feat: polars implementation of a row

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

@@ -1,7 +1,110 @@
 from __future__ import annotations
 
-from abc import ABC
+from abc import ABC, abstractmethod
+from collections.abc import Iterator, Mapping
+from typing import TYPE_CHECKING, Any
 
+if TYPE_CHECKING:
+    from safeds.data.tabular.typing import ColumnType, Schema
 
-class ExperimentalPolarsRow(ABC):  # noqa: B024
-    pass
+    from ._experimental_polars_cell import ExperimentalPolarsCell
+
+
+class ExperimentalPolarsRow(ABC, Mapping[str, Any]):
+    """A row is a one-dimensional collection of named, heterogeneous values."""
+
+    # ------------------------------------------------------------------------------------------------------------------
+    # Dunder methods
+    # ------------------------------------------------------------------------------------------------------------------
+
+    def __contains__(self, name: Any) -> bool:
+        return self.has_column(name)
+
+    @abstractmethod
+    def __eq__(self, other: object) -> bool: ...
+
+    def __getitem__(self, name: str) -> ExperimentalPolarsCell:
+        return self.get_value(name)
+
+    @abstractmethod
+    def __hash__(self) -> int: ...
+
+    def __iter__(self) -> Iterator[Any]:
+        return iter(self.column_names)
+
+    def __len__(self) -> int:
+        return self.number_of_columns
+
+    @abstractmethod
+    def __sizeof__(self) -> int: ...
+
+    # ------------------------------------------------------------------------------------------------------------------
+    # Properties
+    # ------------------------------------------------------------------------------------------------------------------
+
+    @property
+    @abstractmethod
+    def column_names(self) -> list[str]:
+        """The names of the columns in the row."""
+
+    @property
+    @abstractmethod
+    def number_of_columns(self) -> int:
+        """The number of columns in the row."""
+
+    @property
+    @abstractmethod
+    def schema(self) -> Schema:  # TODO: rethink return type
+        """The schema of the row."""
+
+    # ------------------------------------------------------------------------------------------------------------------
+    # Column operations
+    # ------------------------------------------------------------------------------------------------------------------
+
+    @abstractmethod
+    def get_value(self, name: str) -> ExperimentalPolarsCell:
+        """
+        Get the value of the specified column.
+
+        Parameters
+        ----------
+        name:
+            The name of the column.
+
+        Returns
+        -------
+        value:
+            The value of the column.
+        """
+
+    @abstractmethod
+    def get_column_type(self, name: str) -> ColumnType:  # TODO: rethink return type
+        """
+        Get the type of the specified column.
+
+        Parameters
+        ----------
+        name:
+            The name of the column.
+
+        Returns
+        -------
+        type:
+            The type of the column.
+        """
+
+    @abstractmethod
+    def has_column(self, name: str) -> bool:
+        """
+        Check if the row has a column with the specified name.
+
+        Parameters
+        ----------
+        name:
+            The name of the column.
+
+        Returns
+        -------
+        has_column:
+            Whether the row has a column with the specified name.
+        """

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

@@ -3,9 +3,10 @@
 from typing import TYPE_CHECKING, Any, Literal
 
 from safeds._utils import _check_and_normalize_file_path
-from safeds.data.tabular.containers import Table
 from safeds.exceptions import ColumnLengthMismatchError
 
+from ._table import Table
+
 if TYPE_CHECKING:
     from collections.abc import Callable, Mapping, Sequence
     from pathlib import Path
@@ -28,12 +29,13 @@ class ExperimentalPolarsTable:
 
     To create a `Table` call the constructor or use one of the following static methods:
 
-    | Method                                                                                                             | Description                            |
-    | ------------------------------------------------------------------------------------------------------------------ | -------------------------------------- |
-    | [from_csv_file][safeds.data.tabular.containers._experimental_polars_table.ExperimentalPolarsTable.from_csv_file]   | Create a table from a CSV file.        |
-    | [from_json_file][safeds.data.tabular.containers._experimental_polars_table.ExperimentalPolarsTable.from_json_file] | Create a table from a JSON file.       |
-    | [from_dict][safeds.data.tabular.containers._experimental_polars_table.ExperimentalPolarsTable.from_dict]           | Create a table from a dictionary.      |
-    | [from_columns][safeds.data.tabular.containers._experimental_polars_table.ExperimentalPolarsTable.from_columns]     | Create a table from a list of columns. |
+    | Method                                                                                                                   | Description                            |
+    | ------------------------------------------------------------------------------------------------------------------------ | -------------------------------------- |
+    | [from_csv_file][safeds.data.tabular.containers._experimental_polars_table.ExperimentalPolarsTable.from_csv_file]         | Create a table from a CSV file.        |
+    | [from_json_file][safeds.data.tabular.containers._experimental_polars_table.ExperimentalPolarsTable.from_json_file]       | Create a table from a JSON file.       |
+    | [from_parquet_file][safeds.data.tabular.containers._experimental_polars_table.ExperimentalPolarsTable.from_parquet_file] | Create a table from a Parquet file.    |
+    | [from_columns][safeds.data.tabular.containers._experimental_polars_table.ExperimentalPolarsTable.from_columns]           | Create a table from a list of columns. |
+    | [from_dict][safeds.data.tabular.containers._experimental_polars_table.ExperimentalPolarsTable.from_dict]                 | Create a table from a dictionary.      |
 
     Parameters
     ----------
@@ -212,7 +214,7 @@ def __str__(self) -> str:
     @property
     def column_names(self) -> list[str]:
         """
-        Names of the columns in the table.
+        The names of the columns in the table.
 
         Examples
         --------
@@ -354,6 +356,11 @@ def remove_duplicate_rows(self) -> ExperimentalPolarsTable:
         """
         Remove duplicate rows from the table.
 
+        Returns
+        -------
+        filtered_table:
+            The table without duplicate rows.
+
         Examples
         --------
         >>> from safeds.data.tabular.containers import ExperimentalPolarsTable

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

@@ -0,0 +1,11 @@
+from ._experimental_polars_cell import ExperimentalPolarsCell
+from ._experimental_polars_column import ExperimentalPolarsColumn
+
+
+class _VectorizedCell(ExperimentalPolarsCell):
+    # ------------------------------------------------------------------------------------------------------------------
+    # Dunder methods
+    # ------------------------------------------------------------------------------------------------------------------
+
+    def __init__(self, column: ExperimentalPolarsColumn):
+        self._column: ExperimentalPolarsColumn = column

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

@@ -0,0 +1,111 @@
+from __future__ import annotations
+
+from typing import TYPE_CHECKING
+
+from ._experimental_polars_row import ExperimentalPolarsRow
+from ._experimental_vectorized_cell import _VectorizedCell
+
+if TYPE_CHECKING:
+    from safeds.data.tabular.typing import ColumnType, Schema
+
+    from ._experimental_polars_table import ExperimentalPolarsTable
+
+
+class _VectorizedRow(ExperimentalPolarsRow):
+    """
+    A row is a one-dimensional collection of named, heterogeneous values.
+
+    This implementation treats an entire table as a row, where each column is a "cell" in the row. This greatly speeds
+    up operations on the row.
+    """
+
+    # ------------------------------------------------------------------------------------------------------------------
+    # Dunder methods
+    # ------------------------------------------------------------------------------------------------------------------
+
+    def __init__(self, table: ExperimentalPolarsTable):
+        self._table: ExperimentalPolarsTable = table
+
+    def __eq__(self, other: object) -> bool:
+        if not isinstance(other, _VectorizedRow):
+            return NotImplemented
+        if self is other:
+            return True
+        return self._table == other._table
+
+    def __hash__(self) -> int:
+        return self._table.__hash__()
+
+    def __sizeof__(self) -> int:
+        return self._table.__sizeof__()
+
+    # ------------------------------------------------------------------------------------------------------------------
+    # Properties
+    # ------------------------------------------------------------------------------------------------------------------
+
+    @property
+    def column_names(self) -> list[str]:
+        """The names of the columns in the row."""
+        return self._table.column_names
+
+    @property
+    def number_of_columns(self) -> int:
+        """The number of columns in the row."""
+        return self._table.number_of_columns
+
+    @property
+    def schema(self) -> Schema:  # TODO: rethink return type
+        """The schema of the row."""
+        return self._table.schema
+
+    # ------------------------------------------------------------------------------------------------------------------
+    # Column operations
+    # ------------------------------------------------------------------------------------------------------------------
+
+    def get_value(self, name: str) -> _VectorizedCell:
+        """
+        Get the value of the specified column.
+
+        Parameters
+        ----------
+        name:
+            The name of the column.
+
+        Returns
+        -------
+        value:
+            The value of the column.
+        """
+        return _VectorizedCell(self._table.get_column(name))
+
+    def get_column_type(self, name: str) -> ColumnType:  # TODO: rethink return type
+        """
+        Get the type of the specified column.
+
+        Parameters
+        ----------
+        name:
+            The name of the column.
+
+        Returns
+        -------
+        type:
+            The type of the column.
+        """
+        return self._table.get_column_type(name)
+
+    def has_column(self, name: str) -> bool:
+        """
+        Check if the row has a column with the specified name.
+
+        Parameters
+        ----------
+        name:
+            The name of the column.
+
+        Returns
+        -------
+        has_column:
+            Whether the row has a column with the specified name.
+        """
+        return self._table.has_column(name)