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‎doc/api/next_api_changes/behavior/29152_REC.rst

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Return value of ``pie``
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~~~~~~~~~~~~~~~~~~~~~~~
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Previously, if no *labels* were passed to `~.Axes.pie`, and *labeldistance* was
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not ``None``, empty text labels would be added to the axes and returned. This
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is no longer the case. To continue creating empty labels, either pass an empty
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string with the new *wedge_labels* parameter ``wedge_labels=''`` or, for
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compatibility with older Matplotlib versions, pass an empty string for each wedge
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via the *labels* parameter, i.e. ``labels=[''] * number_of_wedges``. Note the
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latter option will stop working at Matplotlib 3.16.

‎doc/api/next_api_changes/deprecations/29152_REC.rst

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``pie`` *labels* and *labeldistance* parameters
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Currently the *labels* parameter of `~.Axes.pie` is used both for annotating the
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pie wedges directly, and for automatic legend entries. For consistency
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with other plotting methods, in future *labels* will only be used for the legend.
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The *labeldistance* parameter will therefore default to ``None`` from Matplotlib
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3.14, when it will also be deprecated and then removed in Matplotlib 3.16. To
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preserve the existing behavior for now, set ``labeldistance=1.1``. For longer
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term, use the new *wedge_labels* parameter of `~.Axes.pie` instead of *labels*.

‎galleries/examples/misc/svg_filter_pie.py

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# We want to draw the shadow for each pie, but we will not use "shadow"
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# option as it doesn't save the references to the shadow patches.
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pies = ax.pie(fracs, explode=explode, labels=labels, autopct='%1.1f%%')
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pies = ax.pie(fracs, explode=explode,
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wedge_labels=[labels, '{frac:.1%}'], wedge_label_distance=[1.1, 0.6])
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for w in pies[0]:
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for w, label in zip(pies[0], labels):
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# set the id with the label.
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w.set_gid(w.get_label())
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w.set_gid(label)
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# we don't want to draw the edge of the pie
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w.set_edgecolor("none")

‎galleries/examples/pie_and_polar_charts/bar_of_pie.py

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explode = [0.1, 0, 0]
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# rotate so that first wedge is split by the x-axis
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angle = -180 * overall_ratios[0]
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wedges, *_ = ax1.pie(overall_ratios, autopct='%1.1f%%', startangle=angle,
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labels=labels, explode=explode)
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wedges, *_ = ax1.pie(
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overall_ratios, startangle=angle, explode=explode,
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wedge_labels=[labels, '{frac:.1%}'], wedge_label_distance=[1.1, 0.6])
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# bar chart parameters
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age_ratios = [.33, .54, .07, .06]

‎galleries/examples/pie_and_polar_charts/pie_and_donut_labels.py

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# Now it's time for the pie. Starting with a pie recipe, we create the data
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# and a list of labels from it.
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#
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# We can provide a function to the ``autopct`` argument, which will expand
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# automatic percentage labeling by showing absolute values; we calculate
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# the latter back from relative data and the known sum of all values.
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# We can provide a format string to the *wedge_labels* parameter, to
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# automatically label each ingredient's wedge with its weight in grams and
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# percentages.
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#
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# We then create the pie and store the returned objects for later. The first
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# returned element of the returned tuple is a list of the wedges. Those are
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import matplotlib.pyplot as plt
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import numpy as np
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fig, ax = plt.subplots(figsize=(6, 3), subplot_kw=dict(aspect="equal"))
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fig, ax = plt.subplots(figsize=(6, 3))
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recipe = ["375 g flour",
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"75 g sugar",
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"250 g butter",
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"300 g berries"]
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data = [float(x.split()[0]) for x in recipe]
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data = [int(x.split()[0]) for x in recipe]
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ingredients = [x.split()[-1] for x in recipe]
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def func(pct, allvals):
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absolute = int(np.round(pct/100.*np.sum(allvals)))
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return f"{pct:.1f}%\n({absolute:d} g)"
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49-
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wedges, texts, autotexts = ax.pie(data, autopct=lambda pct: func(pct, data),
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textprops=dict(color="w"))
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wedges, texts = ax.pie(data, wedge_labels='{frac:.1%}\n({abs:d}g)',
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textprops=dict(color="w", size=8, weight="bold"))
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ax.legend(wedges, ingredients,
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title="Ingredients",
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loc="center left",
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bbox_to_anchor=(1, 0, 0.5, 1))
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58-
plt.setp(autotexts, size=8, weight="bold")
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ax.set_title("Matplotlib bakery: A pie")
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plt.show()
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data = [225, 90, 50, 60, 100, 5]
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wedges, texts = ax.pie(data, wedgeprops=dict(width=0.5), startangle=-40)
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wedges, _ = ax.pie(data, wedgeprops=dict(width=0.5), startangle=-40)
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bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
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kw = dict(arrowprops=dict(arrowstyle="-"),

‎galleries/examples/pie_and_polar_charts/pie_features.py

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# ------------
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#
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# Plot a pie chart of animals and label the slices. To add
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# labels, pass a list of labels to the *labels* parameter
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# labels, pass a list of labels to the *wedge_labels* parameter.
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import matplotlib.pyplot as plt
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labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'
23-
sizes = [15, 30, 45, 10]
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sizes = [10, 25, 40, 5]
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fig, ax = plt.subplots()
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ax.pie(sizes, labels=labels)
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ax.pie(sizes, wedge_labels=labels)
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# %%
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# Each slice of the pie chart is a `.patches.Wedge` object; therefore in
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# addition to the customizations shown here, each wedge can be customized using
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# the *wedgeprops* argument, as demonstrated in
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# :doc:`/gallery/pie_and_polar_charts/nested_pie`.
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#
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# Controlling label positions
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# ---------------------------
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# If you want the labels outside the pie, set a *wedge_label_distance* greater than 1.
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# This is the distance from the center of the pie as a fraction of its radius.
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fig, ax = plt.subplots()
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ax.pie(sizes, wedge_labels=labels, wedge_label_distance=1.1)
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# %%
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#
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# Auto-label slices
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# -----------------
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#
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# Pass a function or format string to *autopct* to label slices.
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# Pass a format string to *wedge_labels* to label slices with their values
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fig, ax = plt.subplots()
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ax.pie(sizes, wedge_labels='{abs:.1f}')
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# %%
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#
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# or with their percentages
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fig, ax = plt.subplots()
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ax.pie(sizes, wedge_labels='{frac:.1%}')
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# %%
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#
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# or both.
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fig, ax = plt.subplots()
40-
ax.pie(sizes, labels=labels, autopct='%1.1f%%')
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ax.pie(sizes, wedge_labels='{abs:d}\n{frac:.1%}')
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# %%
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#
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# Multiple labels
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# ---------------
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#
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# Pass both a list of labels and a format string to *wedge_labels*, with a
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# corresponding *wedge_label_distance* for each.
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fig, ax = plt.subplots()
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ax.pie(sizes, wedge_labels=[labels, '{frac:.1%}'], wedge_label_distance=[1.1, 0.6])
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# %%
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# By default, the label values are obtained from the percent size of the slice.
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#
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# Color slices
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# ------------
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#
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# Pass a list of colors to *colors* to set the color of each slice.
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fig, ax = plt.subplots()
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ax.pie(sizes, labels=labels,
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ax.pie(sizes, wedge_labels=labels, wedge_label_distance=1.1,
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colors=['olivedrab', 'rosybrown', 'gray', 'saddlebrown'])
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# %%
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5893
# Pass a list of hatch patterns to *hatch* to set the pattern of each slice.
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fig, ax = plt.subplots()
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ax.pie(sizes, labels=labels, hatch=['**O', 'oO', 'O.O', '.||.'])
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# %%
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# Swap label and autopct text positions
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# -------------------------------------
66-
# Use the *labeldistance* and *pctdistance* parameters to position the *labels*
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# and *autopct* text respectively.
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fig, ax = plt.subplots()
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ax.pie(sizes, labels=labels, autopct='%1.1f%%',
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pctdistance=1.25, labeldistance=.6)
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ax.pie(sizes, wedge_labels=labels, wedge_label_distance=1.1,
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hatch=['**O', 'oO', 'O.O', '.||.'])
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# %%
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# *labeldistance* and *pctdistance* are ratios of the radius; therefore they
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# vary between ``0`` for the center of the pie and ``1`` for the edge of the
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# pie, and can be set to greater than ``1`` to place text outside the pie.
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#
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# Explode, shade, and rotate slices
79102
# ---------------------------------
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89112
explode = (0, 0.1, 0, 0) # only "explode" the 2nd slice (i.e. 'Hogs')
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fig, ax = plt.subplots()
92-
ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
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shadow=True, startangle=90)
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ax.pie(sizes, explode=explode, wedge_labels=[labels, '{frac:.1%}'],
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wedge_label_distance=[1.1, 0.6], shadow=True, startangle=90)
94117
plt.show()
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# %%
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108131
fig, ax = plt.subplots()
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110-
ax.pie(sizes, labels=labels, autopct='%.0f%%',
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ax.pie(sizes, wedge_labels=[labels, '{frac:.1%}'],
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wedge_label_distance=[1.1, 0.6],
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textprops={'size': 'small'}, radius=0.5)
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plt.show()
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# the `.Shadow` patch. This can be used to modify the default shadow.
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fig, ax = plt.subplots()
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ax.pie(sizes, explode=explode, labels=labels, autopct='%1.1f%%',
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ax.pie(sizes, explode=explode, wedge_labels=[labels, '{frac:.1%}'],
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wedge_label_distance=[1.1, 0.6],
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shadow={'ox': -0.04, 'edgecolor': 'none', 'shade': 0.9}, startangle=90)
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plt.show()
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‎lib/matplotlib/__init__.py

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import atexit
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from collections import namedtuple
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from collections.abc import MutableMapping
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from collections.abc import MutableMapping, Sequence
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import contextlib
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import functools
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import importlib
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13631363
def _replacer(data, value):
13641364
"""
1365-
Either returns ``data[value]`` or passes ``data`` back, converts either to
1366-
a sequence.
1365+
Either returns ``data[value]`` or passes ``value`` back, converts either to
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a sequence. If ``value`` is a non-string sequence, processes each element
1367+
and returns a list.
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"""
13681369
try:
1369-
# if key isn't a string don't bother
1370+
# if key isn't a string or sequence don't bother
13701371
if isinstance(value, str):
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# try to use __getitem__
13721373
value = data[value]
1374+
elif isinstance(value, Sequence):
1375+
return [_replacer(data, x) for x in value]
13731376
except Exception:
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# key does not exist, silently fall back to key
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pass
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- if called with ``data=None``, forward the other arguments to ``func``;
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- otherwise, *data* must be a mapping; for any argument passed in as a
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string ``name``, replace the argument by ``data[name]`` (if this does not
1447-
throw an exception), then forward the arguments to ``func``.
1450+
throw an exception), then forward the arguments to ``func``. For any
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argument passed as a non-string sequence, replace any string elements
1452+
by ``data[name]`` (if that does not throw an exception).
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14491454
In either case, any argument that is a `MappingView` is also converted to a
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list.

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