Changes to the default style#
The most important changes in matplotlib 2.0 are the changes to the default style.
While it is impossible to select the best default for all cases, these are designed to work well in the most common cases.
A 'classic' style sheet is provided so reverting to the 1.x default values is a single line of python
import matplotlib.style
import matplotlib as mpl
mpl.style.use('classic')
See The matplotlibrc file for details about how to persistently and selectively revert many of these changes.
Colors, color cycles, and colormaps#
Colors in default property cycle#
The colors in the default property cycle have been changed from
['b', 'g', 'r', 'c', 'm', 'y', 'k']
to the category10
color palette used by Vega and
d3
originally developed at Tableau.
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In addition to changing the colors, an additional method to specify
colors was added. Previously, the default colors were the single
character short-hand notations for red, green, blue, cyan, magenta,
yellow, and black. This made them easy to type and usable in the
abbreviated style string in plot
, however the new default colors
are only specified via hex values. To access these colors outside of
the property cycling the notation for colors 'CN'
, where N
takes values 0-9, was added to
denote the first 10 colors in rcParams["axes.prop_cycle"]
(default: cycler('color', ['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf'])
). See
Specifying colors for more details.
To restore the old color cycle use
from cycler import cycler
mpl.rcParams['axes.prop_cycle'] = cycler(color='bgrcmyk')
or set
axes.prop_cycle : cycler('color', 'bgrcmyk')
in your matplotlibrc
file.
Colormap#
The new default colormap used by matplotlib.cm.ScalarMappable
instances is
'viridis' (aka option D).
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For an introduction to color theory and how 'viridis' was generated watch Nathaniel Smith and Stéfan van der Walt's talk from SciPy2015. See here for many more details about the other alternatives and the tools used to create the color map. For details on all of the colormaps available in matplotlib see Choosing Colormaps.
The previous default can be restored using
mpl.rcParams['image.cmap'] = 'jet'
or setting
image.cmap : 'jet'
in your matplotlibrc
file; however this is strongly discouraged.
Interactive figures#
The default interactive figure background color has changed from grey to white, which matches the default background color used when saving.
The previous defaults can be restored by
mpl.rcParams['figure.facecolor'] = '0.75'
or by setting
figure.facecolor : '0.75'
in your matplotlibrc
file.
Grid lines#
The default style of grid lines was changed from black dashed lines to thicker solid light grey lines.
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The previous default can be restored by using:
mpl.rcParams['grid.color'] = 'k'
mpl.rcParams['grid.linestyle'] = ':'
mpl.rcParams['grid.linewidth'] = 0.5
or by setting:
grid.color : k # grid color
grid.linestyle : : # dotted
grid.linewidth : 0.5 # in points
in your matplotlibrc
file.
Figure size, font size, and screen dpi#
The default dpi used for on-screen display was changed from 80 dpi to
100 dpi, the same as the default dpi for saving files. Due to this
change, the on-screen display is now more what-you-see-is-what-you-get
for saved files. To keep the figure the same size in terms of pixels, in
order to maintain approximately the same size on the screen, the
default figure size was reduced from 8x6 inches to 6.4x4.8 inches. As
a consequence of this the default font sizes used for the title, tick
labels, and axes labels were reduced to maintain their size relative
to the overall size of the figure. By default the dpi of the saved
image is now the dpi of the Figure
instance being
saved.
This will have consequences if you are trying to match text in a figure directly with external text.
The previous defaults can be restored by
mpl.rcParams['figure.figsize'] = [8.0, 6.0]
mpl.rcParams['figure.dpi'] = 80
mpl.rcParams['savefig.dpi'] = 100
mpl.rcParams['font.size'] = 12
mpl.rcParams['legend.fontsize'] = 'large'
mpl.rcParams['figure.titlesize'] = 'medium'
or by setting:
figure.figsize : [8.0, 6.0]
figure.dpi : 80
savefig.dpi : 100
font.size : 12.0
legend.fontsize : 'large'
figure.titlesize : 'medium'
In your matplotlibrc
file.
In addition, the forward
kwarg to
set_size_inches
now defaults to True
to improve
the interactive experience. Backend canvases that adjust the size of
their bound matplotlib.figure.Figure
must pass forward=False
to
avoid circular behavior. This default is not configurable.
Plotting functions#
scatter
#
The following changes were made to the default behavior of
scatter
The default size of the elements in a scatter plot is now based on
rcParams["lines.markersize"]
(default:6.0
) so it is consistent withplot(X, Y, 'o')
. The old value was 20, and the new value is 36 (6^2).Scatter markers no longer have a black edge.
If the color of the markers is not specified it will follow the property cycle, pulling from the 'patches' cycle on the
Axes
.
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The classic default behavior of scatter
can
only be recovered through mpl.style.use('classic')
. The marker size
can be recovered via
mpl.rcParam['lines.markersize'] = np.sqrt(20)
however, this will also affect the default marker size of
plot
. To recover the classic behavior on
a per-call basis pass the following kwargs:
classic_kwargs = {'s': 20, 'edgecolors': 'k', 'c': 'b'}
plot
#
The following changes were made to the default behavior of
plot
the default linewidth increased from 1 to 1.5
the dash patterns associated with
'--'
,':'
, and'-.'
have changedthe dash patterns now scale with line width
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The previous defaults can be restored by setting:
mpl.rcParams['lines.linewidth'] = 1.0
mpl.rcParams['lines.dashed_pattern'] = [6, 6]
mpl.rcParams['lines.dashdot_pattern'] = [3, 5, 1, 5]
mpl.rcParams['lines.dotted_pattern'] = [1, 3]
mpl.rcParams['lines.scale_dashes'] = False
or by setting:
lines.linewidth : 1.0
lines.dashed_pattern : 6, 6
lines.dashdot_pattern : 3, 5, 1, 5
lines.dotted_pattern : 1, 3
lines.scale_dashes: False
in your matplotlibrc
file.
errorbar
#
By default, caps on the ends of errorbars are not present.
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This also changes the return value of
errorbar()
as the list of 'caplines' will
be empty by default.
The previous defaults can be restored by setting:
mpl.rcParams['errorbar.capsize'] = 3
or by setting
errorbar.capsize : 3
in your matplotlibrc
file.
boxplot
#
Previously, boxplots were composed of a mish-mash of styles that were, for better for worse, inherited from Matlab. Most of the elements were blue, but the medians were red. The fliers (outliers) were black plus-symbols ('+') and the whiskers were dashed lines, which created ambiguity if the (solid and black) caps were not drawn.
For the new defaults, everything is black except for the median and mean lines (if drawn), which are set to the first two elements of the current color cycle. Also, the default flier markers are now hollow circles, which maintain the ability of the plus-symbols to overlap without obscuring data too much.
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The previous defaults can be restored by setting:
mpl.rcParams['boxplot.flierprops.color'] = 'k'
mpl.rcParams['boxplot.flierprops.marker'] = '+'
mpl.rcParams['boxplot.flierprops.markerfacecolor'] = 'none'
mpl.rcParams['boxplot.flierprops.markeredgecolor'] = 'k'
mpl.rcParams['boxplot.boxprops.color'] = 'b'
mpl.rcParams['boxplot.whiskerprops.color'] = 'b'
mpl.rcParams['boxplot.whiskerprops.linestyle'] = '--'
mpl.rcParams['boxplot.medianprops.color'] = 'r'
mpl.rcParams['boxplot.meanprops.color'] = 'r'
mpl.rcParams['boxplot.meanprops.marker'] = '^'
mpl.rcParams['boxplot.meanprops.markerfacecolor'] = 'r'
mpl.rcParams['boxplot.meanprops.markeredgecolor'] = 'k'
mpl.rcParams['boxplot.meanprops.markersize'] = 6
mpl.rcParams['boxplot.meanprops.linestyle'] = '--'
mpl.rcParams['boxplot.meanprops.linewidth'] = 1.0
or by setting:
boxplot.flierprops.color: 'k'
boxplot.flierprops.marker: '+'
boxplot.flierprops.markerfacecolor: 'none'
boxplot.flierprops.markeredgecolor: 'k'
boxplot.boxprops.color: 'b'
boxplot.whiskerprops.color: 'b'
boxplot.whiskerprops.linestyle: '--'
boxplot.medianprops.color: 'r'
boxplot.meanprops.color: 'r'
boxplot.meanprops.marker: '^'
boxplot.meanprops.markerfacecolor: 'r'
boxplot.meanprops.markeredgecolor: 'k'
boxplot.meanprops.markersize: 6
boxplot.meanprops.linestyle: '--'
boxplot.meanprops.linewidth: 1.0
in your matplotlibrc
file.
fill_between
and fill_betweenx
#
fill_between
and
fill_betweenx
both follow the patch color
cycle.
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If the facecolor is set via the facecolors
or color
keyword argument,
then the color is not cycled.
To restore the previous behavior, explicitly pass the keyword argument
facecolors='C0'
to the method call.
Patch edges and color#
Most artists drawn with a patch (~matplotlib.axes.Axes.bar
,
~matplotlib.axes.Axes.pie
, etc) no longer have a black edge by
default. The default face color is now 'C0'
instead of 'b'
.
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The previous defaults can be restored by setting:
mpl.rcParams['patch.force_edgecolor'] = True
mpl.rcParams['patch.facecolor'] = 'b'
or by setting:
patch.facecolor : b
patch.force_edgecolor : True
in your matplotlibrc
file.
hexbin
#
The default value of the linecolor keyword argument for hexbin
has
changed from 'none'
to 'face'
. If 'none' is now supplied, no line edges
are drawn around the hexagons.
bar
and barh
#
The default value of the align
kwarg for both
bar
and barh
is changed from
'edge'
to 'center'
.
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To restore the previous behavior explicitly pass the keyword argument
align='edge'
to the method call.
Hatching#
The color of the lines in the hatch is now determined by
If an edge color is explicitly set, use that for the hatch color
If the edge color is not explicitly set, use
rcParams["hatch.color"]
(default:'black'
) which is looked up at artist creation time.
The width of the lines in a hatch pattern is now configurable by the
rcParams rcParams["hatch.linewidth"]
(default: 1.0
), which defaults to 1 point. The old
behavior for the line width was different depending on backend:
PDF: 0.1 pt
SVG: 1.0 pt
PS: 1 px
Agg: 1 px
The old line width behavior cannot be restored across all backends simultaneously, but can be restored for a single backend by setting:
mpl.rcParams['hatch.linewidth'] = 0.1 # previous pdf hatch linewidth
mpl.rcParams['hatch.linewidth'] = 1.0 # previous svg hatch linewidth
The behavior of the PS and Agg backends was DPI dependent, thus:
mpl.rcParams['figure.dpi'] = dpi
mpl.rcParams['savefig.dpi'] = dpi # or leave as default 'figure'
mpl.rcParams['hatch.linewidth'] = 1.0 / dpi # previous ps and Agg hatch linewidth
There is no direct API level control of the hatch color or linewidth.
Hatching patterns are now rendered at a consistent density, regardless of DPI. Formerly, high DPI figures would be more dense than the default, and low DPI figures would be less dense. This old behavior cannot be directly restored, but the density may be increased by repeating the hatch specifier.
Fonts#
Normal text#
The default font has changed from "Bitstream Vera Sans" to "DejaVu Sans". DejaVu Sans has additional international and math characters, but otherwise has the same appearance as Bitstream Vera Sans. Latin, Greek, Cyrillic, Armenian, Georgian, Hebrew, and Arabic are all supported (but right-to-left rendering is still not handled by matplotlib). In addition, DejaVu contains a sub-set of emoji symbols.
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Math text#
The default math font when using the built-in math rendering engine
(mathtext) has changed from "Computer Modern" (i.e. LaTeX-like) to
"DejaVu Sans". This change has no effect if the
TeX backend is used (i.e. text.usetex
is True
).
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To revert to the old behavior set the:
mpl.rcParams['mathtext.fontset'] = 'cm'
mpl.rcParams['mathtext.rm'] = 'serif'
or set:
mathtext.fontset: cm
mathtext.rm : serif
in your matplotlibrc
file.
This rcParam
is consulted when the text is drawn, not when the
artist is created. Thus all mathtext on a given canvas
will use the
same fontset.
Legends#
By default, the number of points displayed in a legend is now 1.
The default legend location is
'best'
, so the legend will be automatically placed in a location to minimize overlap with data.The legend defaults now include rounded corners, a lighter boundary, and partially transparent boundary and background.
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The previous defaults can be restored by setting:
mpl.rcParams['legend.fancybox'] = False
mpl.rcParams['legend.loc'] = 'upper right'
mpl.rcParams['legend.numpoints'] = 2
mpl.rcParams['legend.fontsize'] = 'large'
mpl.rcParams['legend.framealpha'] = None
mpl.rcParams['legend.scatterpoints'] = 3
mpl.rcParams['legend.edgecolor'] = 'inherit'
or by setting:
legend.fancybox : False
legend.loc : upper right
legend.numpoints : 2 # the number of points in the legend line
legend.fontsize : large
legend.framealpha : None # opacity of legend frame
legend.scatterpoints : 3 # number of scatter points
legend.edgecolor : inherit # legend edge color ('inherit'
# means it uses axes.edgecolor)
in your matplotlibrc
file.
Image#
Interpolation#
The default interpolation method for imshow
is
now 'nearest'
and by default it resamples the data (both up and down
sampling) before colormapping.
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To restore the previous behavior set:
mpl.rcParams['image.interpolation'] = 'bilinear'
mpl.rcParams['image.resample'] = False
or set:
image.interpolation : bilinear # see help(imshow) for options
image.resample : False
in your matplotlibrc
file.
Colormapping pipeline#
Previously, the input data was normalized, then colormapped, and then resampled to the resolution required for the screen. This meant that the final resampling was being done in color space. Because the color maps are not generally linear in RGB space, colors not in the colormap may appear in the final image. This bug was addressed by an almost complete overhaul of the image handling code.
The input data is now normalized, then resampled to the correct resolution (in normalized dataspace), and then colormapped to RGB space. This ensures that only colors from the colormap appear in the final image. (If your viewer subsequently resamples the image, the artifact may reappear.)
The previous behavior cannot be restored.
Shading#
The default shading mode for light source shading, in
matplotlib.colors.LightSource.shade
, is nowoverlay
. Formerly, it washsv
.
Plot layout#
Auto limits#
The previous auto-scaling behavior was to find 'nice' round numbers as view limits that enclosed the data limits, but this could produce bad plots if the data happened to fall on a vertical or horizontal line near the chosen 'round number' limit. The new default sets the view limits to 5% wider than the data range.
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The size of the padding in the x and y directions is controlled by the
'axes.xmargin'
and 'axes.ymargin'
rcParams respectively. Whether
the view limits should be 'round numbers' is controlled by
rcParams["axes.autolimit_mode"]
(default: 'data'
). In the original 'round_number'
mode,
the view limits coincide with ticks.
The previous default can be restored by using:
mpl.rcParams['axes.autolimit_mode'] = 'round_numbers'
mpl.rcParams['axes.xmargin'] = 0
mpl.rcParams['axes.ymargin'] = 0
or setting:
axes.autolimit_mode: round_numbers
axes.xmargin: 0
axes.ymargin: 0
in your matplotlibrc
file.
Z-order#
Ticks and grids are now plotted above solid elements such as filled contours, but below lines. To return to the previous behavior of plotting ticks and grids above lines, set
rcParams['axes.axisbelow'] = False
.
Ticks#
Direction#
To reduce the collision of tick marks with data, the default ticks now point outward by default. In addition, ticks are now drawn only on the bottom and left spines to prevent a porcupine appearance, and for a cleaner separation between subplots.
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To restore the previous behavior set:
mpl.rcParams['xtick.direction'] = 'in'
mpl.rcParams['ytick.direction'] = 'in'
mpl.rcParams['xtick.top'] = True
mpl.rcParams['ytick.right'] = True
or set:
xtick.top: True
xtick.direction: in
ytick.right: True
ytick.direction: in
in your matplotlibrc
file.
Number of ticks#
The default Locator
used for the x and y axis is
AutoLocator
which tries to find, up to some
maximum number, 'nicely' spaced ticks. The locator now includes
an algorithm to estimate the maximum number of ticks that will leave
room for the tick labels. By default it also ensures that there are at least
two ticks visible.
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There is no way, other than using mpl.style.use('classic')
, to restore the
previous behavior as the default. On an axis-by-axis basis you may either
control the existing locator via:
ax.xaxis.get_major_locator().set_params(nbins=9, steps=[1, 2, 5, 10])
or create a new MaxNLocator
:
import matplotlib.ticker as mticker
ax.set_major_locator(mticker.MaxNLocator(nbins=9, steps=[1, 2, 5, 10])
The algorithm used by MaxNLocator
has been
improved, and this may change the choice of tick locations in some
cases. This also affects AutoLocator
, which
uses MaxNLocator
internally.
For a log-scaled axis the default locator is the
LogLocator
. Previously the maximum number
of ticks was set to 15, and could not be changed. Now there is a
numticks kwarg for setting the maximum to any integer value,
to the string 'auto', or to its default value of None which is
equivalent to 'auto'. With the 'auto' setting the maximum number
will be no larger than 9, and will be reduced depending on the
length of the axis in units of the tick font size. As in the
case of the AutoLocator, the heuristic algorithm reduces the
incidence of overlapping tick labels but does not prevent it.
Tick label formatting#
LogFormatter
labeling of minor ticks#
Minor ticks on a log axis are now labeled when the axis view limits
span a range less than or equal to the interval between two major
ticks. See LogFormatter
for details. The
minor tick labeling is turned off when using mpl.style.use('classic')
,
but cannot be controlled independently via rcParams
.
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ScalarFormatter
tick label formatting with offsets#
With the default rcParams["axes.formatter.useoffset"]
(default: True
),
an offset will be used when it will save 4 or more digits. This can
be controlled with the new rcParams["axes.formatter.offset_threshold"]
(default: 4
).
To restore the previous behavior of using an offset to save 2 or more
digits, use rcParams['axes.formatter.offset_threshold'] = 2
.
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AutoDateFormatter
format strings#
The default date formats are now all based on ISO format, i.e., with
the slowest-moving value first. The date formatters are
configurable through the date.autoformatter.*
rcParams.
Threshold (tick interval >= than) |
rcParam |
classic |
v2.0 |
---|---|---|---|
365 days |
|
|
|
30 days |
|
|
|
1 day |
|
|
|
1 hour |
|
|
|
1 minute |
|
|
|
1 second |
|
|
|
1 microsecond |
|
|
|
Python's %x
and %X
date formats may be of particular interest
to format dates based on the current locale.
The previous default can be restored by:
mpl.rcParams['date.autoformatter.year'] = '%Y'
mpl.rcParams['date.autoformatter.month'] = '%b %Y'
mpl.rcParams['date.autoformatter.day'] = '%b %d %Y'
mpl.rcParams['date.autoformatter.hour'] = '%H:%M:%S'
mpl.rcParams['date.autoformatter.minute'] = '%H:%M:%S.%f'
mpl.rcParams['date.autoformatter.second'] = '%H:%M:%S.%f'
mpl.rcParams['date.autoformatter.microsecond'] = '%H:%M:%S.%f'
or setting
date.autoformatter.year : %Y
date.autoformatter.month : %b %Y
date.autoformatter.day : %b %d %Y
date.autoformatter.hour : %H:%M:%S
date.autoformatter.minute : %H:%M:%S.%f
date.autoformatter.second : %H:%M:%S.%f
date.autoformatter.microsecond : %H:%M:%S.%f
in your matplotlibrc
file.
mplot3d#
mplot3d now obeys some style-related rcParams, rather than using hard-coded defaults. These include:
xtick.major.width
ytick.major.width
xtick.color
ytick.color
axes.linewidth
axes.edgecolor
grid.color
grid.linewidth
grid.linestyle