TST: tests using invalid_scalar fixture

pandas/core/arrays/sparse/array.py

@@ -56,6 +56,7 @@
     is_datetime64tz_dtype,
     is_dtype_equal,
     is_integer,
+    is_list_like,
     is_object_dtype,
     is_scalar,
     is_string_dtype,
@@ -927,6 +928,14 @@ def __getitem__(
                 indices = np.arange(len(self), dtype=np.int32)[key]
                 return self.take(indices)
 
+        elif not is_list_like(key):
+            # e.g. "foo" or 2.5
+            # exception message copied from numpy
+            raise IndexError(
+                r"only integers, slices (`:`), ellipsis (`...`), numpy.newaxis "
+                r"(`None`) and integer or boolean arrays are valid indices"
+            )
+
         else:
             # TODO: I think we can avoid densifying when masking a
             # boolean SparseArray with another. Need to look at the

pandas/core/arrays/string_arrow.py

@@ -322,6 +322,13 @@ def __getitem__(
                 elif item[1] is Ellipsis:
                     item = item[0]
 
+        if is_scalar(item) and not is_integer(item):
+            # e.g. "foo" or 2.5
+            # exception message copied from numpy
+            raise IndexError(
+                r"only integers, slices (`:`), ellipsis (`...`), numpy.newaxis "
+                r"(`None`) and integer or boolean arrays are valid indices"
+            )
         # We are not an array indexer, so maybe e.g. a slice or integer
         # indexer. We dispatch to pyarrow.
         value = self._data[item]
@@ -392,6 +399,11 @@ def _cmp_method(self, other, op):
         # TODO(ARROW-9429): Add a .to_numpy() to ChunkedArray
         return BooleanArray._from_sequence(result.to_pandas().values)
 
+    def insert(self, loc: int, item):
+        if not isinstance(item, str) and item is not libmissing.NA:
+            raise TypeError("Scalar must be NA or str")
+        return super().insert(loc, item)
+
     def __setitem__(self, key: int | slice | np.ndarray, value: Any) -> None:
         """Set one or more values inplace.
 

pandas/tests/extension/base/getitem.py

@@ -120,6 +120,33 @@ def test_getitem_scalar(self, data):
         result = pd.Series(data)[0]
         assert isinstance(result, data.dtype.type)
 
+    def test_getitem_invalid(self, data):
+        # TODO: box over scalar, [scalar], (scalar,)?
+
+        msg = (
+            r"only integers, slices \(`:`\), ellipsis \(`...`\), numpy.newaxis "
+            r"\(`None`\) and integer or boolean arrays are valid indices"
+        )
+        with pytest.raises(IndexError, match=msg):
+            data["foo"]
+        with pytest.raises(IndexError, match=msg):
+            data[2.5]
+
+        ub = len(data)
+        msg = "|".join(
+            [
+                "list index out of range",  # json
+                "index out of bounds",  # pyarrow
+                "Out of bounds access",  # Sparse
+                f"index {ub+1} is out of bounds for axis 0 with size {ub}",
+                f"index -{ub+1} is out of bounds for axis 0 with size {ub}",
+            ]
+        )
+        with pytest.raises(IndexError, match=msg):
+            data[ub + 1]
+        with pytest.raises(IndexError, match=msg):
+            data[-ub - 1]
+
     def test_getitem_scalar_na(self, data_missing, na_cmp, na_value):
         result = data_missing[0]
         assert na_cmp(result, na_value)

pandas/tests/extension/base/setitem.py

@@ -367,3 +367,11 @@ def test_delitem_series(self, data):
         expected = ser[taker]
         del ser[1]
         self.assert_series_equal(ser, expected)
+
+    def test_setitem_invalid(self, data, invalid_scalar):
+        msg = ""  # messages vary by subclass, so we do not test it
+        with pytest.raises((ValueError, TypeError), match=msg):
+            data[0] = invalid_scalar
+
+        with pytest.raises((ValueError, TypeError), match=msg):
+            data[:] = invalid_scalar

pandas/tests/extension/json/array.py

@@ -32,7 +32,10 @@
 from pandas._typing import type_t
 
 from pandas.core.dtypes.cast import construct_1d_object_array_from_listlike
-from pandas.core.dtypes.common import pandas_dtype
+from pandas.core.dtypes.common import (
+    is_list_like,
+    pandas_dtype,
+)
 
 import pandas as pd
 from pandas.api.extensions import (
@@ -103,6 +106,13 @@ def __getitem__(self, item):
         elif isinstance(item, slice):
             # slice
             return type(self)(self.data[item])
+        elif not is_list_like(item):
+            # e.g. "foo" or 2.5
+            # exception message copied from numpy
+            raise IndexError(
+                r"only integers, slices (`:`), ellipsis (`...`), numpy.newaxis "
+                r"(`None`) and integer or boolean arrays are valid indices"
+            )
         else:
             item = pd.api.indexers.check_array_indexer(self, item)
             if is_bool_dtype(item.dtype):

pandas/tests/extension/test_numpy.py

@@ -363,6 +363,11 @@ def test_concat(self, data, in_frame):
 
 
 class TestSetitem(BaseNumPyTests, base.BaseSetitemTests):
+    @skip_nested
+    def test_setitem_invalid(self, data, invalid_scalar):
+        # object dtype can hold anything, so doesn't raise
+        super().test_setitem_invalid(data, invalid_scalar)
+
     @skip_nested
     def test_setitem_sequence_broadcasts(self, data, box_in_series):
         # ValueError: cannot set using a list-like indexer with a different

pandas/tests/extension/test_string.py

@@ -160,13 +160,6 @@ def test_value_counts(self, all_data, dropna):
     def test_value_counts_with_normalize(self, data):
         pass
 
-    def test_insert_invalid(self, data, invalid_scalar, request):
-        if data.dtype.storage == "pyarrow":
-            mark = pytest.mark.xfail(reason="casts invalid_scalar to string")
-            request.node.add_marker(mark)
-
-        super().test_insert_invalid(data, invalid_scalar)
-
 
 class TestCasting(base.BaseCastingTests):
     pass

pandas/tests/indexes/common.py

@@ -403,6 +403,33 @@ def test_insert_base(self, index):
         # test 0th element
         assert index[0:4].equals(result.insert(0, index[0]))
 
+    def test_insert_out_of_bounds(self, index):
+        # TypeError/IndexError matches what np.insert raises in these cases
+
+        if len(index) > 0:
+            err = TypeError
+        else:
+            err = IndexError
+        if len(index) == 0:
+            # 0 vs 0.5 in error message varies with numpy version
+            msg = "index (0|0.5) is out of bounds for axis 0 with size 0"
+        else:
+            msg = "slice indices must be integers or None or have an __index__ method"
+        with pytest.raises(err, match=msg):
+            index.insert(0.5, "foo")
+
+        msg = "|".join(
+            [
+                r"index -?\d+ is out of bounds for axis 0 with size \d+",
+                "loc must be an integer between",
+            ]
+        )
+        with pytest.raises(IndexError, match=msg):
+            index.insert(len(index) + 1, 1)
+
+        with pytest.raises(IndexError, match=msg):
+            index.insert(-len(index) - 1, 1)
+
     def test_delete_base(self, index):
         if not len(index):
             return