create labels for custom colors

Author: Michael Vincent Mannino

pandas/plotting/_matplotlib/core.py

@@ -10,6 +10,7 @@
     Iterator,
     Sequence,
 )
+from random import shuffle
 from typing import (
     TYPE_CHECKING,
     Any,
@@ -1337,10 +1338,12 @@ def _make_plot(self, fig: Figure) -> None:
         norm, cmap = self._get_norm_and_cmap(c_values, color_by_categorical)
         cb = self._get_colorbar(c_values, c_is_column)
 
-        orig_invalid_colors = not self._are_valid_colors(c_values)
-        if orig_invalid_colors:
-            unique_color_labels, c_values = self._convert_str_to_colors(c_values)
-            cb = False
+        # if a list of non color strings is passed in as c, generate a list
+        # colored by uniqueness of the strings, such same strings get same color
+        create_colors = not self._are_valid_colors(c_values)
+        if create_colors:
+            color_mapping, c_values = self._uniquely_color_strs(c_values)
+            cb = False  # no colorbar; opt for legend
 
         if self.legend:
             label = self.label
@@ -1372,14 +1375,14 @@ def _make_plot(self, fig: Figure) -> None:
                 label,  # type: ignore[arg-type]
             )
 
-        if orig_invalid_colors:
-            for s in unique_color_labels:
-                self._append_legend_handles_labels(
-                    # error: Argument 2 to "_append_legend_handles_labels" of
-                    # "MPLPlot" has incompatible type "Hashable"; expected "str"
-                    scatter,
-                    s,  # type: ignore[arg-type]
-                )
+        # build legend for labeling custom colors
+        if create_colors:
+            ax.legend(
+                handles=[
+                    mpl.patches.Circle((0, 0), facecolor=color, label=string)
+                    for string, color in color_mapping.items()
+                ]
+            )
 
         errors_x = self._get_errorbars(label=x, index=0, yerr=False)
         errors_y = self._get_errorbars(label=y, index=0, xerr=False)
@@ -1404,29 +1407,31 @@ def _get_c_values(self, color, color_by_categorical: bool, c_is_column: bool):
             c_values = c
         return c_values
 
-    def _are_valid_colors(self, c_values):
-        # check if c_values contains strings. no need to check numerics as these
-        # will be validated for us in .Axes.scatter._parse_scatter_color_args(...)
-        if not (
-            np.iterable(c_values) and len(c_values) > 0 and isinstance(c_values[0], str)
-        ):
-            return True
-
+    def _are_valid_colors(self, c_values: np.ndarray | list):
+        # check if c_values contains strings and if these strings are valid mpl colors
+        # no need to check numerics as these (and mpl colors) will be validated for us
+        # in .Axes.scatter._parse_scatter_color_args(...)
         try:
-            # similar to above, if this conversion is successful, remaining validation
-            # will be done in .Axes.scatter._parse_scatter_color_args(...)
-            _ = mpl.colors.to_rgba_array(c_values)
+            if len(c_values) and all(isinstance(c, str) for c in c_values):
+                mpl.colors.to_rgba_array(c_values)
+
             return True
 
         except (TypeError, ValueError) as _:
             return False
 
-    def _convert_str_to_colors(self, c_values):
+    def _uniquely_color_strs(
+        self, c_values: np.ndarray | list
+    ) -> tuple[dict, np.ndarray]:
+        # well, almost uniquely color them (up to 949)
+        possible_colors = list(mpl.colors.XKCD_COLORS.values())  # Hex representations
+        shuffle(possible_colors)  # TODO: find better way of getting colors
+
         unique = np.unique(c_values)
-        colors = np.linspace(0, 1, len(unique))
+        colors = [possible_colors[i % len(possible_colors)] for i in range(len(unique))]
         color_mapping = dict(zip(unique, colors))
 
-        return unique, np.array(list(map(color_mapping.get, c_values)))
+        return color_mapping, np.array(list(map(color_mapping.get, c_values)))
 
     def _get_norm_and_cmap(self, c_values, color_by_categorical: bool):
         c = self.c