pitrou commented on a change in pull request #8337:
URL: https://github.com/apache/arrow/pull/8337#discussion_r500868740
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File path: cpp/src/arrow/python/arrow_to_pandas.cc
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@@ -791,6 +791,111 @@ Status ConvertListsLike(PandasOptions options, const
ChunkedArray& data,
return Status::OK();
}
+Status ConvertMap(PandasOptions options, const ChunkedArray& data,
+ PyObject** out_values) {
+ // Get columns of underlying key/item arrays
+ std::vector<std::shared_ptr<Array>> key_arrays;
+ std::vector<std::shared_ptr<Array>> item_arrays;
+ for (int c = 0; c < data.num_chunks(); ++c) {
+ const auto& map_arr = checked_cast<const MapArray&>(*data.chunk(c));
+ key_arrays.emplace_back(map_arr.keys());
+ item_arrays.emplace_back(map_arr.items());
+ }
+
+ const auto& map_type = checked_cast<const MapType&>(*data.type());
+ auto key_type = map_type.key_type();
+ auto item_type = map_type.item_type();
+
+ // ARROW-6899: Convert dictionary-encoded children to dense instead of
+ // failing below. A more efficient conversion than this could be done later
+ if (key_type->id() == Type::DICTIONARY) {
+ auto dense_type = checked_cast<const
DictionaryType&>(*key_type).value_type();
+ RETURN_NOT_OK(DecodeDictionaries(options.pool, dense_type, &key_arrays));
+ key_type = dense_type;
+ }
+ if (item_type->id() == Type::DICTIONARY) {
+ auto dense_type = checked_cast<const
DictionaryType&>(*item_type).value_type();
+ RETURN_NOT_OK(DecodeDictionaries(options.pool, dense_type, &item_arrays));
+ item_type = dense_type;
+ }
+
+ // See notes in MakeInnerOptions.
+ options = MakeInnerOptions(std::move(options));
+ // Don't blindly convert because timestamps in lists are handled differently.
+ options.timestamp_as_object = true;
+
+ auto flat_keys = std::make_shared<ChunkedArray>(key_arrays, key_type);
+ auto flat_items = std::make_shared<ChunkedArray>(item_arrays, item_type);
+ OwnedRef list_item;
+ OwnedRef key_value;
+ OwnedRef item_value;
+ OwnedRefNoGIL owned_numpy_keys;
+ RETURN_NOT_OK(ConvertChunkedArrayToPandas(options, flat_keys, nullptr,
+ owned_numpy_keys.ref()));
+ OwnedRefNoGIL owned_numpy_items;
+ RETURN_NOT_OK(ConvertChunkedArrayToPandas(options, flat_items, nullptr,
+ owned_numpy_items.ref()));
+ PyArrayObject* py_keys =
reinterpret_cast<PyArrayObject*>(owned_numpy_keys.obj());
+ PyArrayObject* py_items =
reinterpret_cast<PyArrayObject*>(owned_numpy_items.obj());
+
+ int64_t chunk_offset = 0;
+ for (int c = 0; c < data.num_chunks(); ++c) {
+ const auto& arr = checked_cast<const MapArray&>(*data.chunk(c));
+ const bool has_nulls = data.null_count() > 0;
+
+ // Make a list of key/item pairs for each row in array
+ for (int64_t i = 0; i < arr.length(); ++i) {
+ if (has_nulls && arr.IsNull(i)) {
+ Py_INCREF(Py_None);
+ *out_values = Py_None;
+ } else {
+ int64_t entry_offset = arr.value_offset(i);
+ int64_t num_maps = arr.value_offset(i + 1) - entry_offset;
+
+ // Build the new list object for the row of maps
+ list_item.reset(PyList_New(num_maps));
+ RETURN_IF_PYERROR();
+
+ // Add each key/item pair in the row
+ for (int64_t j = 0; j < num_maps; ++j) {
+ // Get key value, key is non-nullable for a valid row
+ auto ptr_key = reinterpret_cast<const char*>(
+ PyArray_GETPTR1(py_keys, chunk_offset + entry_offset + j));
+ key_value.reset(PyArray_GETITEM(py_keys, ptr_key));
+ RETURN_IF_PYERROR();
+
+ if (item_arrays[c]->IsNull(entry_offset + j)) {
+ // Translate the Null to a None
+ Py_INCREF(Py_None);
+ item_value.reset(Py_None);
+ } else {
+ // Get valid value from item array
+ auto ptr_item = reinterpret_cast<const char*>(
+ PyArray_GETPTR1(py_items, chunk_offset + entry_offset + j));
+ item_value.reset(PyArray_GETITEM(py_items, ptr_item));
+ RETURN_IF_PYERROR();
+ }
+
+ // Add the key/item pair to the list for the row
+ PyList_SET_ITEM(list_item.obj(), j,
+ PyTuple_Pack(2, key_value.obj(), item_value.obj()));
+ RETURN_IF_PYERROR();
+ }
+
+ *out_values = list_item.obj();
Review comment:
`*out_values = list_item.detach()`, simply.
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