diff --git a/merlin/dag/executors.py b/merlin/dag/executors.py
index 75ca53da..ba2550ee 100644
--- a/merlin/dag/executors.py
+++ b/merlin/dag/executors.py
@@ -389,32 +389,26 @@ def transform(
                 if col_dtype:
                     output_dtypes[col_name] = md.dtype(col_dtype).to_numpy
 
-        def empty_like(df, cols):
-            # Construct an empty DataFrame with the same dtypes as df
-
-            # TODO: constructing meta like this can loose dtype information for
-            # columns that are arbitrarily set to 'float64'. We should propagate
-            # dtype information along with column names in the columngroup graph.
-            # This currently only happens during intermediate 'fit' transforms,
-            # so as long as statoperators don't require dtype information on the
-            # DDF this doesn't matter all that much
+        def make_empty(df, cols):
+            # Construct an empty DataFrame
+
+            # TODO: constructing meta like this loses dtype information on the ddf
+            # and sets it all to 'float64'. We should propagate dtype information along
+            # with column names in the columngroup graph. This currently only
+            # happens during intermediate 'fit' transforms, so as long as statoperators
+            # don't require dtype information on the DDF this doesn't matter all that much
             return df._constructor(
-                {
-                    col: df._constructor_sliced(
-                        [], dtype=df[col].dtype if col in df.columns else "float64"
-                    )
-                    for col in cols
-                }
+                {col: df._constructor_sliced([], dtype="float64") for col in cols}
             )
 
         if isinstance(output_dtypes, dict) and isinstance(ddf._meta, pd.DataFrame):
             dtypes = output_dtypes
-            output_dtypes = empty_like(ddf._meta, columns)
+            output_dtypes = make_empty(ddf._meta, columns)
             for col_name, col_dtype in dtypes.items():
                 output_dtypes[col_name] = output_dtypes[col_name].astype(col_dtype)
 
         elif not output_dtypes:
-            output_dtypes = empty_like(ddf._meta, columns)
+            output_dtypes = make_empty(ddf._meta, columns)
 
         return ensure_optimize_dataframe_graph(
             ddf=ddf.map_partitions(