doc/users/prev_whats_new/whats_new_3.9.0.rst
For a list of all of the issues and pull requests since the last revision, see the
:ref:github-stats-3-9-0.
.. contents:: Table of Contents :depth: 4
.. toctree:: :maxdepth: 4
Axes.inset_axes is no longer experimental.Axes.inset_axes is considered stable for use.
Boxplots now support a label parameter to create legend entries. Legend labels can be
passed as a list of strings to label multiple boxes in a single .Axes.boxplot call:
.. plot:: :include-source: :alt: Example of creating 3 boxplots and assigning legend labels as a sequence.
np.random.seed(19680801)
fruit_weights = [
np.random.normal(130, 10, size=100),
np.random.normal(125, 20, size=100),
np.random.normal(120, 30, size=100),
]
labels = ['peaches', 'oranges', 'tomatoes']
colors = ['peachpuff', 'orange', 'tomato']
fig, ax = plt.subplots()
ax.set_ylabel('fruit weight (g)')
bplot = ax.boxplot(fruit_weights,
patch_artist=True, # fill with color
label=labels)
# fill with colors
for patch, color in zip(bplot['boxes'], colors):
patch.set_facecolor(color)
ax.set_xticks([])
ax.legend()
Or as a single string to each individual .Axes.boxplot:
.. plot:: :include-source: :alt: Example of creating 2 boxplots and assigning each legend label as a string.
fig, ax = plt.subplots()
data_A = np.random.random((100, 3))
data_B = np.random.random((100, 3)) + 0.2
pos = np.arange(3)
ax.boxplot(data_A, positions=pos - 0.2, patch_artist=True, label='Box A',
boxprops={'facecolor': 'steelblue'})
ax.boxplot(data_B, positions=pos + 0.2, patch_artist=True, label='Box B',
boxprops={'facecolor': 'lightblue'})
ax.legend()
usetex=TrueIt is common, with .Axes.pie, to specify labels that include a percent sign (%),
which denotes a comment for LaTeX. When enabling LaTeX with :rc:text.usetex or passing
textprops={"usetex": True}, this used to cause the percent sign to disappear.
Now, the percent sign is automatically escaped (by adding a preceding backslash) so that
it appears regardless of the usetex setting. If you have pre-escaped the percent
sign, this will be detected, and remain as is.
hatch parameter for stackplotThe ~.Axes.stackplot hatch parameter now accepts a list of strings describing
hatching styles that will be applied sequentially to the layers in the stack:
.. plot:: :include-source: :alt: Two charts, identified as ax1 and ax2, showing "stackplots", i.e. one-dimensional distributions of data stacked on top of one another. The first plot, ax1 has cross-hatching on all slices, having been given a single string as the "hatch" argument. The second plot, ax2 has different styles of hatching on each slice - diagonal hatching in opposite directions on the first two slices, cross-hatching on the third slice, and open circles on the fourth.
fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(10,5))
cols = 10
rows = 4
data = (
np.reshape(np.arange(0, cols, 1), (1, -1)) ** 2
+ np.reshape(np.arange(0, rows), (-1, 1))
+ np.random.random((rows, cols))*5
)
x = range(data.shape[1])
ax1.stackplot(x, data, hatch="x")
ax2.stackplot(x, data, hatch=["//","\\","x","o"])
ax1.set_title("hatch='x'")
ax2.set_title("hatch=['//','\\\\','x','o']")
plt.show()
Setting the parameter side to 'low' or 'high' allows to only plot one half of the
.Axes.violinplot.
.. plot:: :include-source: :alt: Three copies of a vertical violin plot; first in blue showing the default of both sides, followed by an orange copy that only shows the "low" (or left, in this case) side, and finally a green copy that only shows the "high" (or right) side.
# Fake data with reproducible random state.
np.random.seed(19680801)
data = np.random.normal(0, 8, size=100)
fig, ax = plt.subplots()
ax.violinplot(data, [0], showmeans=True, showextrema=True)
ax.violinplot(data, [1], showmeans=True, showextrema=True, side='low')
ax.violinplot(data, [2], showmeans=True, showextrema=True, side='high')
ax.set_title('Violin Sides Example')
ax.set_xticks([0, 1, 2], ['Default', 'side="low"', 'side="high"'])
ax.set_yticklabels([])
axhline and axhspan on polar axes... now draw circles and circular arcs (~.Axes.axhline) or annuli and wedges
(~.Axes.axhspan).
.. plot:: :include-source: :alt: A sample polar plot, that contains an axhline at radius 1, an axhspan annulus between radius 0.8 and 0.9, and an axhspan wedge between radius 0.6 and 0.7 and 288° and 324°.
fig = plt.figure()
ax = fig.add_subplot(projection="polar")
ax.set_rlim(0, 1.2)
ax.axhline(1, c="C0", alpha=.5)
ax.axhspan(.8, .9, fc="C1", alpha=.5)
ax.axhspan(.6, .7, .8, .9, fc="C2", alpha=.5)
Subplot axes titles can be misaligned vertically if tick labels or xlabels are placed at
the top of one subplot. The new ~.Figure.align_titles method on the .Figure class
will now align the titles vertically.
.. plot:: :include-source: :alt: A figure with two Axes side-by-side, the second of which with ticks on top. The Axes titles and x-labels appear unaligned with each other due to these ticks.
fig, axs = plt.subplots(1, 2, layout='constrained')
axs[0].plot(np.arange(0, 1e6, 1000))
axs[0].set_title('Title 0')
axs[0].set_xlabel('XLabel 0')
axs[1].plot(np.arange(1, 0, -0.1) * 2000, np.arange(1, 0, -0.1))
axs[1].set_title('Title 1')
axs[1].set_xlabel('XLabel 1')
axs[1].xaxis.tick_top()
axs[1].tick_params(axis='x', rotation=55)
.. plot:: :include-source: :alt: A figure with two Axes side-by-side, the second of which with ticks on top. Unlike the previous figure, the Axes titles and x-labels appear aligned.
fig, axs = plt.subplots(1, 2, layout='constrained')
axs[0].plot(np.arange(0, 1e6, 1000))
axs[0].set_title('Title 0')
axs[0].set_xlabel('XLabel 0')
axs[1].plot(np.arange(1, 0, -0.1) * 2000, np.arange(1, 0, -0.1))
axs[1].set_title('Title 1')
axs[1].set_xlabel('XLabel 1')
axs[1].xaxis.tick_top()
axs[1].tick_params(axis='x', rotation=55)
fig.align_labels()
fig.align_titles()
axisartist can now be used together with standard Formatters... instead of being limited to axisartist-specific ones.
Minor ticks on an ~matplotlib.axis.Axis can be displayed or removed using
~matplotlib.axis.Axis.minorticks_on and ~matplotlib.axis.Axis.minorticks_off; e.g.,
ax.xaxis.minorticks_on(). See also ~matplotlib.axes.Axes.minorticks_on.
StrMethodFormatter now respects axes.unicode_minusWhen formatting negative values, .StrMethodFormatter will now use unicode minus signs
if :rc:axes.unicode_minus is set.
>>> from matplotlib.ticker import StrMethodFormatter
>>> with plt.rc_context({'axes.unicode_minus': False}):
... formatter = StrMethodFormatter('{x}')
... print(formatter.format_data(-10))
-10
>>> with plt.rc_context({'axes.unicode_minus': True}):
... formatter = StrMethodFormatter('{x}')
... print(formatter.format_data(-10))
−10
Previously, setting the zorder of a subfigure had no effect, and those were plotted on top of any figure-level artists (i.e for example on top of fig-level legends). Now, subfigures behave like any other artists, and their zorder can be controlled, with default a zorder of 0.
.. plot:: :include-source: :alt: Example on controlling the zorder of a subfigure
x = np.linspace(1, 10, 10)
y1, y2 = x, -x
fig = plt.figure(constrained_layout=True)
subfigs = fig.subfigures(nrows=1, ncols=2)
for subfig in subfigs:
axarr = subfig.subplots(2, 1)
for ax in axarr.flatten():
(l1,) = ax.plot(x, y1, label="line1")
(l2,) = ax.plot(x, y2, label="line2")
subfigs[0].set_zorder(6)
l = fig.legend(handles=[l1, l2], loc="upper center", ncol=2)
.Axes.get_xmargin, .Axes.get_ymargin and .Axes3D.get_zmargin methods have been
added to return the margin values set by .Axes.set_xmargin, .Axes.set_ymargin and
.Axes3D.set_zmargin, respectively.
mathtext documentation improvementsThe documentation is updated to take information directly from the parser. This means
that (almost) all supported symbols, operators, etc. are shown at :ref:mathtext.
mathtext spacing correctionsAs consequence of the updated documentation, the spacing on a number of relational and operator symbols were correctly classified and therefore will be spaced properly.
The .CheckButtons and .RadioButtons widgets now support clearing their state by
calling their .clear method. Note that it is not possible to have no selected radio
buttons, so the selected option at construction time is selected.
Previously, setting the limits of a 3D axis would always add a small margin to the
limits. Limits are now set exactly by default. The newly introduced rcparam
axes3d.automargin can be used to revert to the old behavior where margin is
automatically added.
.. plot:: :include-source: :alt: Example of the new behavior of 3D axis limits, and how setting the rcParam reverts to the old behavior.
fig, axs = plt.subplots(1, 2, subplot_kw={'projection': '3d'})
plt.rcParams['axes3d.automargin'] = True
axs[0].set(xlim=(0, 1), ylim=(0, 1), zlim=(0, 1), title='Old Behavior')
plt.rcParams['axes3d.automargin'] = False # the default in 3.9.0
axs[1].set(xlim=(0, 1), ylim=(0, 1), zlim=(0, 1), title='New Behavior')
New :class:~matplotlib.backends.registry.BackendRegistry class is the single source of
truth for available backends. The singleton instance is
matplotlib.backends.backend_registry. It is used internally by Matplotlib, and also
IPython (and therefore Jupyter) starting with IPython 8.24.0.
There are three sources of backends: built-in (source code is within the Matplotlib
repository), explicit module://some.backend syntax (backend is obtained by loading
the module), or via an entry point (self-registering backend in an external package).
To obtain a list of all registered backends use:
>>> from matplotlib.backends import backend_registry
>>> backend_registry.list_all()
widths, heights and angles setter to EllipseCollectionThe widths, heights and angles values of the
~matplotlib.collections.EllipseCollection can now be changed after the collection has
been created.
.. plot:: :include-source:
from matplotlib.collections import EllipseCollection
rng = np.random.default_rng(0)
widths = (2, )
heights = (3, )
angles = (45, )
offsets = rng.random((10, 2)) * 10
fig, ax = plt.subplots()
ec = EllipseCollection(
widths=widths,
heights=heights,
angles=angles,
offsets=offsets,
units='x',
offset_transform=ax.transData,
)
ax.add_collection(ec)
ax.set_xlim(-2, 12)
ax.set_ylim(-2, 12)
new_widths = rng.random((10, 2)) * 2
new_heights = rng.random((10, 2)) * 3
new_angles = rng.random((10, 2)) * 180
ec.set(widths=new_widths, heights=new_heights, angles=new_angles)
image.interpolation_stage rcParamThis new rcParam controls whether image interpolation occurs in "data" space or in "rgba" space.
A setter method has been added that allows updating the position of the .patches.Arrow
object without requiring a full re-draw.
.. plot::
:include-source:
:alt: Example of changing the position of the arrow with the new set_data method.
from matplotlib import animation
from matplotlib.patches import Arrow
fig, ax = plt.subplots()
ax.set_xlim(0, 10)
ax.set_ylim(0, 10)
a = Arrow(2, 0, 0, 10)
ax.add_patch(a)
# code for modifying the arrow
def update(i):
a.set_data(x=.5, dx=i, dy=6, width=2)
ani = animation.FuncAnimation(fig, update, frames=15, interval=90, blit=False)
plt.show()
When mousing over a ~matplotlib.image.NonUniformImage, the data values are now
displayed.