clusterfork

pandas/core/arrays/datetimelike.py

@@ -1018,7 +1018,7 @@ def _add_nat(self):
         # and datetime dtypes
         result = np.zeros(len(self), dtype=np.int64)
         result.fill(iNaT)
-        return type(self)(result, dtype=self.dtype, freq=None)
+        return type(self)._simple_new(result, dtype=self.dtype, freq=None)
 
     def _sub_nat(self):
         """

pandas/core/arrays/datetimes.py

@@ -141,7 +141,7 @@ def wrapper(self, other):
         else:
             if isinstance(other, list):
                 try:
-                    other = type(self)._from_sequence(other)
+                    other = type(self)._from_sequence_dti(other)
                 except ValueError:
                     other = np.array(other, dtype=np.object_)
             elif not isinstance(other, (np.ndarray, ABCIndexClass, ABCSeries,
@@ -173,7 +173,7 @@ def wrapper(self, other):
                         not hasattr(other, 'asi8')):
                     # e.g. other.dtype == 'datetime64[s]'
                     # or an object-dtype ndarray
-                    other = type(self)._from_sequence(other)
+                    other = type(self)._from_sequence_dti(other)
 
                 result = op(self.view('i8'), other.view('i8'))
                 o_mask = other._isnan
@@ -327,9 +327,13 @@ def _simple_new(cls, values, freq=None, dtype=None):
         return cls(values, freq=freq, dtype=dtype)
 
     @classmethod
-    def _from_sequence(cls, data, dtype=None, copy=False,
-                       tz=None, freq=None,
-                       dayfirst=False, yearfirst=False, ambiguous='raise'):
+    def _from_sequence(cls, scalars, dtype=None, copy=False):
+        assert all(isinstance(x, Timestamp) for x in scalars)
+
+    @classmethod
+    def _from_sequence_dti(cls, data, dtype=None, copy=False,
+                           tz=None, freq=None,
+                           dayfirst=False, yearfirst=False, ambiguous='raise'):
 
         freq, freq_infer = dtl.maybe_infer_freq(freq)
 
@@ -735,7 +739,9 @@ def _add_delta(self, delta):
         result : DatetimeArray
         """
         new_values = super(DatetimeArray, self)._add_delta(delta)
-        return type(self)._from_sequence(new_values, tz=self.tz, freq='infer')
+        result = type(self)(new_values, dtype=self.dtype)
+        result._freq = result.inferred_freq
+        return result
 
     # -----------------------------------------------------------------
     # Timezone Conversion and Localization Methods
@@ -1074,8 +1080,8 @@ def normalize(self):
             new_values[not_null] = new_values[not_null] - adjustment
         else:
             new_values = conversion.normalize_i8_timestamps(self.asi8, self.tz)
-        return type(self)._from_sequence(new_values,
-                                         freq='infer').tz_localize(self.tz)
+        result = type(self)._from_sequence_dti(new_values, freq='infer')
+        resutn result.tz_localize(self.tz)
 
     def to_period(self, freq=None):
         """
@@ -1664,6 +1670,7 @@ def sequence_to_dt64ns(data, dtype=None, copy=False,
     """
 
     inferred_freq = None
+    inferred_tz = None
 
     dtype = _validate_dt64_dtype(dtype)
 
@@ -1712,9 +1719,18 @@ def sequence_to_dt64ns(data, dtype=None, copy=False,
         tz = maybe_infer_tz(tz, data.tz)
         result = data._data
 
-    elif is_datetime64_dtype(data):
+    elif inferred_tz is not None:
+        # data returned from objects_to_datetime64ns represenet unix#
+        #  timestamps
+        assert data.dtype == 'i8', data.dtype
+        result = data.view(_NS_DTYPE)
+
+    elif is_datetime64_dtype(data) or data.dtype == 'i8':
         # tz-naive DatetimeArray or ndarray[datetime64]
         data = getattr(data, "_data", data)
+        if data.dtype == 'i8':
+            data = data.view(_NS_DTYPE)
+
         if data.dtype != _NS_DTYPE:
             data = conversion.ensure_datetime64ns(data)