Update .array

We have Series/Index[datetime64[ns]] be a DatetimeArray

Author: TomAugspurger

pandas/arrays/__init__.py

@@ -5,15 +5,20 @@
 """
 from pandas.core.arrays import (
     IntervalArray, PeriodArray, Categorical, SparseArray, IntegerArray,
-    PandasArray
+    PandasArray,
+    DatetimeArrayMixin as DatetimeArray,
+    TimedeltaArrayMixin as TimedeltaArray,
+
 )
 
 
 __all__ = [
     'Categorical',
+    'DatetimeArray',
     'IntegerArray',
     'IntervalArray',
     'PandasArray',
     'PeriodArray',
     'SparseArray',
+    'TimedeltaArray',
 ]

pandas/core/base.py

@@ -15,11 +15,10 @@
 from pandas.util._validators import validate_bool_kwarg
 
 from pandas.core.dtypes.common import (
-    is_datetime64tz_dtype, is_datetimelike, is_extension_array_dtype,
-    is_extension_type, is_list_like, is_object_dtype, is_scalar)
-from pandas.core.dtypes.generic import (
-    ABCDataFrame, ABCDatetimeArray, ABCIndexClass, ABCSeries,
-    ABCTimedeltaArray)
+    is_datetime64_ns_dtype, is_datetime64tz_dtype, is_datetimelike,
+    is_extension_array_dtype, is_extension_type, is_list_like, is_object_dtype,
+    is_scalar, is_timedelta64_ns_dtype)
+from pandas.core.dtypes.generic import ABCDataFrame, ABCIndexClass, ABCSeries
 from pandas.core.dtypes.missing import isna
 
 from pandas.core import algorithms, common as com
@@ -851,16 +850,17 @@ def array(self):
         """
         result = self._values
 
-        if not (is_extension_array_dtype(result.dtype)
-                or isinstance(result, (ABCDatetimeArray, ABCTimedeltaArray))):
-            # TODO: Should this be a DatetimeArray or PandasArray
-            # for tz-naive data?
-            # DatetimeArray is a bit strange, since tz-naive
-            # arrays are an ExtensionArray, but the dtype is not
-            # an extension dtype.
-            from pandas.core.arrays.numpy_ import PandasArray
+        if is_datetime64_ns_dtype(result.dtype):
+            from pandas.arrays import DatetimeArray
+            result = DatetimeArray(result)
+        elif is_timedelta64_ns_dtype(result.dtype):
+            from pandas.arrays import TimedeltaArray
+            result = TimedeltaArray(result)
 
+        elif not is_extension_array_dtype(result.dtype):
+            from pandas.arrays import PandasArray
             result = PandasArray(result)
+
         return result
 
     def to_numpy(self, dtype=None, copy=False):

pandas/tests/test_base.py

@@ -1211,7 +1211,6 @@ def test_ndarray_values(array, expected):
 
 @pytest.mark.parametrize("arr", [
     np.array([1, 2, 3]),
-    np.array([1, 2, 3], dtype="datetime64[ns]"),
 ])
 def test_numpy_array(arr):
     ser = pd.Series(arr)
@@ -1223,7 +1222,10 @@ def test_numpy_array(arr):
 def test_numpy_array_all_dtypes(any_numpy_dtype):
     ser = pd.Series(dtype=any_numpy_dtype)
     result = ser.array
-    assert isinstance(result, PandasArray)
+    if is_datetime64_dtype(any_numpy_dtype):
+        assert isinstance(result, DatetimeArray)
+    else:
+        assert isinstance(result, PandasArray)
 
 
 @pytest.mark.parametrize("array, attr", [
@@ -1232,7 +1234,7 @@ def test_numpy_array_all_dtypes(any_numpy_dtype):
     (pd.core.arrays.integer_array([0, np.nan]), '_data'),
     (pd.core.arrays.IntervalArray.from_breaks([0, 1]), '_left'),
     (pd.SparseArray([0, 1]), '_sparse_values'),
-    # TODO: tz-naive Datetime. DatetimeArray or ndarray?
+    (DatetimeArray(np.array([1, 2], dtype="datetime64[ns]")), "_data"),
     # tz-aware Datetime
     (DatetimeArray(np.array(['2000-01-01T12:00:00',
                              '2000-01-02T12:00:00'],