Apply suggestions from code review

Co-authored-by: Dmitry Chigarev <zeeron209@gmail.com>
modin-project · Aug 10, 2022 · a9fd84a · a9fd84a
1 parent c9ffbf2
commit a9fd84a
Show file tree

Hide file tree

Showing 3 changed files with 5 additions and 4 deletions.
diff --git a/modin/core/dataframe/base/interchange/dataframe_protocol/dataframe.py b/modin/core/dataframe/base/interchange/dataframe_protocol/dataframe.py
@@ -43,9 +43,8 @@ class ColumnBuffers(TypedDict):  # noqa: GL08
 class CategoricalDescription(TypedDict):  # noqa: GL08
     # whether the ordering of dictionary indices is semantically meaningful
     is_ordered: bool
-    # whether a dictionary-style mapping of categorical values to other objects exists
+    # whether a column-style mapping of categorical values to other objects exists
     is_dictionary: bool
-    # Python-level only (e.g. ``{int: str}``).
     # None if not a column-style categorical.
     categories: Optional["ProtocolColumn"]
 

diff --git a/modin/core/dataframe/pandas/interchange/dataframe_protocol/column.py b/modin/core/dataframe/pandas/interchange/dataframe_protocol/column.py
@@ -196,7 +196,7 @@ def describe_categorical(self) -> CategoricalDescription:
             )
 
         pandas_series = self._col.to_pandas().squeeze(axis=1)
-        cat_frame = Series(pandas_series.cat.categories)._query_compiler._modin_frame
+        cat_frame = self._col.from_pandas(pandas_series.cat.categories)
         return {
             "is_ordered": pandas_series.cat.ordered,
             "is_dictionary": True,

diff --git a/...ecution/native/implementations/omnisci_on_native/interchange/dataframe_protocol/column.py b/...ecution/native/implementations/omnisci_on_native/interchange/dataframe_protocol/column.py
@@ -195,7 +195,9 @@ def describe_categorical(self) -> CategoricalDescription:
             col = col.combine_chunks()
 
         col = col.chunks[0]
-        cat_frame = Series(col.dictionary.tolist())._query_compiler._modin_frame
+        cat_frame = OmnisciOnNativeDataframe.from_arrow(
+            pyarrow.Table.from_pydict({self._col.column_names[0]: col.dictionary})
+        )
         from .dataframe import OmnisciProtocolDataframe
 
         return {