diff --git a/python-package/lightgbm/basic.py b/python-package/lightgbm/basic.py
index 560a9a438..5c3a32a4c 100644
--- a/python-package/lightgbm/basic.py
+++ b/python-package/lightgbm/basic.py
@@ -786,7 +786,7 @@ def _data_from_pandas(
     feature_name: _LGBM_FeatureNameConfiguration,
     categorical_feature: _LGBM_CategoricalFeatureConfiguration,
     pandas_categorical: Optional[List[List]]
-) -> Tuple[np.ndarray, List[str], List[str], List[List]]:
+) -> Tuple[np.ndarray, List[str], Union[List[str], List[int]], List[List]]:
     if len(data.shape) != 2 or data.shape[0] < 1:
         raise ValueError('Input data must be 2 dimensional and non empty.')
 
@@ -800,7 +800,7 @@ def _data_from_pandas(
 
     # determine categorical features
     cat_cols = [col for col, dtype in zip(data.columns, data.dtypes) if isinstance(dtype, pd_CategoricalDtype)]
-    cat_cols_not_ordered = [col for col in cat_cols if not data[col].cat.ordered]
+    cat_cols_not_ordered: List[str] = [col for col in cat_cols if not data[col].cat.ordered]
     if pandas_categorical is None:  # train dataset
         pandas_categorical = [list(data[col].cat.categories) for col in cat_cols]
     else:
@@ -811,10 +811,10 @@ def _data_from_pandas(
                 data[col] = data[col].cat.set_categories(category)
     if len(cat_cols):  # cat_cols is list
         data[cat_cols] = data[cat_cols].apply(lambda x: x.cat.codes).replace({-1: np.nan})
-    if categorical_feature == 'auto':  # use cat cols from DataFrame
+
+    # use cat cols from DataFrame
+    if categorical_feature == 'auto':
         categorical_feature = cat_cols_not_ordered
-    else:  # use cat cols specified by user
-        categorical_feature = list(categorical_feature)  # type: ignore[assignment]
 
     df_dtypes = [dtype.type for dtype in data.dtypes]
     # so that the target dtype considers floats