REF: make is_dates_only a DatetimeArray method

pandas/core/arrays/datetimes.py

@@ -28,12 +28,14 @@
     get_resolution,
     get_supported_reso,
     get_unit_from_dtype,
+    iNaT,
     ints_to_pydatetime,
     is_date_array_normalized,
     is_supported_unit,
     is_unitless,
     normalize_i8_timestamps,
     npy_unit_to_abbrev,
+    periods_per_day,
     timezones,
     to_offset,
     tz_convert_from_utc,
@@ -735,10 +737,8 @@ def astype(self, dtype, copy: bool = True):
     def _format_native_types(
         self, *, na_rep: str | float = "NaT", date_format=None, **kwargs
     ) -> npt.NDArray[np.object_]:
-        from pandas.io.formats.format import is_dates_only
-
         if date_format is None:
-            ido = is_dates_only(self)
+            ido = self._is_dates_only
             if ido:
                 # Only dates and no timezone: provide a default format
                 date_format = "%Y-%m-%d"
@@ -747,6 +747,25 @@ def _format_native_types(
             self.asi8, tz=self.tz, format=date_format, na_rep=na_rep, reso=self._creso
         )
 
+    @property
+    def _is_dates_only(self) -> bool:
+        """
+        Check if we are round times at midnight (and no timezone), which will
+        be given a more compact __repr__ than other cases.
+        """
+        if self.tz is not None:
+            return False
+
+        values_int = self.asi8
+        consider_values = values_int != iNaT
+        dtype = cast(np.dtype, self.dtype)  # since we checked tz above
+        reso = get_unit_from_dtype(dtype)
+        ppd = periods_per_day(reso)
+
+        # TODO: can we reuse is_date_array_normalized?  would need a skipna kwd
+        even_days = np.logical_and(consider_values, values_int % ppd != 0).sum() == 0
+        return even_days
+
     # -----------------------------------------------------------------
     # Comparison Methods
 

pandas/core/indexes/datetimes.py

@@ -393,19 +393,12 @@ def _is_dates_only(self) -> bool:
         -------
         bool
         """
-
-        from pandas.io.formats.format import is_dates_only
-
         delta = getattr(self.freq, "delta", None)
 
         if delta and delta % dt.timedelta(days=1) != dt.timedelta(days=0):
             return False
 
-        # error: Argument 1 to "is_dates_only" has incompatible type
-        # "Union[ExtensionArray, ndarray]"; expected "Union[ndarray,
-        # DatetimeArray, Index, DatetimeIndex]"
-
-        return self.tz is None and is_dates_only(self._values)  # type: ignore[arg-type]
+        return self.tz is None and self._values._is_dates_only
 
     def __reduce__(self):
         d = {"data": self._data, "name": self.name}
@@ -428,7 +421,7 @@ def _is_comparable_dtype(self, dtype: DtypeObj) -> bool:
     def _formatter_func(self):
         from pandas.io.formats.format import get_format_datetime64
 
-        formatter = get_format_datetime64(is_dates_only_=self._is_dates_only)
+        formatter = get_format_datetime64(is_dates_only=self._is_dates_only)
         return lambda x: f"'{formatter(x)}'"
 
     # --------------------------------------------------------------------

pandas/io/formats/format.py

@@ -45,9 +45,7 @@
     NaT,
     Timedelta,
     Timestamp,
-    get_unit_from_dtype,
     iNaT,
-    periods_per_day,
 )
 from pandas._libs.tslibs.nattype import NaTType
 
@@ -1749,31 +1747,6 @@ def format_percentiles(
     return [i + "%" for i in out]
 
 
-def is_dates_only(values: np.ndarray | DatetimeArray | Index | DatetimeIndex) -> bool:
-    # return a boolean if we are only dates (and don't have a timezone)
-    if not isinstance(values, Index):
-        values = values.ravel()
-
-    if not isinstance(values, (DatetimeArray, DatetimeIndex)):
-        values = DatetimeIndex(values)
-
-    if values.tz is not None:
-        return False
-
-    values_int = values.asi8
-    consider_values = values_int != iNaT
-    # error: Argument 1 to "py_get_unit_from_dtype" has incompatible type
-    # "Union[dtype[Any], ExtensionDtype]"; expected "dtype[Any]"
-    reso = get_unit_from_dtype(values.dtype)  # type: ignore[arg-type]
-    ppd = periods_per_day(reso)
-
-    # TODO: can we reuse is_date_array_normalized?  would need a skipna kwd
-    even_days = np.logical_and(consider_values, values_int % ppd != 0).sum() == 0
-    if even_days:
-        return True
-    return False
-
-
 def _format_datetime64(x: NaTType | Timestamp, nat_rep: str = "NaT") -> str:
     if x is NaT:
         return nat_rep
@@ -1799,12 +1772,12 @@ def _format_datetime64_dateonly(
 
 
 def get_format_datetime64(
-    is_dates_only_: bool, nat_rep: str = "NaT", date_format: str | None = None
+    is_dates_only: bool, nat_rep: str = "NaT", date_format: str | None = None
 ) -> Callable:
     """Return a formatter callable taking a datetime64 as input and providing
     a string as output"""
 
-    if is_dates_only_:
+    if is_dates_only:
         return lambda x: _format_datetime64_dateonly(
             x, nat_rep=nat_rep, date_format=date_format
         )
@@ -1815,7 +1788,7 @@ def get_format_datetime64(
 class Datetime64TZFormatter(Datetime64Formatter):
     def _format_strings(self) -> list[str]:
         """we by definition have a TZ"""
-        ido = is_dates_only(self.values)
+        ido = self.values._is_dates_only
         values = self.values.astype(object)
         formatter = self.formatter or get_format_datetime64(
             ido, date_format=self.date_format

pandas/tests/io/formats/test_format.py

@@ -3308,9 +3308,13 @@ def format_func(x):
         assert result == ["10:10", "12:12"]
 
     def test_datetime64formatter_tz_ms(self):
-        x = Series(
-            np.array(["2999-01-01", "2999-01-02", "NaT"], dtype="datetime64[ms]")
-        ).dt.tz_localize("US/Pacific")
+        x = (
+            Series(
+                np.array(["2999-01-01", "2999-01-02", "NaT"], dtype="datetime64[ms]")
+            )
+            .dt.tz_localize("US/Pacific")
+            ._values
+        )
         result = fmt.Datetime64TZFormatter(x).get_result()
         assert result[0].strip() == "2999-01-01 00:00:00-08:00"
         assert result[1].strip() == "2999-01-02 00:00:00-08:00"