feat: raise if remove_colums is called with unknown column by default

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

@@ -642,20 +642,23 @@ def remove_columns(
         self,
         names: str | list[str],
         /,
+        *,
+        ignore_unknown_names: bool = False,
     ) -> Table:
         """
         Return a new table without the specified columns.
 
         **Notes:**
 
         - The original table is not modified.
-        - This method does not raise if a column does not exist. You can use it to ensure that the resulting table does
-          not contain certain columns.
 
         Parameters
         ----------
         names:
             The names of the columns to remove.
+        ignore_unknown_names:
+            If set to True, columns that are not present in the table will be ignored.
+            If set to False, an error will be raised if any of the specified columns do not exist.
 
         Returns
         -------
@@ -677,7 +680,7 @@ def remove_columns(
         |   6 |
         +-----+
 
-        >>> table.remove_columns(["c"])
+        >>> table.remove_columns(["c"], ignore_unknown_names=True)
         +-----+-----+
         |   a |   b |
         | --- | --- |
@@ -691,6 +694,9 @@ def remove_columns(
         if isinstance(names, str):
             names = [names]
 
+        if not ignore_unknown_names:
+            _check_columns_exist(self, names)
+
         return Table._from_polars_lazy_frame(
             self._lazy_frame.drop(names),
         )
@@ -931,7 +937,7 @@ def replace_column(
         _check_columns_dont_exist(self, [column.name for column in new_columns], old_name=old_name)
 
         if len(new_columns) == 0:
-            return self.remove_columns(old_name)
+            return self.remove_columns(old_name, ignore_unknown_names=True)
 
         if len(new_columns) == 1:
             new_column = new_columns[0]

src/safeds/ml/classical/_supervised_model.py

@@ -411,7 +411,7 @@ def _predict_with_sklearn_model(
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", message="X does not have valid feature names")
             predicted_target_vector = model.predict(features._data_frame)
-        output = dataset.remove_columns(target_name).add_columns(
+        output = dataset.remove_columns(target_name, ignore_unknown_names=True).add_columns(
             Column(target_name, predicted_target_vector),
         )
 

src/safeds/ml/classical/regression/_arima.py

@@ -136,7 +136,7 @@ def predict(self, time_series: TimeSeriesDataset) -> Table:
         # make a table without
         forecast_horizon = len(time_series.target._series.to_numpy())
         result_table = time_series.to_table()
-        result_table = result_table.remove_columns([time_series.target.name])
+        result_table = result_table.remove_columns([time_series.target.name], ignore_unknown_names=True)
         # Validation
         if not self.is_fitted or self._arima is None:
             raise ModelNotFittedError

tests/safeds/data/tabular/containers/_table/test_remove_columns.py

@@ -1,26 +1,67 @@
 import pytest
 from safeds.data.tabular.containers import Table
+from safeds.exceptions import ColumnNotFoundError
 
 
+# Test cases where no exception is expected
 @pytest.mark.parametrize(
-    ("table", "expected", "columns"),
+    ("table", "expected", "columns", "ignore_unknown_names"),
     [
-        (Table({"col1": [1, 2, 1], "col2": ["a", "b", "c"]}), Table({"col1": [1, 2, 1]}), ["col2"]),
-        (Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), Table(), ["col1", "col2"]),
-        (Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), []),
-        (Table(), Table(), []),
-        (Table(), Table(), ["col1"]),
+        (Table({"col1": [1, 2, 1], "col2": ["a", "b", "c"]}), Table({"col1": [1, 2, 1]}), ["col2"], True),
+        (Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), Table(), ["col1", "col2"], True),
+        (Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), [], True),
+        (Table(), Table(), [], True),
+        (Table(), Table(), ["col1"], True),
+        (Table({"col1": [1, 2, 1], "col2": ["a", "b", "c"]}), Table({"col1": [1, 2, 1]}), ["col2"], False),
+        (Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}), Table(), ["col1", "col2"], False),
+        (
+            Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}),
+            Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}),
+            [],
+            False,
+        ),
+        (Table(), Table(), [], False),
     ],
     ids=[
+        "one column, ignore unknown names",
+        "multiple columns, ignore unknown names",
+        "no columns, ignore unknown names",
+        "empty, ignore unknown names",
+        "missing columns, ignore unknown names",
         "one column",
         "multiple columns",
         "no columns",
         "empty",
-        "missing columns",
     ],
 )
-def test_should_remove_table_columns(table: Table, expected: Table, columns: list[str]) -> None:
-    table = table.remove_columns(columns)
+def test_should_remove_table_columns_no_exception(
+    table: Table,
+    expected: Table,
+    columns: list[str],
+    ignore_unknown_names: bool,
+) -> None:
+    table = table.remove_columns(columns, ignore_unknown_names=ignore_unknown_names)
     assert table.schema == expected.schema
     assert table == expected
     assert table.row_count == expected.row_count
+
+
+# Test cases where an exception is expected
+@pytest.mark.parametrize(
+    ("table", "columns", "ignore_unknown_names"),
+    [
+        (Table(), ["col1"], False),
+        (Table(), ["col12"], False),
+    ],
+    ids=[
+        "missing columns",
+        "missing columns",
+    ],
+)
+def test_should_raise_error_for_unknown_columns(
+    table: Table,
+    columns: list[str],
+    ignore_unknown_names: bool,
+) -> None:
+    with pytest.raises(ColumnNotFoundError):
+        table.remove_columns(columns, ignore_unknown_names=ignore_unknown_names)

tests/safeds/ml/classical/classification/test_classifier.py

@@ -192,7 +192,7 @@ def test_should_raise_if_not_fitted(self, classifier: Classifier, valid_data: Ta
     def test_should_raise_if_dataset_misses_features(self, classifier: Classifier, valid_data: TabularDataset) -> None:
         fitted_classifier = classifier.fit(valid_data)
         with pytest.raises(DatasetMissesFeaturesError, match="[feat1, feat2]"):
-            fitted_classifier.predict(valid_data.features.remove_columns(["feat1", "feat2"]))
+            fitted_classifier.predict(valid_data.features.remove_columns(["feat1", "feat2"], ignore_unknown_names=True))
 
     @pytest.mark.parametrize(
         ("invalid_data", "expected_error", "expected_error_msg"),

tests/safeds/ml/classical/regression/test_regressor.py

@@ -190,7 +190,7 @@ def test_should_raise_if_not_fitted(self, regressor: Regressor, valid_data: Tabu
     def test_should_raise_if_dataset_misses_features(self, regressor: Regressor, valid_data: TabularDataset) -> None:
         fitted_regressor = regressor.fit(valid_data)
         with pytest.raises(DatasetMissesFeaturesError, match="[feat1, feat2]"):
-            fitted_regressor.predict(valid_data.features.remove_columns(["feat1", "feat2"]))
+            fitted_regressor.predict(valid_data.features.remove_columns(["feat1", "feat2"], ignore_unknown_names=True))
 
     @pytest.mark.parametrize(
         ("invalid_data", "expected_error", "expected_error_msg"),

tests/safeds/ml/nn/test_forward_workflow.py

@@ -25,7 +25,7 @@ def test_forward_model(device: Device) -> None:
     table_1 = Table.from_csv_file(
         path=resolve_resource_path(_inflation_path),
     )
-    table_1 = table_1.remove_columns(["date"])
+    table_1 = table_1.remove_columns(["date"], ignore_unknown_names=True)
     table_2 = table_1.slice_rows(start=0, length=table_1.row_count - 14)
     table_2 = table_2.add_columns([(table_1.slice_rows(start=14)).get_column("value").rename("target")])
     train_table, test_table = table_2.split_rows(0.8)