feat: polars implementation of a column

benchmarks/table/row_operations.py

@@ -7,22 +7,10 @@
 REPETITIONS = 10
 
 
-def _run_add_rows() -> None:
-    table.add_rows(table)
-
-
-def _run_get_row() -> None:
-    table.get_row(0)
-
-
 def _run_group_rows() -> None:
     table.group_rows(lambda row: row.get_value("column_0") % 2 == 0)
 
 
-def _run_keep_only_rows() -> None:
-    table.keep_only_rows(lambda row: row.get_value("column_0") % 2 == 0)
-
-
 def _run_remove_duplicate_rows() -> None:
     table.remove_duplicate_rows()
 
@@ -59,28 +47,20 @@ def _run_to_rows() -> None:
     table.to_rows()
 
 
+def _run_transform_column() -> None:
+    table.transform_column("column_0", lambda row: row.get_value("column_0") * 2)
+
+
 if __name__ == "__main__":
     # Create a synthetic Table
     table = create_synthetic_table(1000, 50)
 
     # Run the benchmarks
     timings: dict[str, float] = {
-        "add_rows": timeit(
-            _run_add_rows,
-            number=REPETITIONS,
-        ),
-        "get_row": timeit(
-            _run_get_row,
-            number=REPETITIONS,
-        ),
         "group_rows": timeit(
             _run_group_rows,
             number=REPETITIONS,
         ),
-        "keep_only_rows": timeit(
-            _run_keep_only_rows,
-            number=REPETITIONS,
-        ),
         "remove_duplicate_rows": timeit(
             _run_remove_duplicate_rows,
             number=REPETITIONS,
@@ -117,6 +97,10 @@ def _run_to_rows() -> None:
             _run_to_rows,
             number=REPETITIONS,
         ),
+        "transform_colum": timeit(
+            _run_transform_column,
+            number=REPETITIONS,
+        ),
     }
 
     # Print the timings

benchmarks/table/row_operations_polars.py

@@ -7,22 +7,6 @@
 REPETITIONS = 10
 
 
-# def _run_add_rows() -> None:
-#     table.add_rows(table)
-#
-#
-# def _run_get_row() -> None:
-#     table.get_row(0)
-#
-#
-# def _run_group_rows() -> None:
-#     table.group_rows(lambda row: row.get_value("column_0") % 2 == 0)
-#
-#
-# def _run_keep_only_rows() -> None:
-#     table.keep_only_rows(lambda row: row.get_value("column_0") % 2 == 0)
-
-
 def _run_remove_duplicate_rows() -> None:
     table.remove_duplicate_rows()._lazy_frame.collect()
 
@@ -33,30 +17,36 @@ def _run_remove_rows_with_missing_values() -> None:
 
 # def _run_remove_rows_with_outliers() -> None:
 #     table.remove_rows_with_outliers()
-#
-#
-# def _run_remove_rows() -> None:
-#     table.remove_rows(lambda row: row.get_value("column_0") % 2 == 0)
-#
-#
-# def _run_shuffle_rows() -> None:
-#     table.shuffle_rows()
-#
-#
-# def _run_slice_rows() -> None:
-#     table.slice_rows(end=table.number_of_rows // 2)
-#
-#
-# def _run_sort_rows() -> None:
-#     table.sort_rows(lambda row1, row2: row1.get_value("column_0") - row2.get_value("column_0"))
-#
-#
-# def _run_split_rows() -> None:
-#     table.split_rows(0.5)
-#
-#
-# def _run_to_rows() -> None:
-#     table.to_rows()
+
+
+def _run_remove_rows() -> None:
+    table.remove_rows(lambda row: row.get_value("column_0") % 2 == 0)._lazy_frame.collect()
+
+
+def _run_shuffle_rows() -> None:
+    table.shuffle_rows()._lazy_frame.collect()
+
+
+def _run_slice_rows() -> None:
+    table.slice_rows(length=table.number_of_rows // 2)._lazy_frame.collect()
+
+
+def _run_sort_rows() -> None:
+    table.sort_rows(lambda row: row.get_value("column_0"))._lazy_frame.collect()
+
+
+def _run_sort_rows_by_column() -> None:
+    table.sort_rows_by_column("column_0")._lazy_frame.collect()
+
+
+def _run_split_rows() -> None:
+    table_1, table_2 = table.split_rows(0.5)
+    table_1._lazy_frame.collect()
+    table_2._lazy_frame.collect()
+
+
+def _run_transform_column() -> None:
+    table.transform_column("column_0", lambda value: value * 2)._lazy_frame.collect()
 
 
 if __name__ == "__main__":
@@ -65,22 +55,6 @@ def _run_remove_rows_with_missing_values() -> None:
 
     # Run the benchmarks
     timings: dict[str, float] = {
-        # "add_rows": timeit(
-        #     _run_add_rows,
-        #     number=REPETITIONS,
-        # ),
-        # "get_row": timeit(
-        #     _run_get_row,
-        #     number=REPETITIONS,
-        # ),
-        # "group_rows": timeit(
-        #     _run_group_rows,
-        #     number=REPETITIONS,
-        # ),
-        # "keep_only_rows": timeit(
-        #     _run_keep_only_rows,
-        #     number=REPETITIONS,
-        # ),
         "remove_duplicate_rows": timeit(
             _run_remove_duplicate_rows,
             number=REPETITIONS,
@@ -93,36 +67,40 @@ def _run_remove_rows_with_missing_values() -> None:
         #     _run_remove_rows_with_outliers,
         #     number=REPETITIONS,
         # ),
-        # "remove_rows": timeit(
-        #     _run_remove_rows,
-        #     number=REPETITIONS,
-        # ),
-        # "shuffle_rows": timeit(
-        #     _run_shuffle_rows,
-        #     number=REPETITIONS,
-        # ),
-        # "slice_rows": timeit(
-        #     _run_slice_rows,
-        #     number=REPETITIONS,
-        # ),
-        # "sort_rows": timeit(
-        #     _run_sort_rows,
-        #     number=REPETITIONS,
-        # ),
-        # "split_rows": timeit(
-        #     _run_split_rows,
-        #     number=REPETITIONS,
-        # ),
-        # "to_rows": timeit(
-        #     _run_to_rows,
-        #     number=REPETITIONS,
-        # ),
+        "remove_rows": timeit(
+            _run_remove_rows,
+            number=REPETITIONS,
+        ),
+        "shuffle_rows": timeit(
+            _run_shuffle_rows,
+            number=REPETITIONS,
+        ),
+        "slice_rows": timeit(
+            _run_slice_rows,
+            number=REPETITIONS,
+        ),
+        "sort_rows": timeit(
+            _run_sort_rows,
+            number=REPETITIONS,
+        ),
+        "sort_rows_by_column": timeit(
+            _run_sort_rows_by_column,
+            number=REPETITIONS,
+        ),
+        "split_rows": timeit(
+            _run_split_rows,
+            number=REPETITIONS,
+        ),
+        "transform_column": timeit(
+            _run_transform_column,
+            number=REPETITIONS,
+        ),
     }
 
     # Print the timings
     print(
         Table(
-            {  # noqa: T201
+            {
                 "method": list(timings.keys()),
                 "timing": list(timings.values()),
             }

benchmarks/table/utils/create_synthetic_table_polars.py

@@ -1,6 +1,6 @@
 from random import randrange
 
-from safeds.data.tabular.containers import ExperimentalPolarsTable
+from safeds.data.tabular.containers import ExperimentalTable
 
 
 def create_synthetic_table_polars(
@@ -9,7 +9,7 @@ def create_synthetic_table_polars(
     *,
     min_value: int = 0,
     max_value: int = 1000,
-) -> ExperimentalPolarsTable:
+) -> ExperimentalTable:
     """Create a synthetic Table with random numerical data.
 
     Parameters
@@ -28,7 +28,7 @@ def create_synthetic_table_polars(
     Table
         A Table with random numerical data.
     """
-    return ExperimentalPolarsTable(
+    return ExperimentalTable(
         {
             f"column_{i}": [randrange(min_value, max_value) for _ in range(number_of_rows)]
             for i in range(number_of_columns)

pyproject.toml

@@ -20,7 +20,7 @@ matplotlib = "^3.6.3"
 openpyxl = "^3.1.2"
 pandas = "^2.0.0"
 pillow = ">=9.5,<11.0"
-polars = {extras = ["pyarrow"], version = "^0.20.23"}
+polars = {extras = ["numpy", "pyarrow"], version = "^0.20.24"}
 scikit-learn = "^1.2.0"
 seaborn = "^0.13.0"
 statsmodels = "^0.14.1"

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

@@ -6,32 +6,32 @@
 
 if TYPE_CHECKING:
     from ._column import Column
-    from ._experimental_polars_cell import ExperimentalPolarsCell
-    from ._experimental_polars_column import ExperimentalPolarsColumn
-    from ._experimental_polars_row import ExperimentalPolarsRow
-    from ._experimental_polars_table import ExperimentalPolarsTable
+    from ._experimental_cell import ExperimentalCell
+    from ._experimental_column import ExperimentalColumn
+    from ._experimental_row import ExperimentalRow
+    from ._experimental_table import ExperimentalTable
     from ._row import Row
     from ._table import Table
 
 apipkg.initpkg(
     __name__,
     {
         "Column": "._column:Column",
-        "ExperimentalPolarsCell": "._experimental_polars_cell:ExperimentalPolarsCell",
-        "ExperimentalPolarsColumn": "._experimental_polars_column:ExperimentalPolarsColumn",
-        "ExperimentalPolarsRow": "._experimental_polars_row:ExperimentalPolarsRow",
-        "ExperimentalPolarsTable": "._experimental_polars_table:ExperimentalPolarsTable",
+        "ExperimentalCell": "._experimental_cell:ExperimentalCell",
+        "ExperimentalColumn": "._experimental_column:ExperimentalColumn",
+        "ExperimentalRow": "._experimental_row:ExperimentalRow",
+        "ExperimentalTable": "._experimental_table:ExperimentalTable",
         "Row": "._row:Row",
         "Table": "._table:Table",
     },
 )
 
 __all__ = [
     "Column",
-    "ExperimentalPolarsCell",
-    "ExperimentalPolarsColumn",
-    "ExperimentalPolarsRow",
-    "ExperimentalPolarsTable",
+    "ExperimentalCell",
+    "ExperimentalColumn",
+    "ExperimentalRow",
+    "ExperimentalTable",
     "Row",
     "Table",
 ]