ENH: accept a dictionary in plot colors (#31071)

doc/source/whatsnew/v1.1.0.rst

@@ -171,7 +171,7 @@ I/O
 Plotting
 ^^^^^^^^
 
--
+- :func:`.plot` for line/bar now accepts color by dictonary (:issue:`8193`).
 -
 
 Groupby/resample/rolling

pandas/plotting/_core.py

@@ -385,6 +385,45 @@ def hist_frame(
 """
 
 
+_bar_or_line_doc = """
+        Parameters
+        ----------
+        x : label or position, optional
+            Allows plotting of one column versus another. If not specified,
+            the index of the DataFrame is used.
+        y : label or position, optional
+            Allows plotting of one column versus another. If not specified,
+            all numerical columns are used.
+        color : str, array_like, or dict, optional
+            The color for each of the DataFrame's columns. Possible values are:
+
+            - A single color string referred to by name, RGB or RGBA code,
+                for instance 'red' or '#a98d19'.
+
+            - A sequence of color strings referred to by name, RGB or RGBA
+                code, which will be used for each column recursively. For
+                instance ['green','yellow'] each column's %(kind)s will be filled in
+                green or yellow, alternatively.
+
+            - A dict of the form {column name : color}, so that each column will be
+                colored accordingly. For example, if your columns are called `a` and
+                `b`, then passing {'a': 'green', 'b': 'red'} will color %(kind)ss for
+                column `a` in green and %(kind)ss for column `b` in red.
+
+            .. versionadded:: 1.1.0
+
+        **kwargs
+            Additional keyword arguments are documented in
+            :meth:`DataFrame.plot`.
+
+        Returns
+        -------
+        matplotlib.axes.Axes or np.ndarray of them
+            An ndarray is returned with one :class:`matplotlib.axes.Axes`
+            per column when ``subplots=True``.
+"""
+
+
 @Substitution(backend="")
 @Appender(_boxplot_doc)
 def boxplot(
@@ -848,31 +887,8 @@ def __call__(self, *args, **kwargs):
 
     __call__.__doc__ = __doc__
 
-    def line(self, x=None, y=None, **kwargs):
+    @Appender(
         """
-        Plot Series or DataFrame as lines.
-
-        This function is useful to plot lines using DataFrame's values
-        as coordinates.
-
-        Parameters
-        ----------
-        x : int or str, optional
-            Columns to use for the horizontal axis.
-            Either the location or the label of the columns to be used.
-            By default, it will use the DataFrame indices.
-        y : int, str, or list of them, optional
-            The values to be plotted.
-            Either the location or the label of the columns to be used.
-            By default, it will use the remaining DataFrame numeric columns.
-        **kwargs
-            Keyword arguments to pass on to :meth:`DataFrame.plot`.
-
-        Returns
-        -------
-        :class:`matplotlib.axes.Axes` or :class:`numpy.ndarray`
-            Return an ndarray when ``subplots=True``.
-
         See Also
         --------
         matplotlib.pyplot.plot : Plot y versus x as lines and/or markers.
@@ -907,6 +923,16 @@ def line(self, x=None, y=None, **kwargs):
            >>> type(axes)
            <class 'numpy.ndarray'>
 
+        .. plot::
+           :context: close-figs
+
+           Let's repeat the same example, but specifying colors for
+           each column (in this case, for each animal).
+
+           >>> axes = df.plot.line(
+           ...     subplots=True, color={"pig": "pink", "horse": "#742802"}
+           ... )
+
         .. plot::
             :context: close-figs
 
@@ -915,36 +941,20 @@ def line(self, x=None, y=None, **kwargs):
 
             >>> lines = df.plot.line(x='pig', y='horse')
         """
-        return self(kind="line", x=x, y=y, **kwargs)
-
-    def bar(self, x=None, y=None, **kwargs):
+    )
+    @Substitution(kind="line")
+    @Appender(_bar_or_line_doc)
+    def line(self, x=None, y=None, **kwargs):
         """
-        Vertical bar plot.
-
-        A bar plot is a plot that presents categorical data with
-        rectangular bars with lengths proportional to the values that they
-        represent. A bar plot shows comparisons among discrete categories. One
-        axis of the plot shows the specific categories being compared, and the
-        other axis represents a measured value.
-
-        Parameters
-        ----------
-        x : label or position, optional
-            Allows plotting of one column versus another. If not specified,
-            the index of the DataFrame is used.
-        y : label or position, optional
-            Allows plotting of one column versus another. If not specified,
-            all numerical columns are used.
-        **kwargs
-            Additional keyword arguments are documented in
-            :meth:`DataFrame.plot`.
+        Plot Series or DataFrame as lines.
 
-        Returns
-        -------
-        matplotlib.axes.Axes or np.ndarray of them
-            An ndarray is returned with one :class:`matplotlib.axes.Axes`
-            per column when ``subplots=True``.
+        This function is useful to plot lines using DataFrame's values
+        as coordinates.
+        """
+        return self(kind="line", x=x, y=y, **kwargs)
 
+    @Appender(
+        """
         See Also
         --------
         DataFrame.plot.barh : Horizontal bar plot.
@@ -986,6 +996,17 @@ def bar(self, x=None, y=None, **kwargs):
             >>> axes = df.plot.bar(rot=0, subplots=True)
             >>> axes[1].legend(loc=2)  # doctest: +SKIP
 
+        If you don't like the default colours, you can specify how you'd
+        like each column to be colored.
+
+        .. plot::
+            :context: close-figs
+
+            >>> axes = df.plot.bar(
+            ...     rot=0, subplots=True, color={"speed": "red", "lifespan": "green"}
+            ... )
+            >>> axes[1].legend(loc=2)  # doctest: +SKIP
+
         Plot a single column.
 
         .. plot::
@@ -999,32 +1020,24 @@ def bar(self, x=None, y=None, **kwargs):
             :context: close-figs
 
             >>> ax = df.plot.bar(x='lifespan', rot=0)
+    """
+    )
+    @Substitution(kind="bar")
+    @Appender(_bar_or_line_doc)
+    def bar(self, x=None, y=None, **kwargs):
         """
-        return self(kind="bar", x=x, y=y, **kwargs)
-
-    def barh(self, x=None, y=None, **kwargs):
-        """
-        Make a horizontal bar plot.
+        Vertical bar plot.
 
-        A horizontal bar plot is a plot that presents quantitative data with
+        A bar plot is a plot that presents categorical data with
         rectangular bars with lengths proportional to the values that they
         represent. A bar plot shows comparisons among discrete categories. One
         axis of the plot shows the specific categories being compared, and the
         other axis represents a measured value.
+        """
+        return self(kind="bar", x=x, y=y, **kwargs)
 
-        Parameters
-        ----------
-        x : label or position, default DataFrame.index
-            Column to be used for categories.
-        y : label or position, default All numeric columns in dataframe
-            Columns to be plotted from the DataFrame.
-        **kwargs
-            Keyword arguments to pass on to :meth:`DataFrame.plot`.
-
-        Returns
-        -------
-        :class:`matplotlib.axes.Axes` or numpy.ndarray of them
-
+    @Appender(
+        """
         See Also
         --------
         DataFrame.plot.bar: Vertical bar plot.
@@ -1054,6 +1067,13 @@ def barh(self, x=None, y=None, **kwargs):
             ...                    'lifespan': lifespan}, index=index)
             >>> ax = df.plot.barh()
 
+        We can specify colors for each column
+
+        .. plot::
+            :context: close-figs
+
+            >>> ax = df.plot.barh(color={"speed": "red", "lifespan": "green"})
+
         Plot a column of the DataFrame to a horizontal bar plot
 
         .. plot::
@@ -1079,6 +1099,19 @@ def barh(self, x=None, y=None, **kwargs):
             >>> df = pd.DataFrame({'speed': speed,
             ...                    'lifespan': lifespan}, index=index)
             >>> ax = df.plot.barh(x='lifespan')
+    """
+    )
+    @Substitution(kind="bar")
+    @Appender(_bar_or_line_doc)
+    def barh(self, x=None, y=None, **kwargs):
+        """
+        Make a horizontal bar plot.
+
+        A horizontal bar plot is a plot that presents quantitative data with
+        rectangular bars with lengths proportional to the values that they
+        represent. A bar plot shows comparisons among discrete categories. One
+        axis of the plot shows the specific categories being compared, and the
+        other axis represents a measured value.
         """
         return self(kind="barh", x=x, y=y, **kwargs)
 

pandas/plotting/_matplotlib/core.py

@@ -726,7 +726,10 @@ def _apply_style_colors(self, colors, kwds, col_num, label):
         has_color = "color" in kwds or self.colormap is not None
         nocolor_style = style is None or re.match("[a-z]+", style) is None
         if (has_color or self.subplots) and nocolor_style:
-            kwds["color"] = colors[col_num % len(colors)]
+            if isinstance(colors, dict):
+                kwds["color"] = colors[label]
+            else:
+                kwds["color"] = colors[col_num % len(colors)]
         return style, kwds
 
     def _get_colors(self, num_colors=None, color_kwds="color"):
@@ -1347,6 +1350,8 @@ def _make_plot(self):
             kwds = self.kwds.copy()
             if self._is_series:
                 kwds["color"] = colors
+            elif isinstance(colors, dict):
+                kwds["color"] = colors[label]
             else:
                 kwds["color"] = colors[i % ncolors]
 

pandas/plotting/_matplotlib/style.py

@@ -27,7 +27,11 @@ def _get_standard_colors(
             warnings.warn(
                 "'color' and 'colormap' cannot be used simultaneously. Using 'color'"
             )
-        colors = list(color) if is_list_like(color) else color
+        colors = (
+            list(color)
+            if is_list_like(color) and not isinstance(color, dict)
+            else color
+        )
     else:
         if color_type == "default":
             # need to call list() on the result to copy so we don't

pandas/tests/plotting/test_misc.py

@@ -406,3 +406,24 @@ def test_get_standard_colors_no_appending(self):
         color_list = cm.gnuplot(np.linspace(0, 1, 16))
         p = df.A.plot.bar(figsize=(16, 7), color=color_list)
         assert p.patches[1].get_facecolor() == p.patches[17].get_facecolor()
+
+    @pytest.mark.slow
+    def test_dictionary_color(self):
+        # issue-8193
+        # Test plot color dictionary format
+        data_files = ["a", "b"]
+
+        expected = [(0.5, 0.24, 0.6), (0.3, 0.7, 0.7)]
+
+        df1 = DataFrame(np.random.rand(2, 2), columns=data_files)
+        dic_color = {"b": (0.3, 0.7, 0.7), "a": (0.5, 0.24, 0.6)}
+
+        # Bar color test
+        ax = df1.plot(kind="bar", color=dic_color)
+        colors = [rect.get_facecolor()[0:-1] for rect in ax.get_children()[0:3:2]]
+        assert all(color == expected[index] for index, color in enumerate(colors))
+
+        # Line color test
+        ax = df1.plot(kind="line", color=dic_color)
+        colors = [rect.get_color() for rect in ax.get_lines()[0:2]]
+        assert all(color == expected[index] for index, color in enumerate(colors))