use .view(_NS_DTYPE)

Author: reidy-p

pandas/core/indexes/base.py

@@ -41,6 +41,7 @@
     is_signed_integer_dtype,
     is_unsigned_integer_dtype,
     is_integer_dtype, is_float_dtype,
+    is_datetime64_dtype,
     is_datetime64_any_dtype,
     is_datetime64tz_dtype,
     is_timedelta64_dtype,
@@ -509,6 +510,7 @@ def _shallow_copy(self, values=None, **kwargs):
 
         # _simple_new expects an ndarray
         values = getattr(values, 'values', values)
+
         return self._simple_new(values, **attributes)
 
     def _shallow_copy_with_infer(self, values=None, **kwargs):

pandas/core/indexes/datetimes.py

@@ -611,15 +611,12 @@ def _simple_new(cls, values, name=None, freq=None, tz=None,
                            dtype=dtype, **kwargs)
             values = np.array(values, copy=False)
 
+        if not is_datetime64_dtype(values):
+            values = _ensure_int64(values).view(_NS_DTYPE)
+
         assert isinstance(values, np.ndarray), "values is not an np.ndarray"
         assert is_datetime64_dtype(values)
 
-        if is_object_dtype(values):
-            return cls(values, name=name, freq=freq, tz=tz,
-                       dtype=dtype, **kwargs).values
-        elif not is_datetime64_dtype(values):
-            values = _ensure_int64(values).view(_NS_DTYPE)
-
         result = object.__new__(cls)
         result._data = values
         result.name = name
@@ -2094,9 +2091,7 @@ def _generate_regular_range(start, end, periods, freq):
                              "if a 'period' is given.")
 
         data = np.arange(b, e, stride, dtype=np.int64)
-
-        # _simple_new is getting an array of int64 here
-        data = DatetimeIndex._simple_new(data, None, tz=tz)
+        data = DatetimeIndex._simple_new(data.view(_NS_DTYPE), None, tz=tz)
     else:
         if isinstance(start, Timestamp):
             start = start.to_pydatetime()