[python] Flatten categorical soma_joinid if presented at write

apis/python/src/tiledbsoma/_dataframe.py

@@ -409,18 +409,25 @@ def write(
         for name in values.schema.names:
             col = values.column(name)
             n = len(col)
+
             cols_map = dim_cols_map if name in dim_names_set else attr_cols_map
             if pa.types.is_dictionary(col.type) and col.num_chunks != 0:
-                attr = self._handle.schema.attr(name)
-                if attr.enum_label is not None:
-                    # Normal case: writing categorical data to categorical schema.
-                    cols_map[name] = col.chunk(0).indices.to_pandas()
-                else:
-                    # Schema is non-categorical but the user is writing categorical.
-                    # Simply decategoricalize for them.
+                if name in dim_names_set:
+                    # Dims are never categorical. Decategoricalize for them.
                     cols_map[name] = pa.chunked_array(
                         [chunk.dictionary_decode() for chunk in col.chunks]
                     )
+                else:
+                    attr = self._handle.schema.attr(name)
+                    if attr.enum_label is not None:
+                        # Normal case: writing categorical data to categorical schema.
+                        cols_map[name] = col.chunk(0).indices.to_pandas()
+                    else:
+                        # Schema is non-categorical but the user is writing categorical.
+                        # Simply decategoricalize for them.
+                        cols_map[name] = pa.chunked_array(
+                            [chunk.dictionary_decode() for chunk in col.chunks]
+                        )
             else:
                 cols_map[name] = col.to_pandas()
 

apis/python/tests/test_dataframe.py

@@ -914,6 +914,41 @@ def test_write_categorical_types(tmp_path):
         assert (df == sdf.read().concat().to_pandas()).all().all()
 
 
+def test_write_categorical_dims(tmp_path):
+    """
+    Categories are not supported as dims. Here we test our handling of what we
+    do when we are given them as input.
+    """
+    schema = pa.schema(
+        [
+            ("soma_joinid", pa.int64()),
+            ("string", pa.dictionary(pa.int8(), pa.large_string())),
+        ]
+    )
+    with soma.DataFrame.create(
+        tmp_path.as_posix(),
+        schema=schema,
+        index_column_names=["soma_joinid"],
+        enumerations={
+            "enum-string": ["b", "a"],
+        },
+        ordered_enumerations=[],
+        column_to_enumerations={
+            "string": "enum-string",
+        },
+    ) as sdf:
+        df = pd.DataFrame(
+            data={
+                "soma_joinid": pd.Categorical([0, 1, 2, 3], categories=[0, 1, 2, 3]),
+                "string": pd.Categorical(["a", "b", "a", "b"], categories=["b", "a"]),
+            }
+        )
+        sdf.write(pa.Table.from_pandas(df))
+
+    with soma.DataFrame.open(tmp_path.as_posix()) as sdf:
+        assert (df == sdf.read().concat().to_pandas()).all().all()
+
+
 def test_result_order(tmp_path):
     # cf. https://docs.tiledb.com/main/background/key-concepts-and-data-format#data-layout
     schema = pa.schema(