danepitkin commented on code in PR #40482:
URL: https://github.com/apache/arrow/pull/40482#discussion_r1520728304
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python/pyarrow/src/arrow/python/arrow_to_pandas.cc:
##########
@@ -826,6 +796,145 @@ Status ConvertListsLike(PandasOptions options, const
ChunkedArray& data,
return Status::OK();
}
+template <typename T>
+enable_if_list_view<T, Status> ConvertListsLikeChunks(PyObject* numpy_array,
+ const ChunkedArray& data,
+ PyObject** out_values) {
+ using ListArrayT = typename TypeTraits<T>::ArrayType;
+ const auto& arr = static_cast<const ListArrayT&>(*data.chunk(0));
+ const bool has_nulls = data.null_count() > 0;
+ for (int64_t i = 0; i < arr.length(); ++i) {
+ if (has_nulls && arr.IsNull(i)) {
+ Py_INCREF(Py_None);
+ *out_values = Py_None;
+ } else {
+ // Need to subtract value_offset(0) since the original chunk might be a
slice
+ // into another array.
+ OwnedRef start(PyLong_FromLongLong(arr.value_offset(i)));
+ OwnedRef end(PyLong_FromLongLong(arr.value_offset(i) +
arr.value_length(i)));
+ OwnedRef slice(PySlice_New(start.obj(), end.obj(), nullptr));
+
+ if (ARROW_PREDICT_FALSE(slice.obj() == nullptr)) {
+ // Fall out of loop, will return from RETURN_IF_PYERROR
+ break;
+ }
+ *out_values = PyObject_GetItem(numpy_array, slice.obj());
+
+ if (*out_values == nullptr) {
+ // Fall out of loop, will return from RETURN_IF_PYERROR
+ break;
+ }
+ }
+ ++out_values;
+ }
+ RETURN_IF_PYERROR();
+
+ return Status::OK();
+}
+
+// template <typename T>
+// enable_if_list_like<T, Status> FlattenList(ArrayVector& value_arrays, const
ChunkedArray& data, PandasOptions options) {
+// using ListArrayT = typename TypeTraits<T>::ArrayType;
+// for (int c = 0; c < data.num_chunks(); c++) {
+// const auto& arr = checked_cast<const ListArrayT&>(*data.chunk(c));
+// std::shared_ptr<Array> flattened_values = arr.values()->Slice(
+// arr.value_offset(0), arr.value_offset(arr.length()) -
arr.value_offset(0));
+// if (arr.value_type()->id() == Type::EXTENSION) {
+// const auto& arr_ext = checked_cast<const
ExtensionArray&>(*flattened_values);
+// value_arrays.emplace_back(arr_ext.storage());
+// } else {
+// value_arrays.emplace_back(flattened_values);
+// }
+// }
+// return Status::OK();
+// }
+
+// template <typename T>
+// enable_if_list_view<T, Status> FlattenList(ArrayVector& value_arrays, const
ChunkedArray& data, PandasOptions options) {
+// using ListArrayT = typename TypeTraits<T>::ArrayType;
+// for (int c = 0; c < data.num_chunks(); c++) {
+// const auto& arr = checked_cast<const ListArrayT&>(*data.chunk(c));
+// std::vector<std::shared_ptr<Array>> slices;
+// for (int i = 0; i < arr.length(); i++) {
+// slices.push_back(arr.value_slice(i));
+// }
+// ARROW_ASSIGN_OR_RAISE(auto flattened_values, Concatenate(slices,
options.pool));
+// if (arr.value_type()->id() == Type::EXTENSION) {
+// const auto& arr_ext = checked_cast<const
ExtensionArray&>(*flattened_values);
+// value_arrays.emplace_back(arr_ext.storage());
+// } else {
+// value_arrays.emplace_back(flattened_values);
+// }
+// }
+// return Status::OK();
+// }
Review Comment:
If we don't want to reuse the C++ Flatten APIs, the commented functions
above are what duplicate implementations would look like in PyArrow.
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