API: consistent __array__ for datetime-like ExtensionArrays

pandas/core/arrays/datetimelike.py

@@ -132,6 +132,12 @@ def asi8(self):
     # ------------------------------------------------------------------
     # Array-like Methods
 
+    def __array__(self, dtype=None):
+        # used for Timedelta/DatetimeArray, overwritten by PeriodArray
+        if is_object_dtype(dtype):
+            return np.array(list(self), dtype=object)
+        return self._data
+
     @property
     def shape(self):
         return (len(self),)

pandas/core/arrays/period.py

@@ -295,6 +295,10 @@ def freq(self):
         """Return the frequency object for this PeriodArray."""
         return self.dtype.freq
 
+    def __array__(self, dtype=None):
+        # overriding DatetimelikeArray
+        return np.array(list(self), dtype=object)
+
     # --------------------------------------------------------------------
     # Vectorized analogues of Period properties
 

pandas/core/indexes/datetimelike.py

@@ -234,6 +234,10 @@ class DatetimeIndexOpsMixin(DatetimeLikeArrayMixin):
     _resolution = cache_readonly(DatetimeLikeArrayMixin._resolution.fget)
     resolution = cache_readonly(DatetimeLikeArrayMixin.resolution.fget)
 
+    def __array__(self, dtype=None):
+        # TODO properly dispatch to EA
+        return Index.__array__(self)
+
     def equals(self, other):
         """
         Determines if two Index objects contain the same elements.

pandas/tests/arrays/test_datetimelike.py

@@ -126,6 +126,24 @@ def test_int_properties(self, datetime_index, propname):
 
         tm.assert_numpy_array_equal(result, expected)
 
+    def test_array(self, datetime_index):
+        arr = DatetimeArrayMixin(datetime_index)
+
+        result = np.asarray(arr)
+        expected = arr._data
+        assert result is expected
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.asarray(arr, dtype=object)
+        expected = np.array(list(arr), dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.asarray(arr, dtype='int64')
+        assert result is not arr.asi8
+        assert not np.may_share_memory(arr, result)
+        expected = arr.asi8.copy()
+        tm.assert_numpy_array_equal(result, expected)
+
 
 class TestTimedeltaArray(object):
     def test_from_tdi(self):
@@ -174,6 +192,24 @@ def test_int_properties(self, timedelta_index, propname):
 
         tm.assert_numpy_array_equal(result, expected)
 
+    def test_array(self, timedelta_index):
+        arr = TimedeltaArrayMixin(timedelta_index)
+
+        result = np.asarray(arr)
+        expected = arr._data
+        assert result is expected
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.asarray(arr, dtype=object)
+        expected = np.array(list(arr), dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.asarray(arr, dtype='int64')
+        assert result is not arr.asi8
+        assert not np.may_share_memory(arr, result)
+        expected = arr.asi8.copy()
+        tm.assert_numpy_array_equal(result, expected)
+
 
 class TestPeriodArray(object):
 
@@ -228,3 +264,16 @@ def test_int_properties(self, period_index, propname):
         expected = np.array(getattr(pi, propname))
 
         tm.assert_numpy_array_equal(result, expected)
+
+    def test_array(self, period_index):
+        arr = PeriodArray(period_index)
+
+        result = np.asarray(arr)
+        expected = np.array(list(arr), dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        result = np.asarray(arr, dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
+        with pytest.raises(TypeError):
+            np.asarray(arr, dtype='int64')

pandas/tests/extension/base/interface.py

@@ -6,6 +6,7 @@
 from pandas.core.dtypes.dtypes import ExtensionDtype
 
 import pandas as pd
+import pandas.util.testing as tm
 
 from .base import BaseExtensionTests
 
@@ -35,6 +36,10 @@ def test_array_interface(self, data):
         result = np.array(data)
         assert result[0] == data[0]
 
+        result = np.array(data, dtype=object)
+        expected = np.array(list(data), dtype=object)
+        tm.assert_numpy_array_equal(result, expected)
+
     def test_repr(self, data):
         ser = pd.Series(data)
         assert data.dtype.name in repr(ser)