ENH: DataFrame.plot.scatter argument c now accepts a column of strings, where rows with the same string are colored identically

doc/source/whatsnew/v3.0.0.rst

@@ -53,6 +53,7 @@ Other enhancements
 - :meth:`DataFrame.pivot_table` and :func:`pivot_table` now allow the passing of keyword arguments to ``aggfunc`` through ``**kwargs`` (:issue:`57884`)
 - :meth:`Series.cummin` and :meth:`Series.cummax` now supports :class:`CategoricalDtype` (:issue:`52335`)
 - :meth:`Series.plot` now correctly handle the ``ylabel`` parameter for pie charts, allowing for explicit control over the y-axis label (:issue:`58239`)
+- :meth:`DataFrame.plot.scatter` argument ``c`` now accepts a column of strings, where rows with the same string are colored identically (:issue:`16827` and :issue:`16485`)
 - :meth:`pandas.concat` will raise a ``ValueError`` when ``ignore_index=True`` and ``keys`` is not ``None`` (:issue:`59274`)
 - Multiplying two :class:`DateOffset` objects will now raise a ``TypeError`` instead of a ``RecursionError`` (:issue:`59442`)
 - Restore support for reading Stata 104-format and enable reading 103-format dta files (:issue:`58554`)

pandas/plotting/_matplotlib/core.py

@@ -1343,6 +1343,22 @@ def _make_plot(self, fig: Figure) -> None:
             label = self.label
         else:
             label = None
+
+        # if a list of non color strings is passed in as c, color points
+        # 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 = self._get_color_mapping(c_values)
+            c_values = [color_mapping[s] for s in c_values]
+
+            # build legend for labeling custom colors
+            ax.legend(
+                handles=[
+                    mpl.patches.Circle((0, 0), facecolor=c, label=s)
+                    for s, c in color_mapping.items()
+                ]
+            )
+
         scatter = ax.scatter(
             data[x].values,
             data[y].values,
@@ -1353,6 +1369,7 @@ def _make_plot(self, fig: Figure) -> None:
             s=self.s,
             **self.kwds,
         )
+
         if cb:
             cbar_label = c if c_is_column else ""
             cbar = self._plot_colorbar(ax, fig=fig, label=cbar_label)
@@ -1392,6 +1409,30 @@ 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: Series) -> bool:
+        # 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(...)
+        unique = np.unique(c_values)
+        try:
+            if len(c_values) and all(isinstance(c, str) for c in unique):
+                mpl.colors.to_rgba_array(unique)
+
+            return True
+
+        except (TypeError, ValueError) as _:
+            return False
+
+    def _get_color_mapping(self, c_values: Series) -> dict[str, np.ndarray]:
+        unique = np.unique(c_values)
+        n_colors = len(unique)
+
+        # passing `None` here will default to :rc:`image.cmap`
+        cmap = mpl.colormaps.get_cmap(self.colormap)
+        colors = cmap(np.linspace(0, 1, n_colors))  # RGB tuples
+
+        return dict(zip(unique, colors))
+
     def _get_norm_and_cmap(self, c_values, color_by_categorical: bool):
         c = self.c
         if self.colormap is not None:

pandas/tests/plotting/frame/test_frame_color.py

@@ -217,8 +217,53 @@ def test_scatter_with_c_column_name_with_colors(self, cmap):
                 ax = df.plot.scatter(x=0, y=1, cmap=cmap, c="species")
         else:
             ax = df.plot.scatter(x=0, y=1, c="species", cmap=cmap)
+
+        assert len(np.unique(ax.collections[0].get_facecolor(), axis=0)) == 3  # r/g/b
+        assert (
+            np.unique(ax.collections[0].get_facecolor(), axis=0)
+            == np.array(
+                [[0.0, 0.0, 1.0, 1.0], [0.0, 0.5, 0.0, 1.0], [1.0, 0.0, 0.0, 1.0]]
+            )  # r/g/b
+        ).all()
         assert ax.collections[0].colorbar is None
 
+    def test_scatter_with_c_column_name_without_colors(self):
+        # Given
+        colors = ["NY", "MD", "MA", "CA"]
+        color_count = 4  # 4 unique colors
+
+        # When
+        df = DataFrame(
+            {
+                "dataX": range(100),
+                "dataY": range(100),
+                "color": (colors[i % len(colors)] for i in range(100)),
+            }
+        )
+
+        # Then
+        ax = df.plot.scatter("dataX", "dataY", c="color")
+        assert len(np.unique(ax.collections[0].get_facecolor(), axis=0)) == color_count
+
+        # Given
+        colors = ["r", "g", "not-a-color"]
+        color_count = 3
+        # Also, since not all are mpl-colors, points matching 'r' or 'g'
+        # are not necessarily red or green
+
+        # When
+        df = DataFrame(
+            {
+                "dataX": range(100),
+                "dataY": range(100),
+                "color": (colors[i % len(colors)] for i in range(100)),
+            }
+        )
+
+        # Then
+        ax = df.plot.scatter("dataX", "dataY", c="color")
+        assert len(np.unique(ax.collections[0].get_facecolor(), axis=0)) == color_count
+
     def test_scatter_colors(self):
         df = DataFrame({"a": [1, 2, 3], "b": [1, 2, 3], "c": [1, 2, 3]})
         with pytest.raises(TypeError, match="Specify exactly one of `c` and `color`"):
@@ -229,7 +274,14 @@ def test_scatter_colors_not_raising_warnings(self):
         # provided via 'c'. Parameters 'cmap' will be ignored
         df = DataFrame({"x": [1, 2, 3], "y": [1, 2, 3]})
         with tm.assert_produces_warning(None):
-            df.plot.scatter(x="x", y="y", c="b")
+            ax = df.plot.scatter(x="x", y="y", c="b")
+            assert (
+                len(np.unique(ax.collections[0].get_facecolor(), axis=0)) == 1
+            )  # blue
+            assert (
+                np.unique(ax.collections[0].get_facecolor(), axis=0)
+                == np.array([[0.0, 0.0, 1.0, 1.0]])
+            ).all()  # blue
 
     def test_scatter_colors_default(self):
         df = DataFrame({"a": [1, 2, 3], "b": [1, 2, 3], "c": [1, 2, 3]})