checkpoint tests passing

pandas-dev · jreback · Nov 8, 2020 · Oct 26, 2020 · Oct 26, 2020 · Oct 26, 2020
commit bdad92ce718542b01b0fc7a93f8a1afd53fed210
diff --git a/pandas/core/arrays/datetimelike.py b/pandas/core/arrays/datetimelike.py
@@ -1266,18 +1266,15 @@ def min(self, axis=None, skipna=True, *args, **kwargs):
         nv.validate_min(args, kwargs)
         nv.validate_minmax_axis(axis, self.ndim)
 
-        # View as M8[ns] to get correct NaT/masking semantics for PeriodDtype
-        result = nanops.nanmin(
-            self._ndarray.view("M8[ns]"), skipna=skipna, mask=self.isna()
-        )
+        if is_period_dtype(self.dtype):
+            result = self.to_timestamp("S").min(axis=axis, skipna=skipna)
+            if result is not NaT:
+                result = result.to_period(self.freq)
+            return result
+
+        result = nanops.nanmin(self._ndarray, skipna=skipna, mask=self.isna())
         if lib.is_scalar(result):
-            if isna(result):
-                # Period._from_ordinal does not handle NaT gracefully
-                return NaT
-            # nanops may unwantedly cast to Timestamp
-            result = getattr(result, "value", result)
             return self._box_func(result)
-        result = result.astype("i8", copy=False)
         return self._from_backing_data(result)
 
     def max(self, axis=None, skipna=True, *args, **kwargs):
@@ -1296,18 +1293,15 @@ def max(self, axis=None, skipna=True, *args, **kwargs):
         nv.validate_max(args, kwargs)
         nv.validate_minmax_axis(axis, self.ndim)
 
-        # View as M8[ns] to get correct NaT/masking semantics for PeriodDtype
-        result = nanops.nanmax(
-            self._ndarray.view("M8[ns]"), skipna=skipna, mask=self.isna()
-        )
+        if is_period_dtype(self.dtype):
+            result = self.to_timestamp("S").max(axis=axis, skipna=skipna)
+            if result is not NaT:
+                result = result.to_period(self.freq)
+            return result
+
+        result = nanops.nanmax(self._ndarray, skipna=skipna, mask=self.isna())
         if lib.is_scalar(result):
-            if isna(result):
-                # Period._from_ordinal does not handle NaT gracefully
-                return NaT
-            # nanops may unwantedly cast to Timestamp
-            result = getattr(result, "value", result)
             return self._box_func(result)
-        result = result.astype("i8", copy=False)
         return self._from_backing_data(result)
 
     def mean(self, skipna=True):

diff --git a/pandas/core/nanops.py b/pandas/core/nanops.py
@@ -341,6 +341,7 @@ def _wrap_results(result, dtype: DtypeObj, fill_value=None):
             if result == fill_value:
                 result = np.nan
             if tz is not None:
+                # we get here e.g. via nanmean when we call it on a DTA[tz]
                 result = Timestamp(result, tz=tz)
             elif isna(result):
                 result = np.datetime64("NaT", "ns")