more test fixes

Author: jorisvandenbossche

pandas/core/arrays/sparse/array.py

@@ -975,6 +975,8 @@ def __getitem__(
             # _NestedSequence[Union[bool, int]]], ...]]"
             data_slice = self.to_dense()[key]  # type: ignore[index]
         elif isinstance(key, slice):
+            if key == slice(None):
+                return type(self)._simple_new(self.sp_values, self.sp_index, self.dtype)
             # Avoid densifying when handling contiguous slices
             if key.step is None or key.step == 1:
                 start = 0 if key.start is None else key.start

pandas/core/dtypes/astype.py

@@ -25,6 +25,7 @@
 )
 from pandas.core.dtypes.dtypes import (
     CategoricalDtype,
+    DatetimeTZDtype,
     ExtensionDtype,
     IntervalDtype,
     NumpyEADtype,
@@ -286,7 +287,9 @@ def astype_is_view(dtype: DtypeObj, new_dtype: DtypeObj) -> bool:
         new_dtype = getattr(new_dtype, "numpy_dtype", new_dtype)
         return getattr(dtype, "unit", None) == getattr(new_dtype, "unit", None)
 
-    elif new_dtype == object and isinstance(dtype, (PeriodDtype, IntervalDtype)):
+    elif new_dtype == object and isinstance(
+        dtype, (DatetimeTZDtype, PeriodDtype, IntervalDtype)
+    ):
         return False
 
     elif isinstance(dtype, CategoricalDtype) and not isinstance(

pandas/tests/base/test_conversion.py

@@ -285,7 +285,7 @@ def test_array(arr, attr, index_or_series):
         arr = getattr(arr, attr)
         result = getattr(result, attr)
 
-    assert result is arr
+    assert np.shares_memory(result, arr)
 
 
 def test_array_multiindex_raises():

pandas/tests/extension/base/setitem.py

@@ -510,4 +510,9 @@ def test_readonly_propagates_to_numpy_array_method(self, data):
         # non-NA fill value should always result in a copy
         if data.isna().any():
             arr = data.to_numpy(copy=False, na_value=data[0])
-            assert arr.flags.writeable
+            if isinstance(data.dtype, pd.ArrowDtype) and data.dtype.kind == "f":
+                # for float dtype, after the fillna, the conversion from pyarrow to
+                # numpy is zero-copy, and pyarrow will mark the array as readonly
+                assert not arr.flags.writeable
+            else:
+                assert arr.flags.writeable

pandas/tests/extension/json/array.py

@@ -155,7 +155,6 @@ def __array__(self, dtype=None, copy=None):
             raise ValueError(
                 "Unable to avoid copy while creating an array as requested."
             )
-
         if dtype is None:
             dtype = object
         if dtype == object:

pandas/tests/extension/json/test_json.py

@@ -418,6 +418,12 @@ def test_setitem_slice_array(self, data):
     def test_setitem_invalid(self, data, invalid_scalar):
         super().test_setitem_invalid(data, invalid_scalar)
 
+    @pytest.mark.xfail(
+        reason="result readonly flag is incorrect and does not support na_value"
+    )
+    def test_readonly_propagates_to_numpy_array_method(self, data):
+        super().test_readonly_propagates_to_numpy_array_method(data)
+
     @pytest.mark.xfail(reason="only integer scalar arrays can be converted")
     def test_setitem_2d_values(self, data):
         super().test_setitem_2d_values(data)