REF: de-duplicate period-dispatch (#50215)

* REF: de-duplicate period-dispatch

* mypy fixup

Author: jbrockmendel

pandas/core/arrays/datetimelike.py

@@ -4,6 +4,7 @@
     datetime,
     timedelta,
 )
+from functools import wraps
 import operator
 from typing import (
     TYPE_CHECKING,
@@ -57,6 +58,7 @@
     DatetimeLikeScalar,
     Dtype,
     DtypeObj,
+    F,
     NpDtype,
     PositionalIndexer2D,
     PositionalIndexerTuple,
@@ -157,6 +159,31 @@
 DatetimeLikeArrayT = TypeVar("DatetimeLikeArrayT", bound="DatetimeLikeArrayMixin")
 
 
+def _period_dispatch(meth: F) -> F:
+    """
+    For PeriodArray methods, dispatch to DatetimeArray and re-wrap the results
+    in PeriodArray.  We cannot use ._ndarray directly for the affected
+    methods because the i8 data has different semantics on NaT values.
+    """
+
+    @wraps(meth)
+    def new_meth(self, *args, **kwargs):
+        if not is_period_dtype(self.dtype):
+            return meth(self, *args, **kwargs)
+
+        arr = self.view("M8[ns]")
+        result = meth(arr, *args, **kwargs)
+        if result is NaT:
+            return NaT
+        elif isinstance(result, Timestamp):
+            return self._box_func(result.value)
+
+        res_i8 = result.view("i8")
+        return self._from_backing_data(res_i8)
+
+    return cast(F, new_meth)
+
+
 class DatetimeLikeArrayMixin(OpsMixin, NDArrayBackedExtensionArray):
     """
     Shared Base/Mixin class for DatetimeArray, TimedeltaArray, PeriodArray
@@ -1546,6 +1573,15 @@ def __isub__(self: DatetimeLikeArrayT, other) -> DatetimeLikeArrayT:
     # --------------------------------------------------------------
     # Reductions
 
+    @_period_dispatch
+    def _quantile(
+        self: DatetimeLikeArrayT,
+        qs: npt.NDArray[np.float64],
+        interpolation: str,
+    ) -> DatetimeLikeArrayT:
+        return super()._quantile(qs=qs, interpolation=interpolation)
+
+    @_period_dispatch
     def min(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
         """
         Return the minimum value of the Array or minimum along
@@ -1560,21 +1596,10 @@ def min(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
         nv.validate_min((), kwargs)
         nv.validate_minmax_axis(axis, self.ndim)
 
-        if is_period_dtype(self.dtype):
-            # pass datetime64 values to nanops to get correct NaT semantics
-            result = nanops.nanmin(
-                self._ndarray.view("M8[ns]"), axis=axis, skipna=skipna
-            )
-            if result is NaT:
-                return NaT
-            result = result.view("i8")
-            if axis is None or self.ndim == 1:
-                return self._box_func(result)
-            return self._from_backing_data(result)
-
         result = nanops.nanmin(self._ndarray, axis=axis, skipna=skipna)
         return self._wrap_reduction_result(axis, result)
 
+    @_period_dispatch
     def max(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
         """
         Return the maximum value of the Array or maximum along
@@ -1589,18 +1614,6 @@ def max(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
         nv.validate_max((), kwargs)
         nv.validate_minmax_axis(axis, self.ndim)
 
-        if is_period_dtype(self.dtype):
-            # pass datetime64 values to nanops to get correct NaT semantics
-            result = nanops.nanmax(
-                self._ndarray.view("M8[ns]"), axis=axis, skipna=skipna
-            )
-            if result is NaT:
-                return result
-            result = result.view("i8")
-            if axis is None or self.ndim == 1:
-                return self._box_func(result)
-            return self._from_backing_data(result)
-
         result = nanops.nanmax(self._ndarray, axis=axis, skipna=skipna)
         return self._wrap_reduction_result(axis, result)
 
@@ -1641,22 +1654,13 @@ def mean(self, *, skipna: bool = True, axis: AxisInt | None = 0):
         )
         return self._wrap_reduction_result(axis, result)
 
+    @_period_dispatch
     def median(self, *, axis: AxisInt | None = None, skipna: bool = True, **kwargs):
         nv.validate_median((), kwargs)
 
         if axis is not None and abs(axis) >= self.ndim:
             raise ValueError("abs(axis) must be less than ndim")
 
-        if is_period_dtype(self.dtype):
-            # pass datetime64 values to nanops to get correct NaT semantics
-            result = nanops.nanmedian(
-                self._ndarray.view("M8[ns]"), axis=axis, skipna=skipna
-            )
-            result = result.view("i8")
-            if axis is None or self.ndim == 1:
-                return self._box_func(result)
-            return self._from_backing_data(result)
-
         result = nanops.nanmedian(self._ndarray, axis=axis, skipna=skipna)
         return self._wrap_reduction_result(axis, result)
 

pandas/core/arrays/period.py

@@ -672,31 +672,22 @@ def searchsorted(
     ) -> npt.NDArray[np.intp] | np.intp:
         npvalue = self._validate_setitem_value(value).view("M8[ns]")
 
-        # Cast to M8 to get datetime-like NaT placement
+        # Cast to M8 to get datetime-like NaT placement,
+        #  similar to dtl._period_dispatch
         m8arr = self._ndarray.view("M8[ns]")
         return m8arr.searchsorted(npvalue, side=side, sorter=sorter)
 
     def fillna(self, value=None, method=None, limit=None) -> PeriodArray:
         if method is not None:
-            # view as dt64 so we get treated as timelike in core.missing
+            # view as dt64 so we get treated as timelike in core.missing,
+            #  similar to dtl._period_dispatch
             dta = self.view("M8[ns]")
             result = dta.fillna(value=value, method=method, limit=limit)
             # error: Incompatible return value type (got "Union[ExtensionArray,
             # ndarray[Any, Any]]", expected "PeriodArray")
             return result.view(self.dtype)  # type: ignore[return-value]
         return super().fillna(value=value, method=method, limit=limit)
 
-    def _quantile(
-        self: PeriodArray,
-        qs: npt.NDArray[np.float64],
-        interpolation: str,
-    ) -> PeriodArray:
-        # dispatch to DatetimeArray implementation
-        dtres = self.view("M8[ns]")._quantile(qs, interpolation)
-        # error: Incompatible return value type (got "Union[ExtensionArray,
-        # ndarray[Any, Any]]", expected "PeriodArray")
-        return dtres.view(self.dtype)  # type: ignore[return-value]
-
     # ------------------------------------------------------------------
     # Arithmetic Methods