diff --git a/evalml/pipelines/components/transformers/encoders/ordinal_encoder.py b/evalml/pipelines/components/transformers/encoders/ordinal_encoder.py
index 3636a302e9..49c86230c2 100644
--- a/evalml/pipelines/components/transformers/encoders/ordinal_encoder.py
+++ b/evalml/pipelines/components/transformers/encoders/ordinal_encoder.py
@@ -217,22 +217,24 @@ def transform(self, X, y=None):
         """
         X = infer_feature_types(X)
 
-        X_copy = X.ww.copy()
-        X = X.ww.drop(columns=self.features_to_encode)
+        if not self.features_to_encode:
+            # If there are no features to encode, X needs no transformation, so return a copy
+            return X.ww.copy()
+
+        X_orig = X.ww.drop(columns=self.features_to_encode)
 
         # Call sklearn's transform on only the ordinal columns
-        if len(self.features_to_encode) > 0:
-            X_ord = pd.DataFrame(
-                self._encoder.transform(X_copy[self.features_to_encode]),
-                index=X_copy.index,
-            )
-            X_ord.columns = self._get_feature_names()
-            X_ord.ww.init(logical_types={c: "Double" for c in X_ord.columns})
-            self._feature_names = X_ord.columns
+        X_t = pd.DataFrame(
+            self._encoder.transform(X[self.features_to_encode]),
+            index=X.index,
+        )
+        X_t.columns = self._get_feature_names()
+        X_t.ww.init(logical_types={c: "Double" for c in X_t.columns})
+        self._feature_names = X_t.columns
 
-            X = ww.utils.concat_columns([X, X_ord])
+        X_t = ww.utils.concat_columns([X_orig, X_t])
 
-        return X
+        return X_t
 
     def _get_feature_names(self):
         """Return feature names for the ordinal features after fitting.