perf: remove unneeded copy operations

src/safeds/data/tabular/containers/_tagged_table.py

@@ -1,6 +1,5 @@
 from __future__ import annotations
 
-import copy
 from typing import TYPE_CHECKING
 
 from safeds.data.tabular.containers import Column, Row, Table
@@ -193,21 +192,6 @@ def target(self) -> Column:
         """
         return self._target
 
-    # ------------------------------------------------------------------------------------------------------------------
-    # Helpers
-    # ------------------------------------------------------------------------------------------------------------------
-
-    def _copy(self) -> TaggedTable:
-        """
-        Return a copy of this tagged table.
-
-        Returns
-        -------
-        table : TaggedTable
-            The copy of this tagged table.
-        """
-        return copy.deepcopy(self)
-
     # ------------------------------------------------------------------------------------------------------------------
     # Specific methods from TaggedTable class:
     # ------------------------------------------------------------------------------------------------------------------
@@ -268,7 +252,7 @@ def add_columns_as_features(self, columns: list[Column] | Table) -> TaggedTable:
             super().add_columns(columns),
             target_name=self.target.name,
             feature_names=self.features.column_names
-            + [col.name for col in (columns.to_columns() if isinstance(columns, Table) else columns)],
+                          + [col.name for col in (columns.to_columns() if isinstance(columns, Table) else columns)],
         )
 
     # ------------------------------------------------------------------------------------------------------------------
@@ -702,8 +686,8 @@ def replace_column(self, old_column_name: str, new_columns: list[Column]) -> Tag
                     self.features.column_names
                     if old_column_name not in self.features.column_names
                     else self.features.column_names[: self.features.column_names.index(old_column_name)]
-                    + [col.name for col in new_columns]
-                    + self.features.column_names[self.features.column_names.index(old_column_name) + 1 :]
+                         + [col.name for col in new_columns]
+                         + self.features.column_names[self.features.column_names.index(old_column_name) + 1:]
                 ),
             )
 
@@ -763,7 +747,7 @@ def slice_rows(
     def sort_columns(
         self,
         comparator: Callable[[Column, Column], int] = lambda col1, col2: (col1.name > col2.name)
-        - (col1.name < col2.name),
+                                                                         - (col1.name < col2.name),
     ) -> TaggedTable:
         """
         Sort the columns of a `TaggedTable` with the given comparator and return a new `TaggedTable`.

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

@@ -139,7 +139,7 @@ def transform(self, table: Table) -> Table:
             if not table.get_column(column).type.is_numeric():
                 raise NonNumericColumnError(f"{column} is of type {table.get_column(column).type}.")
 
-        data = table._data.copy()
+        data = table._data.reset_index(drop=True)
         data.columns = table.column_names
         data[self._column_names] = self._wrapped_transformer.transform(data[self._column_names])
         return Table._from_pandas_dataframe(data)

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

@@ -210,7 +210,7 @@ def transform(self, table: Table) -> Table:
         if table.number_of_rows == 0:
             raise ValueError("The Imputer cannot transform the table because it contains 0 rows")
 
-        data = table._data.copy()
+        data = table._data.reset_index(drop=True)
         data[self._column_names] = pd.DataFrame(
             self._wrapped_transformer.transform(data[self._column_names]),
             columns=self._column_names,

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

@@ -112,7 +112,7 @@ def transform(self, table: Table) -> Table:
         if table.number_of_rows == 0:
             raise ValueError("The LabelEncoder cannot transform the table because it contains 0 rows")
 
-        data = table._data.copy()
+        data = table._data.reset_index(drop=True)
         data.columns = table.column_names
         data[self._column_names] = self._wrapped_transformer.transform(data[self._column_names])
         return Table._from_pandas_dataframe(data)
@@ -171,7 +171,7 @@ def inverse_transform(self, transformed_table: Table) -> Table:
                 ),
             )
 
-        data = transformed_table._data.copy()
+        data = transformed_table._data.reset_index(drop=True)
         data.columns = transformed_table.column_names
         data[self._column_names] = self._wrapped_transformer.inverse_transform(data[self._column_names])
         return Table._from_pandas_dataframe(data)

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

@@ -121,7 +121,7 @@ def fit(self, table: Table, column_names: list[str] | None) -> OneHotEncoder:
                 stacklevel=2,
             )
 
-        data = table._data.copy()
+        data = table._data.reset_index(drop=True)
         data.columns = table.column_names
 
         result = OneHotEncoder()

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

@@ -151,7 +151,7 @@ def transform(self, table: Table) -> Table:
                 ),
             )
 
-        data = table._data.copy()
+        data = table._data.reset_index(drop=True)
         data.columns = table.column_names
         data[self._column_names] = self._wrapped_transformer.transform(data[self._column_names])
         return Table._from_pandas_dataframe(data)
@@ -213,7 +213,7 @@ def inverse_transform(self, transformed_table: Table) -> Table:
                 ),
             )
 
-        data = transformed_table._data.copy()
+        data = transformed_table._data.reset_index(drop=True)
         data.columns = transformed_table.column_names
         data[self._column_names] = self._wrapped_transformer.inverse_transform(data[self._column_names])
         return Table._from_pandas_dataframe(data)

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

@@ -133,7 +133,7 @@ def transform(self, table: Table) -> Table:
                 ),
             )
 
-        data = table._data.copy()
+        data = table._data.reset_index(drop=True)
         data.columns = table.column_names
         data[self._column_names] = self._wrapped_transformer.transform(data[self._column_names])
         return Table._from_pandas_dataframe(data)
@@ -195,7 +195,7 @@ def inverse_transform(self, transformed_table: Table) -> Table:
                 ),
             )
 
-        data = transformed_table._data.copy()
+        data = transformed_table._data.reset_index(drop=True)
         data.columns = transformed_table.column_names
         data[self._column_names] = self._wrapped_transformer.inverse_transform(data[self._column_names])
         return Table._from_pandas_dataframe(data)

src/safeds/ml/classical/_util_sklearn.py

@@ -160,7 +160,7 @@ def predict(model: Any, dataset: Table, feature_names: list[str] | None, target_
     dataset_df = dataset.keep_only_columns(feature_names)._data
     dataset_df.columns = feature_names
 
-    result_set = dataset._data.copy(deep=True)
+    result_set = dataset._data.reset_index(drop=True)
     result_set.columns = dataset.column_names
 
     try: