results for pyarrow backed :class:`Series`. (:issue:`59154`)
 - Bug in :meth:`to_datetime` not respecting dayfirst if an uncommon date string was passed. (:issue:`58859`)
 - Bug in :meth:`to_datetime` on float array with missing values throwing ``FloatingPointError`` (:issue:`58419`)
-- Bug in :meth:`to_datetime` on float32 df with year, month, day etc. columns leads to precision issues and incorrect result. (:issue:`60506`)
 - Bug in :meth:`to_datetime` reports incorrect index in case of any failure scenario. (:issue:`58298`)
 - Bug in :meth:`to_datetime` wrongly converts when ``arg`` is a ``np.datetime64`` object with unit of ``ps``. (:issue:`60341`)
 - Bug in setting scalar values with mismatched resolution into arrays with non-nanosecond ``datetime64``, ``timedelta64`` or :class:`DatetimeTZDtype` incorrectly truncating those scalars (:issue:`56410`)
-- Bug in :attr:`Series.dt.date` where Series with all NaT values would raise an error when compared to a datetime.date (:issue:`61188`)
 
 Timedelta
 ^^^^^^^^^

pandas/core/indexes/accessors.py

@@ -11,8 +11,11 @@
 )
 import warnings
 
+
 import numpy as np
 
+
+
 from pandas._libs import lib
 from pandas.util._exceptions import find_stack_level
 
@@ -108,7 +111,7 @@ def _delegate_property_get(self, name: str):
         else:
             index = self._parent.index
         # return the result as a Series
-        return Series(result, index=index, name=self.name).__finalize__(self._parent)
+        return Series(result, index=index, name=self.name, dtype=result.dtype).__finalize__(self._parent)
 
     def _delegate_property_set(self, name: str, value, *args, **kwargs) -> NoReturn:
         raise ValueError(
@@ -396,53 +399,6 @@ def freq(self):
         '2YS-JAN'
         """
         return self._get_values().inferred_freq
-    
-    @property
-    def date(self):
-        """
-        Return the date component (year, month, day) of each datetime in the Series.
-
-        This property returns a Series of Python datetime.date objects corresponding to
-        the date portion of each datetime64[ns] value in the Series. For missing values
-        (NaT), the result will be NaT.
-
-        Returns
-        -------
-        Series
-            A Series of datetime.date objects or NaT, with dtype object.
-
-        Notes
-        -----
-        - The result is always returned with object dtype.
-        - This ensures comparisons with Python datetime.date values (e.g. using <=)
-        work even when the Series contains only missing values (NaT).
-
-        Examples
-        --------
-        >>> s = pd.Series(pd.to_datetime(["2020-01-01", pd.NaT]))
-        >>> s.dt.date
-        0    2020-01-01
-        1           NaT
-        dtype: object
-
-        >>> s.dt.date <= datetime.date(2024, 1, 1)
-        0     True
-        1    False
-        dtype: bool
-        """
-        
-        from pandas import Series
-        import datetime
-
-        values = self._get_values()
-        as_dates = values.to_pydatetime()
-
-        def extract_date(x):
-            return x.date() if isinstance(x, datetime.datetime) else pd.NaT
-
-        result = [extract_date(v) for v in as_dates]
-
-        return Series(result, index=self._parent.index, name=self._parent.name, dtype=object).__finalize__(self._parent)
 
     def isocalendar(self) -> DataFrame:
         """

pandas/tests/series/indexing/test_datetime.py

@@ -498,8 +498,10 @@ def test_dt_date_dtype_all_nat_is_object():
     s = pd.Series([pd.NaT, pd.NaT])
     s = pd.to_datetime(s)
     result = s.dt.date
-    assert result.dtype == object
-    assert result.isna().all()
+
+    expected = pd.Series([pd.NaT, pd.NaT], dtype=object)
+
+    tm.assert_series_equal(result, expected)
 
 def test_dt_date_all_nat_le_date():
     #All-NaT Series should not raise error when compared to a datetime.date