diff --git a/amlb/datautils.py b/amlb/datautils.py
index 7378b7b33..94aab0981 100644
--- a/amlb/datautils.py
+++ b/amlb/datautils.py
@@ -59,18 +59,18 @@ def read_csv(path, nrows=None, header=True, index=False, as_data_frame=True, dty
     return df if as_data_frame else df.values
 
 
-def write_csv(
+def write_csv(  # type: ignore[no-untyped-def]
         data: pd.DataFrame | dict | list | np.ndarray,
         path,
         header: bool = True,
         columns: Iterable[str] | None = None,
         index: bool = False,
         append: bool = False
-) -> None:  # type: ignore  # path required pandas internal types
-    if is_data_frame(data):
+) -> None:
+    if isinstance(data, pd.DataFrame):
         data_frame = data
     else:
-        data_frame = to_data_frame(data, columns=columns)
+        data_frame = to_data_frame(data, column_names=columns)
         header = header and columns is not None
     touch(path)
     data_frame.to_csv(
@@ -139,17 +139,17 @@ def is_data_frame(df: object) -> bool:
     return isinstance(df, pd.DataFrame)
 
 
-def to_data_frame(obj: object, columns: Iterable[str]| None=None):
+def to_data_frame(obj: object, column_names: Iterable[str]| None=None) -> pd.DataFrame:
     if obj is None:
         return pd.DataFrame()
-    elif isinstance(obj, dict):
+    columns = list(column_names) if column_names else None
+    if isinstance(obj, dict):
         orient = cast(Literal['columns', 'index'], 'columns' if columns is None else 'index')
         return pd.DataFrame.from_dict(obj, columns=columns, orient=orient)
-    elif isinstance(obj, (list, np.ndarray)):
+    if isinstance(obj, (list, np.ndarray)):
         return pd.DataFrame.from_records(obj, columns=columns)
-    else:
-        raise ValueError("Object should be a dictionary {col1:values, col2:values, ...} "
-                         "or an array of dictionary-like objects [{col1:val, col2:val}, {col1:val, col2:val}, ...].")
+    raise ValueError("Object should be a dictionary {col1:values, col2:values, ...} "
+                     "or an array of dictionary-like objects [{col1:val, col2:val}, {col1:val, col2:val}, ...].")
 
 
 class Encoder(TransformerMixin):