matplotlib · rcomer · Dec 22, 2024
diff --git a/doc/api/next_api_changes/behavior/29152_REC.rst b/doc/api/next_api_changes/behavior/29152_REC.rst
@@ -0,0 +1,9 @@
+Return value of ``pie``
+~~~~~~~~~~~~~~~~~~~~~~~
+Previously, if no *labels* were passed to `~.Axes.pie`, and *labeldistance* was
+not ``None``, empty text labels would be added to the axes and returned.  This
+is no longer the case.  To continue creating empty labels, either pass an empty
+string with the new *wedge_labels* parameter ``wedge_labels=''`` or, for
+compatibility with older Matplotlib versions, pass an empty string for each wedge
+via the *labels* parameter, i.e. ``labels=[''] * number_of_wedges``.  Note the
+latter option will stop working at Matplotlib 3.16.
diff --git a/doc/api/next_api_changes/deprecations/29152_REC.rst b/doc/api/next_api_changes/deprecations/29152_REC.rst
@@ -0,0 +1,10 @@
+``pie`` *labels* and *labeldistance* parameters
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+Currently the *labels* parameter of `~.Axes.pie` is used both for annotating the
+pie wedges directly, and for automatic legend entries.  For consistency
+with other plotting methods, in future *labels* will only be used for the legend.
+
+The *labeldistance* parameter will therefore default to ``None`` from Matplotlib
+3.14, when it will also be deprecated and then removed in Matplotlib 3.16.  To
+preserve the existing behavior for now, set ``labeldistance=1.1``.  For longer
+term, use the new *wedge_labels* parameter of `~.Axes.pie` instead of *labels*.
diff --git a/doc/release/next_whats_new/pie_wedge_labels.rst b/doc/release/next_whats_new/pie_wedge_labels.rst
@@ -0,0 +1,18 @@
+New *wedge_labels* parameter for pie
+------------------------------------
+
+`~.Axes.pie` now accepts a *wedge_labels* parameter which may be used to
+annotate the wedges of the pie chart.  It can take
+
+* a list of strings, similar to the existing *labels* parameter
+* a format string in analogy to the existing *autopct* parameter except that it
+  uses the `str.format` method, and it can handle absolute values as well as
+  fractions/percentages
+
+To add multiple labels per wedge, *wedge_labels* can take a sequence of any combination
+of the above two options.
+
+*wedge_labels* have accompanying *wedge_label_distance* and *rotate_wedge_labels*
+parameters, to customise the position and rotation of the labels.
+
+For examples, see :doc:`/gallery/pie_and_polar_charts/pie_features`.
diff --git a/galleries/examples/misc/svg_filter_pie.py b/galleries/examples/misc/svg_filter_pie.py
@@ -28,11 +28,12 @@
 
 # We want to draw the shadow for each pie, but we will not use "shadow"
 # option as it doesn't save the references to the shadow patches.
-pies = ax.pie(fracs, explode=explode, labels=labels, autopct='%1.1f%%')
+pies = ax.pie(fracs, explode=explode,
+              wedge_labels=[labels, '{frac:.1%}'], wedge_label_distance=[1.1, 0.6])
 
-for w in pies[0]:
+for w, label in zip(pies[0], labels):
     # set the id with the label.
-    w.set_gid(w.get_label())
+    w.set_gid(label)
 
     # we don't want to draw the edge of the pie
     w.set_edgecolor("none")

diff --git a/galleries/examples/pie_and_polar_charts/bar_of_pie.py b/galleries/examples/pie_and_polar_charts/bar_of_pie.py
@@ -25,8 +25,9 @@
 explode = [0.1, 0, 0]
 # rotate so that first wedge is split by the x-axis
 angle = -180 * overall_ratios[0]
-wedges, *_ = ax1.pie(overall_ratios, autopct='%1.1f%%', startangle=angle,
-                     labels=labels, explode=explode)
+wedges, *_ = ax1.pie(
+    overall_ratios, startangle=angle, explode=explode,
+    wedge_labels=[labels, '{frac:.1%}'], wedge_label_distance=[1.1, 0.6])
 
 # bar chart parameters
 age_ratios = [.33, .54, .07, .06]

diff --git a/galleries/examples/pie_and_polar_charts/pie_and_donut_labels.py b/galleries/examples/pie_and_polar_charts/pie_and_donut_labels.py
@@ -15,9 +15,9 @@
 # Now it's time for the pie. Starting with a pie recipe, we create the data
 # and a list of labels from it.
 #
-# We can provide a function to the ``autopct`` argument, which will expand
-# automatic percentage labeling by showing absolute values; we calculate
-# the latter back from relative data and the known sum of all values.
+# We can provide a format string to the *wedge_labels* parameter, to
+# automatically label each ingredient's wedge with its weight in grams and
+# percentages.
 #
 # We then create the pie and store the returned objects for later.  The first
 # returned element of the returned tuple is a list of the wedges.  Those are
@@ -31,32 +31,24 @@
 import matplotlib.pyplot as plt
 import numpy as np
 
-fig, ax = plt.subplots(figsize=(6, 3), subplot_kw=dict(aspect="equal"))
+fig, ax = plt.subplots(figsize=(6, 3))
 
 recipe = ["375 g flour",
           "75 g sugar",
           "250 g butter",
           "300 g berries"]
 
-data = [float(x.split()[0]) for x in recipe]
+data = [int(x.split()[0]) for x in recipe]
 ingredients = [x.split()[-1] for x in recipe]
 
-
-def func(pct, allvals):
-    absolute = int(np.round(pct/100.*np.sum(allvals)))
-    return f"{pct:.1f}%\n({absolute:d} g)"
-
-
-wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),
-                                  textprops=dict(color="w"))
+wedges, texts = ax.pie(data, wedge_labels='{frac:.1%}\n({absval:d}g)',
+                       textprops=dict(color="w", size=8, weight="bold"))
 
 ax.legend(wedges, ingredients,
           title="Ingredients",
           loc="center left",
           bbox_to_anchor=(1, 0, 0.5, 1))
 
-plt.setp(autotexts, size=8, weight="bold")
-
 ax.set_title("Matplotlib bakery: A pie")
 
 plt.show()
@@ -97,7 +89,7 @@ def func(pct, allvals):
 
 data = [225, 90, 50, 60, 100, 5]
 
-wedges, texts = ax.pie(data, wedgeprops=dict(width=0.5), startangle=-40)
+wedges, _ = ax.pie(data, wedgeprops=dict(width=0.5), startangle=-40)
 
 bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
 kw = dict(arrowprops=dict(arrowstyle="-"),

diff --git a/galleries/examples/pie_and_polar_charts/pie_features.py b/galleries/examples/pie_and_polar_charts/pie_features.py
@@ -15,40 +15,75 @@
 # ------------
 #
 # Plot a pie chart of animals and label the slices. To add
-# labels, pass a list of labels to the *labels* parameter
+# labels, pass a list of labels to the *wedge_labels* parameter.
 
 import matplotlib.pyplot as plt
 
 labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
-sizes = [15, 30, 45, 10]
+sizes = [12, 24, 36, 8]
 
 fig, ax = plt.subplots()
-ax.pie(sizes, labels=labels)
+ax.pie(sizes, wedge_labels=labels)
 
 # %%
 # Each slice of the pie chart is a `.patches.Wedge` object; therefore in
 # addition to the customizations shown here, each wedge can be customized using
 # the *wedgeprops* argument, as demonstrated in
 # :doc:`/gallery/pie_and_polar_charts/nested_pie`.
 #
+# Controlling label positions
+# ---------------------------
+# If you want the labels outside the pie, set a *wedge_label_distance* greater than 1.
+# This is the distance from the center of the pie as a fraction of its radius.
+
+fig, ax = plt.subplots()
+ax.pie(sizes, wedge_labels=labels, wedge_label_distance=1.1)
+
+# %%
+#
 # Auto-label slices
 # -----------------
 #
-# Pass a function or format string to *autopct* to label slices.
+# Pass a format string to *wedge_labels* to label slices with their values
+
+fig, ax = plt.subplots()
+ax.pie(sizes, wedge_labels='{absval:.1f}')
+
+# %%
+#
+# or with their percentages
+
+fig, ax = plt.subplots()
+ax.pie(sizes, wedge_labels='{frac:.1%}')
+
+# %%
+#
+# or both.
 
 fig, ax = plt.subplots()
-ax.pie(sizes, labels=labels, autopct='%1.1f%%')
+ax.pie(sizes, wedge_labels='{absval:d}\n{frac:.1%}')
+
+
+# %%
+#
+# Multiple labels
+# ---------------
+#
+# Pass both a list of labels and a format string to *wedge_labels*, with a
+# corresponding *wedge_label_distance* for each.
+
+fig, ax = plt.subplots()
+ax.pie(sizes, wedge_labels=[labels, '{frac:.1%}'], wedge_label_distance=[1.1, 0.6])
 
 # %%
-# By default, the label values are obtained from the percent size of the slice.
 #
 # Color slices
 # ------------
 #
 # Pass a list of colors to *colors* to set the color of each slice.
 
 fig, ax = plt.subplots()
-ax.pie(sizes, labels=labels,
+ax.pie(sizes, wedge_labels=labels, wedge_label_distance=1.1,
        colors=['olivedrab', 'rosybrown', 'gray', 'saddlebrown'])
 
 # %%
@@ -58,22 +93,10 @@
 # Pass a list of hatch patterns to *hatch* to set the pattern of each slice.
 
 fig, ax = plt.subplots()
-ax.pie(sizes, labels=labels, hatch=['**O', 'oO', 'O.O', '.||.'])
-
-# %%
-# Swap label and autopct text positions
-# -------------------------------------
-# Use the *labeldistance* and *pctdistance* parameters to position the *labels*
-# and *autopct* text respectively.
-
-fig, ax = plt.subplots()
-ax.pie(sizes, labels=labels, autopct='%1.1f%%',
-       pctdistance=1.25, labeldistance=.6)
+ax.pie(sizes, wedge_labels=labels, wedge_label_distance=1.1,
+       hatch=['**O', 'oO', 'O.O', '.||.'])
 
 # %%
-# *labeldistance* and *pctdistance* are ratios of the radius; therefore they
-# vary between ``0`` for the center of the pie and ``1`` for the edge of the
-# pie, and can be set to greater than ``1`` to place text outside the pie.
 #
 # Explode, shade, and rotate slices
 # ---------------------------------
@@ -89,8 +112,8 @@
 explode = (0, 0.1, 0, 0)  # only "explode" the 2nd slice (i.e. 'Hogs')
 
 fig, ax = plt.subplots()
-ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
-       shadow=True, startangle=90)
+ax.pie(sizes, explode=explode, wedge_labels=[labels, '{frac:.1%}'],
+       wedge_label_distance=[1.1, 0.6], shadow=True, startangle=90)
 plt.show()
 
 # %%
@@ -107,7 +130,8 @@
 
 fig, ax = plt.subplots()
 
-ax.pie(sizes, labels=labels, autopct='%.0f%%',
+ax.pie(sizes, wedge_labels=[labels, '{frac:.1%}'],
+       wedge_label_distance=[1.1, 0.6],
        textprops={'size': 'small'}, radius=0.5)
 plt.show()
 
@@ -119,7 +143,8 @@
 # the `.Shadow` patch. This can be used to modify the default shadow.
 
 fig, ax = plt.subplots()
-ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
+ax.pie(sizes, explode=explode, wedge_labels=[labels, '{frac:.1%}'],
+       wedge_label_distance=[1.1, 0.6],
        shadow={'ox': -0.04, 'edgecolor': 'none', 'shade': 0.9}, startangle=90)
 plt.show()
 

diff --git a/lib/matplotlib/__init__.py b/lib/matplotlib/__init__.py
@@ -1429,7 +1429,8 @@ def _add_data_doc(docstring, replace_names):
     return docstring.replace('    DATA_PARAMETER_PLACEHOLDER', data_doc)
 
 
-def _preprocess_data(func=None, *, replace_names=None, label_namer=None):
+def _preprocess_data(func=None, *, replace_names=None, replace_names_multi=None,
+                     label_namer=None):
     """
     A decorator to add a 'data' kwarg to a function.
 
@@ -1454,6 +1455,11 @@ def func(ax, *args, **kwargs): ...
     replace_names : list of str or None, default: None
         The list of parameter names for which lookup into *data* should be
         attempted. If None, replacement is attempted for all arguments.
+    replace_names_multi : list of str or None, default: None
+        As for *replace_names*, but if a sequence is passed, a lookup into *data*
+        will be attempted for each element of the sequence.  Currently only
+        supported for parameters named in the function signature (not those passed via
+        *args or **kwargs).
     label_namer : str, default: None
         If set e.g. to "namer" (which must be a kwarg in the function's
         signature -- not as ``**kwargs``), if the *namer* argument passed in is
@@ -1471,7 +1477,8 @@ def func(foo, label=None): ...
     if func is None:  # Return the actual decorator.
         return functools.partial(
             _preprocess_data,
-            replace_names=replace_names, label_namer=label_namer)
+            replace_names=replace_names, replace_names_multi=replace_names_multi,
+            label_namer=label_namer)
 
     sig = inspect.signature(func)
     varargs_name = None
@@ -1523,6 +1530,9 @@ def inner(ax, *args, data=None, **kwargs):
             else:
                 if replace_names is None or k in replace_names:
                     bound.arguments[k] = _replacer(data, v)
+                if (replace_names_multi is not None and k in replace_names_multi and
+                        not cbook.is_scalar_or_string(v)):
+                    bound.arguments[k] = [_replacer(data, vi) for vi in v]
 
         new_args = bound.args
         new_kwargs = bound.kwargs