Remove unneccessary copied in OneHotEncoder

src/safeds/data/tabular/transformation/_one_hot_encoder.py

@@ -142,10 +142,6 @@ def transform(self, table: Table) -> Table:
         if len(missing_columns) > 0:
             raise UnknownColumnNameError(list(missing_columns))
 
-        # Make a copy of the table:
-        # TODO: change to copy method once implemented
-        new_table = table.remove_columns([])
-
         encoded_values = {}
         for new_column_name in self._value_to_column.values():
             encoded_values[new_column_name] = [0.0 for _ in range(table.number_of_rows)]
@@ -162,21 +158,25 @@ def transform(self, table: Table) -> Table:
                 encoded_values[new_column_name][i] = 1.0
 
             for new_column in self._column_names[old_column_name]:
-                new_table = new_table.add_column(Column(new_column, encoded_values[new_column]))
-
-        # Drop corresponding old columns:
-        new_table = new_table.remove_columns(list(self._column_names.keys()))
+                table = table.add_column(Column(new_column, encoded_values[new_column]))
 
+        # New columns may not be sorted:
         column_names = []
-
         for name in table.column_names:
             if name not in self._column_names.keys():
                 column_names.append(name)
             else:
                 column_names.extend(
                     [f_name for f_name in self._value_to_column.values() if f_name.startswith(name)],
                 )
-        return new_table.sort_columns(lambda col1, col2: column_names.index(col1.name) - column_names.index(col2.name))
+
+        # Drop old, non-encoded columns:
+        # (Don't do this earlier - we need the old column nams for sorting,
+        # plus we need to prevent the table from possibly having 0 columns temporarily.)
+        table = table.remove_columns(list(self._column_names.keys()))
+
+        # Apply sorting and return:
+        return table.sort_columns(lambda col1, col2: column_names.index(col1.name) - column_names.index(col2.name))
 
     # noinspection PyProtectedMember
     def inverse_transform(self, transformed_table: Table) -> Table:
@@ -204,10 +204,6 @@ def inverse_transform(self, transformed_table: Table) -> Table:
         if self._column_names is None or self._value_to_column is None:
             raise TransformerNotFittedError
 
-        # Make a copy of the table:
-        # TODO: change to copy method once implemented
-        new_table = transformed_table.remove_columns([])
-
         original_columns = {}
         for original_column_name in self._column_names:
             original_columns[original_column_name] = [None for _ in range(transformed_table.number_of_rows)]
@@ -218,11 +214,10 @@ def inverse_transform(self, transformed_table: Table) -> Table:
                 if transformed_table.get_column(constructed_column)[i] == 1.0:
                     original_columns[original_column_name][i] = value
 
-        for column_name, encoded_column in original_columns.items():
-            new_table = new_table.add_column(Column(column_name, encoded_column))
+        table = transformed_table
 
-        # Drop old column names:
-        new_table = new_table.remove_columns(list(self._value_to_column.values()))
+        for column_name, encoded_column in original_columns.items():
+            table = table.add_column(Column(column_name, encoded_column))
 
         column_names = [
             (
@@ -236,9 +231,13 @@ def inverse_transform(self, transformed_table: Table) -> Table:
                     ][0]
                 ]
             )
-            for name in transformed_table.column_names
+            for name in table.column_names
         ]
-        return new_table.sort_columns(lambda col1, col2: column_names.index(col1.name) - column_names.index(col2.name))
+
+        # Drop old column names:
+        table = table.remove_columns(list(self._value_to_column.values()))
+
+        return table.sort_columns(lambda col1, col2: column_names.index(col1.name) - column_names.index(col2.name))
 
     def is_fitted(self) -> bool:
         """