feat: implement violin plots (#900)

Closes #867 

### Summary of Changes

This pull request applies the changes, which implement violin plots for
tables and columns, while also adding corresponding tests as well as
adding a section in data_visualization.ipynb.

---------

Co-authored-by: megalinter-bot <[email protected]>
Co-authored-by: Lars Reimann <[email protected]>

Author: 3 people

docs/tutorials/data_visualization.ipynb

@@ -56,7 +56,6 @@ def box_plot(self, *, theme: Literal["dark", "light"] = "light") -> Image:
         """
         if self._column.row_count > 0:
             _check_column_is_numeric(self._column, operation="create a box plot")
-
         import matplotlib.pyplot as plt
 
         def _set_boxplot_colors(box: dict, theme: str) -> None:
@@ -127,6 +126,73 @@ def _set_boxplot_colors(box: dict, theme: str) -> None:
 
             return _figure_to_image(fig)
 
+    def violin_plot(self, *, theme: Literal["dark", "light"] = "light") -> Image:
+        """
+        Create a violin plot for the values in the column. This is only possible for numeric columns.
+
+        Parameters
+        ----------
+        theme:
+            The color theme of the plot. Default is "light".
+
+        Returns
+        -------
+        plot:
+            The violin plot as an image.
+
+        Raises
+        ------
+        TypeError
+            If the column is not numeric.
+
+        Examples
+        --------
+        >>> from safeds.data.tabular.containers import Column
+        >>> column = Column("test", [1, 2, 3])
+        >>> violinplot = column.plot.violin_plot()
+        """
+        if self._column.row_count > 0:
+            _check_column_is_numeric(self._column, operation="create a violin plot")
+        from math import nan
+
+        import matplotlib.pyplot as plt
+
+        style = "dark_background" if theme == "dark" else "default"
+        with plt.style.context(style):
+            if theme == "dark":
+                plt.rcParams.update(
+                    {
+                        "text.color": "white",
+                        "axes.labelcolor": "white",
+                        "axes.edgecolor": "white",
+                        "xtick.color": "white",
+                        "ytick.color": "white",
+                        "grid.color": "gray",
+                        "grid.linewidth": 0.5,
+                    },
+                )
+            else:
+                plt.rcParams.update(
+                    {
+                        "grid.linewidth": 0.5,
+                    },
+                )
+
+            fig, ax = plt.subplots()
+            data = self._column._series.drop_nulls()
+            if len(data) == 0:
+                data = [nan, nan]
+            ax.violinplot(
+                data,
+            )
+
+            ax.set(title=self._column.name)
+
+            ax.yaxis.grid(visible=True)
+            fig.tight_layout()
+
+            return _figure_to_image(fig)
+
     def histogram(self, *, max_bin_count: int = 10, theme: Literal["dark", "light"] = "light") -> Image:
         """
         Create a histogram for the values in the column.

src/safeds/data/tabular/plotting/_column_plotter.py

@@ -119,20 +119,102 @@ def box_plots(self, *, theme: Literal["dark", "light"] = "light") -> Image:
                     fig.delaxes(axs[number_of_rows - 1, i])
 
             fig.tight_layout()
+            return _figure_to_image(fig)
+
+    def violin_plots(self, *, theme: Literal["dark", "light"] = "light") -> Image:
+        """
+        Create a violin plot for every numerical column.
+
+        Parameters
+        ----------
+        theme:
+            The color theme of the plot. Default is "light".
+
+        Returns
+        -------
+        plot:
+            The violin plot(s) as an image.
+
+        Raises
+        ------
+        NonNumericColumnError
+            If the table contains only non-numerical columns.
+
+        Examples
+        --------
+        >>> from safeds.data.tabular.containers import Table
+        >>> table = Table({"a": [1, 2], "b": [3, 42]})
+        >>> image = table.plot.violin_plots()
+        """
+        numerical_table = self._table.remove_non_numeric_columns()
+        if numerical_table.column_count == 0:
+            raise NonNumericColumnError("This table contains only non-numerical columns.")
+        from math import ceil
+
+        import matplotlib.pyplot as plt
+
+        style = "dark_background" if theme == "dark" else "default"
+        with plt.style.context(style):
+            if theme == "dark":
+                plt.rcParams.update(
+                    {
+                        "text.color": "white",
+                        "axes.labelcolor": "white",
+                        "axes.edgecolor": "white",
+                        "xtick.color": "white",
+                        "ytick.color": "white",
+                        "grid.color": "gray",
+                        "grid.linewidth": 0.5,
+                    },
+                )
+            else:
+                plt.rcParams.update(
+                    {
+                        "grid.linewidth": 0.5,
+                    },
+                )
+
+            columns = numerical_table.to_columns()
+            columns = [column._series.drop_nulls() for column in columns]
+            max_width = 3
+            number_of_columns = len(columns) if len(columns) <= max_width else max_width
+            number_of_rows = ceil(len(columns) / number_of_columns)
+
+            fig, axs = plt.subplots(nrows=number_of_rows, ncols=number_of_columns)
+            line = 0
+            for i, column in enumerate(columns):
+                data = column.to_list()
+
+                if i % number_of_columns == 0 and i != 0:
+                    line += 1
+
+                if number_of_columns == 1:
+                    axs.violinplot(
+                        data,
+                    )
+                    axs.set_title(numerical_table.column_names[i])
+                    break
 
-            style = "dark_background" if theme == "dark" else "default"
-            with plt.style.context(style):
-                if theme == "dark":
-                    plt.rcParams.update(
-                        {
-                            "text.color": "white",
-                            "axes.labelcolor": "white",
-                            "axes.edgecolor": "white",
-                            "xtick.color": "white",
-                            "ytick.color": "white",
-                        },
+                if number_of_rows == 1:
+                    axs[i].violinplot(
+                        data,
+                    )
+                    axs[i].set_title(numerical_table.column_names[i])
+
+                else:
+                    axs[line, i % number_of_columns].violinplot(
+                        data,
                     )
-                return _figure_to_image(fig)
+                    axs[line, i % number_of_columns].set_title(numerical_table.column_names[i])
+
+            # removes unused ax indices, so there wont be empty plots
+            last_filled_ax_index = len(columns) % number_of_columns
+            for i in range(last_filled_ax_index, number_of_columns):
+                if number_of_rows != 1 and last_filled_ax_index != 0:
+                    fig.delaxes(axs[number_of_rows - 1, i])
+
+            fig.tight_layout()
+            return _figure_to_image(fig)
 
     def correlation_heatmap(self, *, theme: Literal["dark", "light"] = "light") -> Image:
         """

src/safeds/data/tabular/plotting/_table_plotter.py

@@ -0,0 +1,46 @@
+import pytest
+from safeds.data.tabular.containers import Column
+from safeds.exceptions import ColumnTypeError
+from syrupy import SnapshotAssertion
+
+
+@pytest.mark.parametrize(
+    "column",
+    [
+        Column("a", []),
+        Column("a", [0]),
+        Column("a", [0, 1]),
+    ],
+    ids=[
+        "empty",
+        "one row",
+        "multiple rows",
+    ],
+)
+def test_should_match_snapshot(column: Column, snapshot_png_image: SnapshotAssertion) -> None:
+    violin_plot = column.plot.violin_plot()
+    assert violin_plot == snapshot_png_image
+
+
+@pytest.mark.parametrize(
+    "column",
+    [
+        Column("a", []),
+        Column("a", [0]),
+        Column("a", [0, 1]),
+    ],
+    ids=[
+        "empty",
+        "one row",
+        "multiple rows",
+    ],
+)
+def test_should_match_dark_snapshot(column: Column, snapshot_png_image: SnapshotAssertion) -> None:
+    violin_plot = column.plot.violin_plot(theme="dark")
+    assert violin_plot == snapshot_png_image
+
+
+def test_should_raise_if_column_contains_non_numerical_values() -> None:
+    column = Column("a", ["A", "B", "C"])
+    with pytest.raises(ColumnTypeError):
+        column.plot.violin_plot()

tests/safeds/data/tabular/containers/_column/__snapshots__/test_plot_violin_plot/test_should_match_dark_snapshot[empty].png

@@ -0,0 +1,49 @@
+import pytest
+from safeds.data.tabular.containers import Table
+from safeds.exceptions import NonNumericColumnError
+from syrupy import SnapshotAssertion
+
+
+@pytest.mark.parametrize(
+    "table",
+    [
+        Table({"A": [1, 2, 3]}),
+        Table({"A": [1, 2, 3], "B": ["A", "A", "Bla"], "C": [True, True, False], "D": [1.0, 2.1, 4.5]}),
+        Table({"A": [1, 2, 3], "B": [1.0, 2.1, 4.5], "C": [1, 2, 3], "D": [1.0, 2.1, 4.5]}),
+    ],
+    ids=["one column", "four columns (some non-numeric)", "four columns (all numeric)"],
+)
+def test_should_match_snapshot(table: Table, snapshot_png_image: SnapshotAssertion) -> None:
+    violinplots = table.plot.violin_plots()
+    assert violinplots == snapshot_png_image
+
+
+@pytest.mark.parametrize(
+    "table",
+    [
+        Table({"A": [1, 2, 3]}),
+        Table({"A": [1, 2, 3], "B": ["A", "A", "Bla"], "C": [True, True, False], "D": [1.0, 2.1, 4.5]}),
+        Table({"A": [1, 2, 3], "B": [1.0, 2.1, 4.5], "C": [1, 2, 3], "D": [1.0, 2.1, 4.5]}),
+    ],
+    ids=["one column", "four columns (some non-numeric)", "four columns (all numeric)"],
+)
+def test_should_match_dark_snapshot(table: Table, snapshot_png_image: SnapshotAssertion) -> None:
+    violinplots = table.plot.violin_plots(theme="dark")
+    assert violinplots == snapshot_png_image
+
+
+def test_should_raise_if_column_contains_non_numerical_values() -> None:
+    table = Table.from_dict({"A": ["1", "2", "3.5"], "B": ["0.2", "4", "77"]})
+    with pytest.raises(
+        NonNumericColumnError,
+        match=(
+            r"Tried to do a numerical operation on one or multiple non-numerical columns: \nThis table contains only"
+            r" non-numerical columns."
+        ),
+    ):
+        table.plot.violin_plots()
+
+
+def test_should_fail_on_empty_table() -> None:
+    with pytest.raises(NonNumericColumnError):
+        Table().plot.violin_plots()