Filter invalid rows in crosswalking

containers/crosswalking/context/main.py

@@ -9,32 +9,6 @@
 from src.util.ids import create_temp_asctb_id
 
 
-def filter_crosswalk_table(
-    table: pd.DataFrame,
-    organ_id: str,
-    organ_level: str,
-    organ_id_column: str,
-    organ_level_column: str,
-    table_label_column: str,
-) -> pd.DataFrame:
-    """Filter the crosswalk table to only include rows with organ id and level.
-
-    Also removes empty rows.
-
-    Args:
-        table (pd.DataFrame): Original full crosswalk table
-
-    Returns:
-        pd.DataFrame: Filtered table
-    """
-    organ_id_rows = table[organ_id_column].str.lower() == organ_id.lower()
-    organ_level_rows = table[organ_level_column].str.lower() == organ_level.lower()
-    filtered_table = table[organ_id_rows & organ_level_rows]
-    normalized_table = filtered_table.dropna(how="all")
-    unique_table = normalized_table.drop_duplicates(table_label_column)
-    return unique_table
-
-
 def crosswalk(
     matrix: anndata.AnnData,
     organ_id: str,
@@ -74,7 +48,8 @@ def crosswalk(
         table_clid_column: data_clid_column,
         table_match_column: data_match_column,
     }
-    table = filter_crosswalk_table(
+    matrix = _filter_invalid_rows(matrix, data_label_column)
+    table = _filter_crosswalk_table(
         table,
         organ_id,
         organ_level,
@@ -103,6 +78,47 @@ def crosswalk(
     return result
 
 
+def _filter_invalid_rows(matrix: anndata.AnnData, column: str) -> anndata.AnnData:
+    """Filter out rows where the obs column's value is NaN or the empty string ''.
+
+    Args:
+        matrix (anndata.AnnData): Matrix to filter
+        column (str): Column in obs
+
+    Returns:
+        anndata.AnnData: Filtered subset matrix
+    """
+    obs_subset = matrix.obs[column]
+    mask = obs_subset.notna() & (obs_subset != "")
+    return matrix[mask, :]
+
+
+def _filter_crosswalk_table(
+    table: pd.DataFrame,
+    organ_id: str,
+    organ_level: str,
+    organ_id_column: str,
+    organ_level_column: str,
+    table_label_column: str,
+) -> pd.DataFrame:
+    """Filter the crosswalk table to only include rows with organ id and level.
+
+    Also removes empty rows.
+
+    Args:
+        table (pd.DataFrame): Original full crosswalk table
+
+    Returns:
+        pd.DataFrame: Filtered table
+    """
+    organ_id_rows = table[organ_id_column].str.lower() == organ_id.lower()
+    organ_level_rows = table[organ_level_column].str.lower() == organ_level.lower()
+    filtered_table = table[organ_id_rows & organ_level_rows]
+    normalized_table = filtered_table.dropna(how="all")
+    unique_table = normalized_table.drop_duplicates(table_label_column)
+    return unique_table
+
+
 def _set_defaults(
     obs: pd.DataFrame, column: str, defaults: t.Union[pd.Series, str]
 ) -> None: