Feature implementation from commits b18407b..7fa82d7

doc/_embedded_plots/grouped_bar.py

@@ -0,0 +1,15 @@
+import matplotlib.pyplot as plt
+
+categories = ['A', 'B']
+data0 = [1.0, 3.0]
+data1 = [1.4, 3.4]
+data2 = [1.8, 3.8]
+
+fig, ax = plt.subplots(figsize=(4, 2.2))
+ax.grouped_bar(
+    [data0, data1, data2],
+    tick_labels=categories,
+    labels=['dataset 0', 'dataset 1', 'dataset 2'],
+    colors=['#1f77b4', '#58a1cf', '#abd0e6'],
+)
+ax.legend()

doc/api/axes_api.rst

@@ -67,6 +67,7 @@ Basic
    Axes.bar
    Axes.barh
    Axes.bar_label
+   Axes.grouped_bar
 
    Axes.stem
    Axes.eventplot

doc/api/bezier_api.rst

@@ -5,4 +5,5 @@
 .. automodule:: matplotlib.bezier
    :members:
    :undoc-members:
+   :special-members: __call__
    :show-inheritance:

doc/api/next_api_changes/deprecations/30070-OG.rst

@@ -0,0 +1,4 @@
+``BezierSegment.point_at_t``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+... is deprecated. Instead, it is possible to call the BezierSegment with an argument.

doc/api/next_api_changes/deprecations/30088-AL.rst

@@ -0,0 +1,4 @@
+*fontfile* parameter of ``PdfFile.createType1Descriptor``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+This parameter is deprecated; all relevant pieces of information are now
+directly extracted from the *t1font* argument.

doc/api/next_api_changes/removals/30004-DS.rst

@@ -0,0 +1,10 @@
+``apply_theta_transforms`` option in ``PolarTransform``
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+Applying theta transforms in `~matplotlib.projections.polar.PolarTransform` and
+`~matplotlib.projections.polar.InvertedPolarTransform` has been removed, and
+the ``apply_theta_transforms`` keyword argument removed from both classes.
+
+If you need to retain the behaviour where theta values
+are transformed, chain the ``PolarTransform`` with a `~matplotlib.transforms.Affine2D`
+transform that performs the theta shift and/or sign shift.

doc/api/pyplot_summary.rst

@@ -60,6 +60,7 @@ Basic
    bar
    barh
    bar_label
+   grouped_bar
    stem
    eventplot
    pie

doc/users/next_whats_new/grouped_bar.rst

@@ -0,0 +1,26 @@
+Grouped bar charts
+------------------
+
+The new method `~.Axes.grouped_bar()` simplifies the creation of grouped bar charts
+significantly. It supports different input data types (lists of datasets, dicts of
+datasets, data in 2D arrays, pandas DataFrames), and allows for easy customization
+of placement via controllable distances between bars and between bar groups.
+
+Example:
+
+.. plot::
+    :include-source: true
+    :alt: Diagram of a grouped bar chart of 3 datasets with 2 categories.
+
+    import matplotlib.pyplot as plt
+
+    categories = ['A', 'B']
+    datasets = {
+        'dataset 0': [1, 11],
+        'dataset 1': [3, 13],
+        'dataset 2': [5, 15],
+    }
+
+    fig, ax = plt.subplots()
+    ax.grouped_bar(datasets, tick_labels=categories)
+    ax.legend()

galleries/examples/axisartist/demo_axis_direction.py

@@ -22,7 +22,7 @@ def setup_axes(fig, rect):
     grid_helper = GridHelperCurveLinear(
         (
             Affine2D().scale(np.pi/180., 1.) +
-            PolarAxes.PolarTransform(apply_theta_transforms=False)
+            PolarAxes.PolarTransform()
         ),
         extreme_finder=angle_helper.ExtremeFinderCycle(
             20, 20,

galleries/examples/axisartist/demo_curvelinear_grid.py

@@ -54,8 +54,7 @@ def curvelinear_test2(fig):
 
     # PolarAxes.PolarTransform takes radian. However, we want our coordinate
     # system in degree
-    tr = Affine2D().scale(np.pi/180, 1) + PolarAxes.PolarTransform(
-        apply_theta_transforms=False)
+    tr = Affine2D().scale(np.pi/180, 1) + PolarAxes.PolarTransform()
     # Polar projection, which involves cycle, and also has limits in
     # its coordinates, needs a special method to find the extremes
     # (min, max of the coordinate within the view).

galleries/examples/axisartist/demo_floating_axes.py

@@ -54,7 +54,7 @@ def setup_axes2(fig, rect):
     With custom locator and formatter.
     Note that the extreme values are swapped.
     """
-    tr = PolarAxes.PolarTransform(apply_theta_transforms=False)
+    tr = PolarAxes.PolarTransform()
 
     pi = np.pi
     angle_ticks = [(0, r"$0$"),
@@ -99,8 +99,7 @@ def setup_axes3(fig, rect):
     # scale degree to radians
     tr_scale = Affine2D().scale(np.pi/180., 1.)
 
-    tr = tr_rotate + tr_scale + PolarAxes.PolarTransform(
-        apply_theta_transforms=False)
+    tr = tr_rotate + tr_scale + PolarAxes.PolarTransform()
 
     grid_locator1 = angle_helper.LocatorHMS(4)
     tick_formatter1 = angle_helper.FormatterHMS()

galleries/examples/axisartist/demo_floating_axis.py

@@ -22,8 +22,7 @@
 def curvelinear_test2(fig):
     """Polar projection, but in a rectangular box."""
     # see demo_curvelinear_grid.py for details
-    tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform(
-        apply_theta_transforms=False)
+    tr = Affine2D().scale(np.pi / 180., 1.) + PolarAxes.PolarTransform()
 
     extreme_finder = angle_helper.ExtremeFinderCycle(20,
                                                      20,

galleries/examples/axisartist/simple_axis_pad.py

@@ -21,8 +21,7 @@ def setup_axes(fig, rect):
     """Polar projection, but in a rectangular box."""
 
     # see demo_curvelinear_grid.py for details
-    tr = Affine2D().scale(np.pi/180., 1.) + PolarAxes.PolarTransform(
-        apply_theta_transforms=False)
+    tr = Affine2D().scale(np.pi/180., 1.) + PolarAxes.PolarTransform()
 
     extreme_finder = angle_helper.ExtremeFinderCycle(20, 20,
                                                      lon_cycle=360,

galleries/examples/lines_bars_and_markers/barchart.py

@@ -10,7 +10,6 @@
 # data from https://allisonhorst.github.io/palmerpenguins/
 
 import matplotlib.pyplot as plt
-import numpy as np
 
 species = ("Adelie", "Chinstrap", "Gentoo")
 penguin_means = {
@@ -19,22 +18,15 @@
     'Flipper Length': (189.95, 195.82, 217.19),
 }
 
-x = np.arange(len(species))  # the label locations
-width = 0.25  # the width of the bars
-multiplier = 0
-
 fig, ax = plt.subplots(layout='constrained')
 
-for attribute, measurement in penguin_means.items():
-    offset = width * multiplier
-    rects = ax.bar(x + offset, measurement, width, label=attribute)
-    ax.bar_label(rects, padding=3)
-    multiplier += 1
+res = ax.grouped_bar(penguin_means, tick_labels=species, group_spacing=1)
+for container in res.bar_containers:
+    ax.bar_label(container, padding=3)
 
-# Add some text for labels, title and custom x-axis tick labels, etc.
+# Add some text for labels, title, etc.
 ax.set_ylabel('Length (mm)')
 ax.set_title('Penguin attributes by species')
-ax.set_xticks(x + width, species)
 ax.legend(loc='upper left', ncols=3)
 ax.set_ylim(0, 250)