handle min/max correctly for datetimelikes

Author: jreback

pandas/core/arrays/base.py

@@ -735,9 +735,7 @@ def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
         -------
         scalar
         """
-
-        # we dispatchh to the nanops operations
-        return op(self, axis=axis, skipna=skipna)
+        raise AbstractMethodError(self)
 
 
 class ExtensionScalarOpsMixin(ExtensionOpsMixin):

pandas/core/arrays/datetimelike.py

@@ -341,6 +341,42 @@ def _validate_frequency(cls, index, freq, **kwargs):
                              'does not conform to passed frequency {passed}'
                              .format(infer=inferred, passed=freq.freqstr))
 
+    # ------------------------------------------------------------------
+    # Reduction Methods
+
+    def _reduce(self, op, name, axis=0, skipna=True, numeric_only=None,
+                filter_type=None, **kwargs):
+        """Return a scalar result of performing the op
+
+        Parameters
+        ----------
+        op : callable
+            function to apply to the array
+        name : str
+            name of the function
+        axis : int, default 0
+            axis over which to apply, defined as 0 currently
+        skipna : bool, default True
+            if True, skip NaN values
+        numeric_only : bool, optional
+            if True, only perform numeric ops
+        filter_type : str, optional
+        kwargs : dict
+
+        Returns
+        -------
+        scalar
+        """
+        # if we have a reduction op already defined, use it
+        # this is important for min/max where tz's must be preserved
+        # and nanops is not geared towards this
+        # TODO(jreback): we are ignoring skipna
+        if hasattr(self, name):
+            return getattr(self, name)()
+
+        # we dispatch to the nanops operations
+        return op(self, axis=axis, skipna=skipna)
+
     # ------------------------------------------------------------------
     # Arithmetic Methods
 

pandas/core/dtypes/common.py

@@ -1236,7 +1236,8 @@ def is_datetime_or_timedelta_dtype(arr_or_dtype):
     if arr_or_dtype is None:
         return False
     tipo = _get_dtype_type(arr_or_dtype)
-    return issubclass(tipo, (np.datetime64, np.timedelta64))
+    return (issubclass(tipo, (np.datetime64, np.timedelta64)) or
+            is_datetime64tz_dtype(arr_or_dtype))
 
 
 def _is_unorderable_exception(e):

pandas/tests/dtypes/test_common.py

@@ -388,6 +388,8 @@ def test_is_datetime_or_timedelta_dtype():
     assert not com.is_datetime_or_timedelta_dtype(np.array(['a', 'b']))
 
     assert com.is_datetime_or_timedelta_dtype(np.datetime64)
+    assert com.is_datetime_or_timedelta_dtype(
+        DatetimeTZDtype("ns", "US/Eastern"))
     assert com.is_datetime_or_timedelta_dtype(np.timedelta64)
     assert com.is_datetime_or_timedelta_dtype(
         np.array([], dtype=np.timedelta64))