diff --git a/optimus/dataframe/columns.py b/optimus/dataframe/columns.py
index effcf35e..bd4bcd21 100644
--- a/optimus/dataframe/columns.py
+++ b/optimus/dataframe/columns.py
@@ -2163,16 +2163,8 @@ def string_to_index(input_cols=None, output_cols=None, columns=None):
         :param columns:
         :return:
         """
-        df = self
 
-        if columns is None:
-            input_cols = parse_columns(df, input_cols)
-        else:
-            input_cols, output_cols = zip(*columns)
-
-        df = ml_string_to_index(df, input_cols, output_cols)
-
-        return df
+        return ml_string_to_index(df, input_cols, output_cols, columns)
 
     @add_attr(cols)
     def bucketizer(input_cols, splits, output_cols=None):
diff --git a/optimus/ml/encoding.py b/optimus/ml/encoding.py
index 7c24fb09..92b4249b 100644
--- a/optimus/ml/encoding.py
+++ b/optimus/ml/encoding.py
@@ -1,9 +1,9 @@
 from pyspark.ml import feature, Pipeline
 from pyspark.ml.feature import StringIndexer, IndexToString, OneHotEncoder, VectorAssembler, Normalizer
 
-from optimus.helpers.constants import Actions
 from optimus.helpers.check import is_dataframe, is_, is_str
-from optimus.helpers.columns import parse_columns, name_col
+from optimus.helpers.columns import parse_columns, name_col, get_output_cols
+from optimus.helpers.constants import Actions
 from optimus.helpers.raiseit import RaiseIt
 
 
@@ -29,7 +29,7 @@ def n_gram(df, input_col, n=2):
     return df_model, tfidf_model
 
 
-def string_to_index(df, input_cols, output_cols=None, **kargs):
+def string_to_index(df, input_cols, output_cols=None, columns=None, **kargs):
     """
     Maps a string column of labels to an ML column of label indices. If the input column is
     numeric, we cast it to string and index the string values.
@@ -38,17 +38,23 @@ def string_to_index(df, input_cols, output_cols=None, **kargs):
     :param output_cols:Column where the ouput is going to be saved
     :return: Dataframe with indexed columns.
     """
-    df_old = df
-    input_cols = parse_columns(df, input_cols)
-    if output_cols is None:
-        output_cols = [name_col(input_col, "index_to_string") for input_col in input_cols]
+    df_actual = df
+
+    if columns is None:
+        input_cols = parse_columns(df, input_cols)
+        if output_cols is None:
+            output_cols = [name_col(input_col, "index_to_string") for input_col in input_cols]
+        output_cols = get_output_cols(input_cols, output_cols)
+    else:
+        input_cols, output_cols = zip(*columns)
 
     indexers = [StringIndexer(inputCol=input_col, outputCol=output_col, **kargs).fit(df) for input_col, output_col
                 in zip(list(set(input_cols)), list(set(output_cols)))]
 
     pipeline = Pipeline(stages=indexers)
     df = pipeline.fit(df).transform(df)
-    df = df.preserve_meta(df_old, Actions.STRING_TO_INDEX.value, output_cols)
+
+    df = df.preserve_meta(df_actual, Actions.STRING_TO_INDEX.value, output_cols)
 
     return df