GH-9: [Python] Fix non-subscriptable error (#32)

* fix: The type parameter of array should be covariant (apache#253)

* release 20.0.0.20250716 (apache#254)

* Add py.typed file to signify that the library is typed

See the relevant PEP https://peps.python.org/pep-0561

* Prepare `pyarrow-stubs` for history merging

MINOR: [Python] Prepare `pyarrow-stubs` for history merging

Co-authored-by: ZhengYu, Xu <zen-xu@outlook.com>

* Add `ty` configuration and suppress error codes

* One line per rule

* Add licence header from original repo for all `.pyi` files

* Revert "Add licence header from original repo for all `.pyi` files"

This reverts commit 1631f39.

* Prepare for licence merging

* Exclude `stubs` from `rat` test

* Add Apache licence clause to `py.typed`

* Reduce list

* Add `ty` as a step in the action

* Run in the correct directory

* Remove `check` from `pip`

* Fix `unresolved-reference` error

* Revert "Fix `unresolved-reference` error"

This reverts commit 7ee3d2f.

* Fix non-subscriptable issues

---------

Co-authored-by: Patrick J. Roddy <patrickjamesroddy@gmail.com>

Author: rok

python/pyarrow/pandas_compat.py

@@ -755,8 +755,8 @@ def _reconstruct_block(item, columns=None, extension_columns=None, return_block=
         # create ExtensionBlock
         arr = item['py_array']
         assert len(placement) == 1
-        name = columns[placement[0]]
-        pandas_dtype = extension_columns[name]
+        name = columns.get(placement[0], None)
+        pandas_dtype = extension_columns.get(name, None)
         if not hasattr(pandas_dtype, '__from_arrow__'):
             raise ValueError("This column does not support to be converted "
                              "to a pandas ExtensionArray")

python/pyarrow/tests/test_cuda_numba_interop.py

@@ -49,7 +49,7 @@ def teardown_module(module):
 @pytest.mark.parametrize("c", range(len(context_choice_ids)),
                          ids=context_choice_ids)
 def test_context(c):
-    ctx, nb_ctx = context_choices[c]
+    ctx, nb_ctx = context_choices.get(c, (None, None))
     assert ctx.handle == nb_ctx.handle.value
     assert ctx.handle == ctx.to_numba().handle.value
     ctx2 = cuda.Context.from_numba(nb_ctx)
@@ -83,7 +83,7 @@ def make_random_buffer(size, target='host', dtype='uint8', ctx=None):
 @pytest.mark.parametrize("dtype", dtypes, ids=dtypes)
 @pytest.mark.parametrize("size", [0, 1, 8, 1000])
 def test_from_object(c, dtype, size):
-    ctx, nb_ctx = context_choices[c]
+    ctx, nb_ctx = context_choices.get(c, (None, None))
     arr, cbuf = make_random_buffer(size, target='device', dtype=dtype, ctx=ctx)
 
     # Creating device buffer from numba DeviceNDArray:
@@ -161,7 +161,7 @@ def __cuda_array_interface__(self):
                          ids=context_choice_ids)
 @pytest.mark.parametrize("dtype", dtypes, ids=dtypes)
 def test_numba_memalloc(c, dtype):
-    ctx, nb_ctx = context_choices[c]
+    ctx, nb_ctx = context_choices.get(c, (None, None))
     dtype = np.dtype(dtype)
     # Allocate memory using numba context
     # Warning: this will not be reflected in pyarrow context manager
@@ -184,7 +184,7 @@ def test_numba_memalloc(c, dtype):
                          ids=context_choice_ids)
 @pytest.mark.parametrize("dtype", dtypes, ids=dtypes)
 def test_pyarrow_memalloc(c, dtype):
-    ctx, nb_ctx = context_choices[c]
+    ctx, nb_ctx = context_choices.get(c, (None, None))
     size = 10
     arr, cbuf = make_random_buffer(size, target='device', dtype=dtype, ctx=ctx)
 
@@ -198,7 +198,7 @@ def test_pyarrow_memalloc(c, dtype):
                          ids=context_choice_ids)
 @pytest.mark.parametrize("dtype", dtypes, ids=dtypes)
 def test_numba_context(c, dtype):
-    ctx, nb_ctx = context_choices[c]
+    ctx, nb_ctx = context_choices.get(c, (None, None))
     size = 10
     with nb_cuda.gpus[0]:
         arr, cbuf = make_random_buffer(size, target='device',
@@ -217,7 +217,7 @@ def test_numba_context(c, dtype):
                          ids=context_choice_ids)
 @pytest.mark.parametrize("dtype", dtypes, ids=dtypes)
 def test_pyarrow_jit(c, dtype):
-    ctx, nb_ctx = context_choices[c]
+    ctx, nb_ctx = context_choices.get(c, (None, None))
 
     @nb_cuda.jit
     def increment_by_one(an_array):

python/pyarrow/tests/test_gdb.py

@@ -159,7 +159,7 @@ def select_frame(self, func_name):
         if m is None:
             pytest.fail(f"Could not select frame for function {func_name}")
 
-        frame_num = int(m[1])
+        frame_num = int(m.get(1, None))
         out = self.run_command(f"frame {frame_num}")
         assert f"in {func_name}" in out
 

python/stubs/__lib_pxi/array.pyi

@@ -1992,6 +1992,7 @@ class Array(_PandasConvertible[pd.Series], Generic[_Scalar_co]):
     def __getitem__(self, key: int) -> _Scalar_co: ...
     @overload
     def __getitem__(self, key: builtins.slice) -> Self: ...
+    def __getitem__(self, key):
         """
         Slice or return value at given index