Make DTI[tz]._values and Series[tz]._values return DTA (pandas-dev#24534

)

pandas/core/dtypes/concat.py

@@ -426,8 +426,7 @@ def _concat_datetime(to_concat, axis=0, typs=None):
     if any(typ.startswith('datetime') for typ in typs):
 
         if 'datetime' in typs:
-            to_concat = [np.array(x, copy=False).view(np.int64)
-                         for x in to_concat]
+            to_concat = [x.astype(np.int64, copy=False) for x in to_concat]
             return _concatenate_2d(to_concat, axis=axis).view(_NS_DTYPE)
         else:
             # when to_concat has different tz, len(typs) > 1.
@@ -451,7 +450,7 @@ def _convert_datetimelike_to_object(x):
     # if dtype is of datetimetz or timezone
     if x.dtype.kind == _NS_DTYPE.kind:
         if getattr(x, 'tz', None) is not None:
-            x = x.astype(object).values
+            x = np.asarray(x.astype(object))
         else:
             shape = x.shape
             x = tslib.ints_to_pydatetime(x.view(np.int64).ravel(),

pandas/core/indexes/datetimes.py

@@ -316,6 +316,12 @@ def _simple_new(cls, values, name=None, freq=None, tz=None, dtype=None):
         we require the we have a dtype compat for the values
         if we are passed a non-dtype compat, then coerce using the constructor
         """
+        if isinstance(values, DatetimeArray):
+            values = DatetimeArray(values, freq=freq, tz=tz, dtype=dtype)
+            tz = values.tz
+            freq = values.freq
+            values = values._data
+
         # DatetimeArray._simple_new will accept either i8 or M8[ns] dtypes
         assert isinstance(values, np.ndarray), type(values)
 
@@ -340,7 +346,7 @@ def _values(self):
         # tz-naive -> ndarray
         # tz-aware -> DatetimeIndex
         if self.tz is not None:
-            return self
+            return self._eadata
         else:
             return self.values
 
@@ -629,6 +635,9 @@ def intersection(self, other):
               not other.freq.isAnchored() or
               (not self.is_monotonic or not other.is_monotonic)):
             result = Index.intersection(self, other)
+            # Invalidate the freq of `result`, which may not be correct at
+            # this point, depending on the values.
+            result.freq = None
             result = self._shallow_copy(result._values, name=result.name,
                                         tz=result.tz, freq=None)
             if result.freq is None:

pandas/core/internals/blocks.py

@@ -34,7 +34,8 @@
     _isna_compat, array_equivalent, is_null_datelike_scalar, isna, notna)
 
 import pandas.core.algorithms as algos
-from pandas.core.arrays import Categorical, ExtensionArray
+from pandas.core.arrays import (
+    Categorical, DatetimeArrayMixin as DatetimeArray, ExtensionArray)
 from pandas.core.base import PandasObject
 import pandas.core.common as com
 from pandas.core.indexes.datetimes import DatetimeIndex
@@ -2437,8 +2438,14 @@ def _try_coerce_args(self, values, other):
         """ provide coercion to our input arguments """
 
         if isinstance(other, ABCDatetimeIndex):
-            # to store DatetimeTZBlock as object
-            other = other.astype(object).values
+            # May get a DatetimeIndex here. Unbox it.
+            other = other.array
+
+        if isinstance(other, DatetimeArray):
+            # hit in pandas/tests/indexing/test_coercion.py
+            # ::TestWhereCoercion::test_where_series_datetime64[datetime64tz]
+            # when falling back to ObjectBlock.where
+            other = other.astype(object)
 
         return values, other
 
@@ -2985,7 +2992,8 @@ def _try_coerce_args(self, values, other):
         elif (is_null_datelike_scalar(other) or
               (lib.is_scalar(other) and isna(other))):
             other = tslibs.iNaT
-        elif isinstance(other, self._holder):
+        elif isinstance(other, (self._holder, DatetimeArray)):
+            # TODO: DatetimeArray check will be redundant after GH#24024
             if other.tz != self.values.tz:
                 raise ValueError("incompatible or non tz-aware value")
             other = _block_shape(other.asi8, ndim=self.ndim)

pandas/core/series.py

@@ -477,7 +477,10 @@ def _values(self):
         """
         Return the internal repr of this data.
         """
-        return self._data.internal_values()
+        result = self._data.internal_values()
+        if isinstance(result, DatetimeIndex):
+            result = result._eadata
+        return result
 
     def _formatting_values(self):
         """
@@ -1602,10 +1605,6 @@ def unique(self):
         Categories (3, object): [a < b < c]
         """
         result = super(Series, self).unique()
-        if isinstance(result, DatetimeIndex):
-            # TODO: This should be unnecessary after Series._values returns
-            #  DatetimeArray
-            result = result._eadata
         return result
 
     def drop_duplicates(self, keep='first', inplace=False):

pandas/tests/indexing/test_coercion.py

@@ -31,7 +31,7 @@ def has_test(combo):
     for combo in combos:
         if not has_test(combo):
             msg = 'test method is not defined: {0}, {1}'
-            raise AssertionError(msg.format(type(cls), combo))
+            raise AssertionError(msg.format(cls.__name__, combo))
 
     yield
 

pandas/tests/test_base.py

@@ -17,12 +17,12 @@
                     PeriodIndex, Timedelta, IntervalIndex, Interval,
                     CategoricalIndex, Timestamp, DataFrame, Panel)
 from pandas.core.arrays import (
+    PandasArray,
     DatetimeArrayMixin as DatetimeArray,
     TimedeltaArrayMixin as TimedeltaArray,
 )
 from pandas.compat import StringIO, PYPY, long
 from pandas.compat.numpy import np_array_datetime64_compat
-from pandas.core.arrays import PandasArray
 from pandas.core.accessor import PandasDelegate
 from pandas.core.base import PandasObject, NoNewAttributesMixin
 from pandas.core.indexes.datetimelike import DatetimeIndexOpsMixin
@@ -388,11 +388,9 @@ def test_value_counts_unique_nunique(self):
                 for r in result:
                     assert isinstance(r, Timestamp)
 
-                # TODO(#24024) once orig._values returns DTA, remove
-                #  the `._eadata` below
                 tm.assert_numpy_array_equal(
                     result.astype(object),
-                    orig._values._eadata.astype(object))
+                    orig._values.astype(object))
             else:
                 tm.assert_numpy_array_equal(result, orig.values)
 
@@ -418,9 +416,7 @@ def test_value_counts_unique_nunique_null(self):
                     else:
                         o = o.copy()
                         o[0:2] = iNaT
-                        # TODO(#24024) once Series._values returns DTA, remove
-                        #  the `._eadata` here
-                        values = o._values._eadata
+                        values = o._values
 
                 elif needs_i8_conversion(o):
                     values[0:2] = iNaT
@@ -1158,7 +1154,7 @@ def test_iter_box(self):
     (np.array(['a', 'b']), np.ndarray, 'object'),
     (pd.Categorical(['a', 'b']), pd.Categorical, 'category'),
     (pd.DatetimeIndex(['2017', '2018']), np.ndarray, 'datetime64[ns]'),
-    (pd.DatetimeIndex(['2017', '2018'], tz="US/Central"), pd.DatetimeIndex,
+    (pd.DatetimeIndex(['2017', '2018'], tz="US/Central"), DatetimeArray,
      'datetime64[ns, US/Central]'),
     (pd.TimedeltaIndex([10**10]), np.ndarray, 'm8[ns]'),
     (pd.PeriodIndex([2018, 2019], freq='A'), pd.core.arrays.PeriodArray,