diff --git a/fedot/core/data/data_preprocessing.py b/fedot/core/data/data_preprocessing.py
index b0076e9f0a..49650e71c7 100644
--- a/fedot/core/data/data_preprocessing.py
+++ b/fedot/core/data/data_preprocessing.py
@@ -72,11 +72,15 @@ def find_categorical_columns(table: np.ndarray, column_type_ids: Optional[np.nda
 
 def force_categorical_determination(table: np.ndarray):
     """ Find string columns using 'computationally expensive' approach """
-    real_columns_selector = np.all(np.isreal(table), axis=0)
-    non_categorical_ids = np.flatnonzero(real_columns_selector).tolist()
-    categorical_ids = np.flatnonzero(~real_columns_selector).tolist()
-
-    return categorical_ids, non_categorical_ids
+    categorical_ids = []
+    non_categorical_ids = []
+    # For every column in table make check
+    for column_id, column in enumerate(table.T):
+        # Check if column is of string objects
+        if pd.api.types.infer_dtype(column, skipna=True) == 'string':
+            categorical_ids.append(column_id)
+        else:
+            non_categorical_ids.append(column_id)
 
 
 def data_has_missing_values(data: InputData) -> bool:
diff --git a/fedot/preprocessing/preprocessing.py b/fedot/preprocessing/preprocessing.py
index b45e2ceabc..95985539d0 100644
--- a/fedot/preprocessing/preprocessing.py
+++ b/fedot/preprocessing/preprocessing.py
@@ -314,9 +314,11 @@ def _clean_extra_spaces(data: InputData) -> InputData:
         """
 
         def strip_all_strs(item: Union[object, str]):
-            if isinstance(item, str):
+            try:
                 return item.strip()
-            return item
+            except AttributeError:
+                # not an str object
+                return item
 
         features_df = pd.DataFrame(data.features)
         mixed_or_str = features_df.select_dtypes(object)