wjones127 commented on code in PR #34730:
URL: https://github.com/apache/arrow/pull/34730#discussion_r1167102035
##########
python/pyarrow/tests/test_pandas.py:
##########
@@ -4542,3 +4569,48 @@ def test_does_not_mutate_timedelta_nested():
df = table.to_pandas()
assert df["timedelta_2"][0].to_pytimedelta() == timedelta_2[0]
+
+
+def test_roundtrip_nested_map_table_with_pydicts():
Review Comment:
This is a bit paranoid, but could you also test the `to_pandas` conversion
on a MapArray that has been sliced (or even a chunked one where each chunk has
been sliced)? That often brings out some bugs in these implementations.
##########
python/pyarrow/src/arrow/python/arrow_to_pandas.cc:
##########
@@ -911,6 +877,97 @@ Status ConvertMap(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);
+ 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());
+
+ if (!options.maps_as_pydicts) {
+ // The default behavior to express an Arrow MAP as a list of [(key,
value), ...] pairs
+ OwnedRef list_item;
+ return ConvertMapHelper(
+ [&list_item](int64_t num_pairs) {
+ list_item.reset(PyList_New(num_pairs));
+ return CheckPyError();
+ },
+ [&list_item](int64_t idx, OwnedRef& key_value, OwnedRef& item_value) {
+ PyList_SET_ITEM(list_item.obj(), idx,
+ PyTuple_Pack(2, key_value.obj(), item_value.obj()));
+ return CheckPyError();
+ },
+ [&list_item]{ return list_item.detach(); },
+ data,
+ py_keys,
+ py_items,
+ item_arrays,
+ out_values);
+ } else {
+ // Use a native pydict
+ OwnedRef dict_item;
+ return ConvertMapHelper(
+ [&dict_item]([[maybe_unused]] int64_t) {
+ dict_item.reset(PyDict_New());
+ return CheckPyError();
+ },
+ [&dict_item]([[maybe_unused]] int64_t idx, OwnedRef& key_value,
OwnedRef& item_value) {
+ auto setitem_result =
+ PyDict_SetItem(dict_item.obj(), key_value.obj(),
item_value.obj());
+ RETURN_IF_PYERROR();
Review Comment:
This should handle if `TypeError` is raised in case `key_value.obj()` isn't
hashable? Perhaps we should test that? I think having a key type of a map or
list would trigger that.
##########
python/pyarrow/src/arrow/python/arrow_to_pandas.cc:
##########
@@ -176,6 +176,7 @@ static inline bool ListTypeSupported(const DataType& type) {
case Type::DATE32:
case Type::DATE64:
case Type::STRUCT:
+ case Type::MAP:
Review Comment:
Agreed. Although it's interesting we check the value type for list, but not
the fields of structs? I was thinking you should put `Type::MAP` down with
`Type::LIST`, since it is a subclass of ListType, though since the immediate
child is `Type::STRUCT` right now it won't do anything meaningful.
##########
python/pyarrow/src/arrow/python/python_to_arrow.cc:
##########
@@ -762,7 +762,7 @@ class PyListConverter : public ListConverter<T,
PyConverter, PyConverterTrait> {
RETURN_NOT_OK(AppendSequence(value));
} else if (PySet_Check(value) || (Py_TYPE(value) == &PyDictValues_Type)) {
RETURN_NOT_OK(AppendIterable(value));
- } else if (PyDict_Check(value) && this->options_.type->id() == Type::MAP) {
+ } else if (PyDict_Check(value) && this->type()->id() == Type::MAP) {
Review Comment:
Agreed. `MakeConverter` doesn't seem to expect that options is specific to
the type in question; it's templated over it. This change seems correct to me.
Thanks for catching it. 👍
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