create labels for custom colors

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