perf: Faster plot_histograms and more reliable plots (#659)

## Fixed problem that histogram get plottet a lot faster 

### Summary of Changes

- rewrote the entire funtion
- reduce numerical bins to a given parameter for more performance
(default 10)
- Bins are now also sorted
- sometimes specific columns had strange plots, these are also now fixed
- swaped the images for the test with new ones

---------

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

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

@@ -2177,13 +2177,18 @@ def plot_boxplots(self) -> Image:
         buffer.seek(0)
         return Image.from_bytes(buffer.read())
 
-    def plot_histograms(self) -> Image:
+    def plot_histograms(self, *, number_of_bins : int = 10) -> Image:
         """
         Plot a histogram for every column.
 
+        Parameters
+        ----------
+        number_of_bins:
+            The number of bins to use in the histogram. Default is 10.
+
         Returns
         -------
-        plot: Image
+        plot:
             The plot as an image.
 
         Examples
@@ -2193,26 +2198,52 @@ def plot_histograms(self) -> Image:
         >>> image = table.plot_histograms()
         """
         import matplotlib.pyplot as plt
+        import numpy as np
         import pandas as pd
-        import seaborn as sns
 
-        col_wrap = min(self.number_of_columns, 3)
+        n_cols = min(3, self.number_of_columns)
+        n_rows = 1 + (self.number_of_columns - 1) // n_cols
 
-        data = pd.melt(self._data.map(lambda value: str(value)), value_vars=self.column_names)
-        grid = sns.FacetGrid(data=data, col="variable", col_wrap=col_wrap, sharex=False, sharey=False)
-        grid.map(sns.histplot, "value")
-        grid.set_xlabels("")
-        grid.set_ylabels("")
-        grid.set_titles("{col_name}")
-        for axes in grid.axes.flat:
-            axes.set_xticks(axes.get_xticks())
-            axes.set_xticklabels(axes.get_xticklabels(), rotation=45, horizontalalignment="right")
-        grid.tight_layout()
-        fig = grid.fig
+        one_col = n_cols == 1 and n_rows == 1
+        fig, axs = plt.subplots(n_rows, n_cols, tight_layout=True, figsize=(n_cols * 3, n_rows * 3))
+
+        col_names = self.column_names
+        for col_name, ax in zip(col_names, axs.flatten() if not one_col else [axs]):
+            np_col = np.array(self.get_column(col_name))
+            bins = min(number_of_bins, len(pd.unique(np_col)))
+
+            ax.set_title(col_name)
+            ax.set_xlabel("")
+            ax.set_ylabel("")
+
+            if self.get_column(col_name).type.is_numeric():
+                np_col = np_col[~np.isnan(np_col)]
+
+                if bins < len(pd.unique(np_col)):
+                    min_val = np.min(np_col)
+                    max_val = np.max(np_col)
+                    hist, bin_edges = np.histogram(self.get_column(col_name), bins, range=(min_val, max_val))
+
+                    bars = np.array([])
+                    for i in range(len(hist)):
+                        bars = np.append(bars, f'{round(bin_edges[i], 2)}-{round(bin_edges[i+1], 2)}')
+
+                    ax.bar(bars, hist, edgecolor='black')
+                    ax.set_xticks(np.arange(len(hist)), bars, rotation=45, horizontalalignment="right")
+                    continue
+
+            np_col = np_col.astype(str)
+            unique_values = np.unique(np_col)
+            hist = np.array([np.sum(np_col == value) for value in unique_values])
+            ax.bar(unique_values, hist, edgecolor='black')
+            ax.set_xticks(np.arange(len(unique_values)), unique_values, rotation=45, horizontalalignment="right")
+
+        for i in range(len(col_names), n_rows * n_cols):
+            fig.delaxes(axs.flatten()[i])  # Remove empty subplots
 
         buffer = io.BytesIO()
         fig.savefig(buffer, format="png")
-        plt.close()
+        plt.close()  # Prevents the figure from being displayed directly
         buffer.seek(0)
         return Image.from_bytes(buffer.read())
 

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

@@ -15,8 +15,14 @@
                 "D": [1.0, 2.1, 2.1, 2.1, 2.1, 3.0, 3.0],
             },
         ),
+        Table(
+            {
+                "A": [3.8, 1.8, 3.2, 2.2, 1.0, 2.4, 3.5, 3.9, 1.9, 4.0, 1.4, 4.2, 4.5, 4.5, 1.4, 2.5, 2.8, 2.8, 1.9, 4.3],
+                "B": ["a", "b", "b", "c", "d", "f", "a", "f", "e", "a", "b", "b", "k", "j", "b", "i", "h", "g", "g", "a"],
+            }
+        ),
     ],
-    ids=["one column", "four columns"],
+    ids=["one column", "four columns", "two columns with compressed visualization"],
 )
 def test_should_match_snapshot(table: Table, snapshot_png_image: SnapshotAssertion) -> None:
     histograms = table.plot_histograms()