feat: Table.remove_rows

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

@@ -1126,19 +1126,24 @@ def add_rows(self, rows: list[Row] | Table) -> Table:
 
     def filter_rows(self, query: Callable[[Row], bool]) -> Table:
         """
-        Return a new table with rows filtered by Callable (e.g. lambda function).
+        Return a new table containing only the rows that satisfy the query.
 
         The original table is not modified.
 
         Parameters
         ----------
         query:
-            A Callable that is applied to all rows.
+            A callable that returns True if a row should be included in the new table.
 
         Returns
         -------
         table:
-            A table containing only the rows filtered by the query.
+            A table containing only the rows that satisfy the query.
+
+        See Also
+        --------
+        remove_rows:
+            Remove rows that satifsfy a query.
 
         Examples
         --------
@@ -1337,6 +1342,44 @@ def remove_columns_with_non_numerical_values(self) -> Table:
         """
         return Table.from_columns([column for column in self.to_columns() if column.type.is_numeric()])
 
+    def remove_rows(self, query: Callable[[Row], bool]) -> Table:
+        """
+        Return a new table without the rows that satisfy the query.
+
+        The original table is not modified.
+
+        Parameters
+        ----------
+        query:
+            A callable that returns True if the row should be removed.
+
+        Returns
+        -------
+        table:
+            A table without the rows that satisfy the query.
+
+        See Also
+        --------
+        filter_rows:
+            Create a table containing only the rows that satisfy a query.
+
+        Examples
+        --------
+        >>> from safeds.data.tabular.containers import Table
+        >>> table = Table.from_dict({"a": [1, 3], "b": [2, 4]})
+        >>> table.remove_rows(lambda x: x["a"] < 2)
+           a  b
+        0  3  4
+        """
+        import pandas as pd
+
+        rows: list[Row] = [row for row in self.to_rows() if not query(row)]
+        if len(rows) == 0:
+            result_table = Table._from_pandas_dataframe(pd.DataFrame(), self._schema)
+        else:
+            result_table = self.from_rows(rows)
+        return result_table
+
     def remove_duplicate_rows(self) -> Table:
         """
         Return a new table with every duplicate row removed.

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

@@ -8,10 +8,10 @@
     ("table1", "filter_column", "filter_value", "table2"),
     [
         (
-            Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}),
+            Table(),
             "col1",
             1,
-            Table({"col1": [1, 1], "col2": [1, 4]}),
+            Table._from_pandas_dataframe(pd.DataFrame(), Schema({})),
         ),
         (
             Table({"col1": [3, 2, 4], "col2": [1, 2, 4]}),
@@ -20,22 +20,19 @@
             Table._from_pandas_dataframe(pd.DataFrame(), Schema({"col1": Integer(), "col2": Integer()})),
         ),
         (
-            Table(),
+            Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}),
             "col1",
             1,
-            Table._from_pandas_dataframe(pd.DataFrame(), Schema({})),
+            Table({"col1": [1, 1], "col2": [1, 4]}),
         ),
     ],
-    ids=["filter for col1 = 1", "no finding", "empty table"],
+    ids=[
+        "empty table",
+        "no matches",
+        "matches",
+    ],
 )
 def test_should_filter_rows(table1: Table, filter_column: str, filter_value: ColumnType, table2: Table) -> None:
     table1 = table1.filter_rows(lambda row: row.get_value(filter_column) == filter_value)
     assert table1.schema == table2.schema
     assert table2 == table1
-
-
-# noinspection PyTypeChecker
-def test_should_raise_error_if_column_type_invalid() -> None:
-    table = Table({"col1": [1, 2, 3], "col2": [1, 1, 4]})
-    with pytest.raises(TypeError, match=r"'Series' object is not callable"):
-        table.filter_rows(table.get_column("col1")._data > table.get_column("col2")._data)

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

@@ -0,0 +1,38 @@
+import pandas as pd
+import pytest
+from safeds.data.tabular.containers import Table
+from safeds.data.tabular.typing import ColumnType, Schema
+
+
+@pytest.mark.parametrize(
+    ("table1", "remove_column", "remove_value", "table2"),
+    [
+        (
+            Table(),
+            "col1",
+            1,
+            Table._from_pandas_dataframe(pd.DataFrame(), Schema({})),
+        ),
+        (
+            Table({"col1": [3, 2, 4], "col2": [1, 2, 4]}),
+            "col1",
+            1,
+            Table({"col1": [3, 2, 4], "col2": [1, 2, 4]}),
+        ),
+        (
+            Table({"col1": [1, 2, 1], "col2": [1, 2, 4]}),
+            "col1",
+            1,
+            Table({"col1": [2], "col2": [2]}),
+        ),
+    ],
+    ids=[
+        "empty table",
+        "no match",
+        "matches",
+    ],
+)
+def test_should_remove_rows(table1: Table, remove_column: str, remove_value: ColumnType, table2: Table) -> None:
+    table1 = table1.remove_rows(lambda row: row.get_value(remove_column) == remove_value)
+    assert table1.schema == table2.schema
+    assert table2 == table1