Table Of Contents

This Page

New in matplotlib 1.4

Thomas A. Caswell served as the release manager for the 1.4 release.


matplotlib 1.4 supports Python 2.6, 2.7, 3.3, and 3.4

New colormap

In heatmaps, a green-to-red spectrum is often used to indicate intensity of activity, but this can be problematic for the red/green colorblind. A new, colorblind-friendly colormap is now available at This colormap maintains the red/green symbolism while achieving deuteranopic legibility through brightness variations. See here for more information.

The nbagg backend

Phil Elson added a new backend, named “nbagg”, which enables interactive figures in a live IPython notebook session. The backend makes use of the infrastructure developed for the webagg backend, which itself gives standalone server backed interactive figures in the browser, however nbagg does not require a dedicated matplotlib server as all communications are handled through the IPython Comm machinery.

As with other backends nbagg can be enabled inside the IPython notebook with:

import matplotlib

Once figures are created and then subsequently shown, they will placed in an interactive widget inside the notebook allowing panning and zooming in the same way as any other matplotlib backend. Because figures require a connection to the IPython notebook server for their interactivity, once the notebook is saved, each figure will be rendered as a static image - thus allowing non-interactive viewing of figures on services such as nbviewer.

New plotting features

Power-law normalization

Ben Gamari added a power-law normalization method, PowerNorm. This class maps a range of values to the interval [0,1] with power-law scaling with the exponent provided by the constructor’s gamma argument. Power law normalization can be useful for, e.g., emphasizing small populations in a histogram.

Fully customizable boxplots

Paul Hobson overhauled the boxplot() method such that it is now completely customizable in terms of the styles and positions of the individual artists. Under the hood, boxplot() relies on a new function (boxplot_stats()), which accepts any data structure currently compatible with boxplot(), and returns a list of dictionaries containing the positions for each element of the boxplots. Then a second method, bxp() is called to draw the boxplots based on the stats.

The boxplot() function can be used as before to generate boxplots from data in one step. But now the user has the flexibility to generate the statistics independently, or to modify the output of boxplot_stats() prior to plotting with bxp().

Lastly, each artist (e.g., the box, outliers, cap, notches) can now be toggled on or off and their styles can be passed in through individual kwargs. See the examples: statistics example code: and statistics example code:

Added a bool kwarg, manage_xticks, which if False disables the management of the ticks and limits on the x-axis by bxp().

Support for datetime axes in 2d plots

Andrew Dawson added support for datetime axes to contour(), contourf(), pcolormesh() and pcolor().

Support for additional spectrum types

Todd Jennings added support for new types of frequency spectrum plots: magnitude_spectrum(), phase_spectrum(), and angle_spectrum(), as well as corresponding functions in mlab.

He also added these spectrum types to specgram(), as well as adding support for linear scaling there (in addition to the existing dB scaling). Support for additional spectrum types was also added to specgram().

He also increased the performance for all of these functions and plot types.

Support for detrending and windowing 2D arrays in mlab

Todd Jennings added support for 2D arrays in the detrend_mean(), detrend_none(), and detrend(), as well as adding apply_window() which support windowing 2D arrays.

Support for strides in mlab

Todd Jennings added some functions to mlab to make it easier to use numpy strides to create memory-efficient 2D arrays. This includes stride_repeat(), which repeats an array to create a 2D array, and stride_windows(), which uses a moving window to create a 2D array from a 1D array.

Formatter for new-style formatting strings

Added FormatStrFormatterNewStyle which does the same job as FormatStrFormatter, but accepts new-style formatting strings instead of printf-style formatting strings

Consistent grid sizes in streamplots

streamplot() uses a base grid size of 30x30 for both density=1 and density=(1, 1). Previously a grid size of 30x30 was used for density=1, but a grid size of 25x25 was used for density=(1, 1).

Get a list of all tick labels (major and minor)

Added the kwarg ‘which’ to get_xticklabels(), get_yticklabels() and get_ticklabels(). ‘which’ can be ‘major’, ‘minor’, or ‘both’ select which ticks to return, like set_ticks_position(). If ‘which’ is None then the old behaviour (controlled by the bool minor).

Separate horizontal/vertical axes padding support in ImageGrid

The kwarg ‘axes_pad’ to mpl_toolkits.axes_grid1.ImageGrid can now be a tuple if separate horizontal/vertical padding is needed. This is supposed to be very helpful when you have a labelled legend next to every subplot and you need to make some space for legend’s labels.

Support for skewed transformations

The Affine2D gained additional methods skew and skew_deg to create skewed transformations. Additionally, matplotlib internals were cleaned up to support using such transforms in Axes. This transform is important for some plot types, specifically the Skew-T used in meteorology.

(Source code, png, pdf)


Support for specifying properties of wedge and text in pie charts.

Added the kwargs ‘wedgeprops’ and ‘textprops’ to pie() to accept properties for wedge and text objects in a pie. For example, one can specify wedgeprops = {‘linewidth’:3} to specify the width of the borders of the wedges in the pie. For more properties that the user can specify, look at the docs for the wedge and text objects.

Fixed the direction of errorbar upper/lower limits

Larry Bradley fixed the errorbar() method such that the upper and lower limits (lolims, uplims, xlolims, xuplims) now point in the correct direction.

More consistent add-object API for Axes

Added the Axes method add_image to put image handling on a par with artists, collections, containers, lines, patches, and tables.

Violin Plots

Per Parker, Gregory Kelsie, Adam Ortiz, Kevin Chan, Geoffrey Lee, Deokjae Donald Seo, and Taesu Terry Lim added a basic implementation for violin plots. Violin plots can be used to represent the distribution of sample data. They are similar to box plots, but use a kernel density estimation function to present a smooth approximation of the data sample used. The added features are:

violin() - Renders a violin plot from a collection of statistics. violin_stats() - Produces a collection of statistics suitable for rendering a violin plot. violinplot() - Creates a violin plot from a set of sample data. This method makes use of violin_stats() to process the input data, and violin_stats() to do the actual rendering. Users are also free to modify or replace the output of violin_stats() in order to customize the violin plots to their liking.

This feature was implemented for a software engineering course at the University of Toronto, Scarborough, run in Winter 2014 by Anya Tafliovich.

More markevery options to show only a subset of markers

Rohan Walker extended the markevery property in Line2D. You can now specify a subset of markers to show with an int, slice object, numpy fancy indexing, or float. Using a float shows markers at approximately equal display-coordinate-distances along the line.

Fixed the mouse coordinates giving the wrong theta value in Polar graph

Added code to transform_non_affine() to ensure that the calculated theta value was between the range of 0 and 2 * pi since the problem was that the value can become negative after applying the direction and rotation to the theta calculation.

Simple quiver plot for mplot3d toolkit

A team of students in an Engineering Large Software Systems course, taught by Prof. Anya Tafliovich at the University of Toronto, implemented a simple version of a quiver plot in 3D space for the mplot3d toolkit as one of their term project. This feature is documented in quiver(). The team members are: Ryan Steve D’Souza, Victor B, xbtsw, Yang Wang, David, Caradec Bisesar and Vlad Vassilovski.

(Source code, png, pdf)


polar-plot r-tick locations

Added the ability to control the angular position of the r-tick labels on a polar plot via set_rlabel_position().

Date handling

n-d array support for date conversion

Andrew Dawson added support for n-d array handling to matplotlib.dates.num2date(), matplotlib.dates.date2num() and matplotlib.dates.datestr2num(). Support is also added to the unit conversion interfaces matplotlib.dates.DateConverter and matplotlib.units.Registry.

Configuration (rcParams)

savefig.transparent added

Controls whether figures are saved with a transparent background by default. Previously savefig always defaulted to a non-transparent background.


Added rcParam to control the weight of the title

axes.formatter.useoffset added

Controls the default value of useOffset in ScalarFormatter. If True and the data range is much smaller than the data average, then an offset will be determined such that the tick labels are meaningful. If False then the full number will be formatted in all conditions.

nbagg.transparent added

Controls whether nbagg figures have a transparent background. nbagg.transparent is True by default.

XDG compliance

Matplotlib now looks for configuration files (both rcparams and style) in XDG compliant locations.

style package added

You can now easily switch between different styles using the new style package:

>>> from matplotlib import style
>>> style.use('dark_background')

Subsequent plots will use updated colors, sizes, etc. To list all available styles, use:

>>> print style.available

You can add your own custom <style name>.mplstyle files to ~/.matplotlib/stylelib or call use with a URL pointing to a file with matplotlibrc settings.

Note that this is an experimental feature, and the interface may change as users test out this new feature.


Qt5 backend

Martin Fitzpatrick and Tom Badran implemented a Qt5 backend. The differences in namespace locations between Qt4 and Qt5 was dealt with by shimming Qt4 to look like Qt5, thus the Qt5 implementation is the primary implementation. Backwards compatibility for Qt4 is maintained by wrapping the Qt5 implementation.

The Qt5Agg backend currently does not work with IPython’s %matplotlib magic.

The 1.4.0 release has a known bug where the toolbar is broken. This can be fixed by:

cd path/to/installed/matplotlib
# unix2dos 3322.diff (if on windows to fix line endings)
patch -p2 < 3322.diff

Qt4 backend

Rudolf Höfler changed the appearance of the subplottool. All sliders are vertically arranged now, buttons for tight layout and reset were added. Furthermore, the subplottool is now implemented as a modal dialog. It was previously a QMainWindow, leaving the SPT open if one closed the plot window.

In the figure options dialog one can now choose to (re-)generate a simple automatic legend. Any explicitly set legend entries will be lost, but changes to the curves’ label, linestyle, et cetera will now be updated in the legend.

Interactive performance of the Qt4 backend has been dramatically improved under windows.

The mapping of key-signals from Qt to values matplotlib understands was greatly improved (For both Qt4 and Qt5).

Cairo backends

The Cairo backends are now able to use the cairocffi bindings which are more actively maintained than the pycairo bindings.

Gtk3Agg backend

The Gtk3Agg backend now works on Python 3.x, if the cairocffi bindings are installed.

PDF backend

Added context manager for saving to multi-page PDFs.


Text URLs supported by SVG backend

The svg backend will now render Text objects’ url as a link in output SVGs. This allows one to make clickable text in saved figures using the url kwarg of the Text class.

Anchored sizebar font

Added the fontproperties kwarg to AnchoredSizeBar to control the font properties.

Sphinx extensions

The :context: directive in the plot_directive Sphinx extension can now accept an optional reset setting, which will cause the context to be reset. This allows more than one distinct context to be present in documentation. To enable this option, use :context: reset instead of :context: any time you want to reset the context.

Legend and PathEffects documentation

The Legend guide and Path effects guide have both been updated to better reflect the full potential of each of these powerful features.


Span Selector

Added an option span_stays to the SpanSelector which makes the selector rectangle stay on the axes after you release the mouse.

GAE integration

Matplotlib will now run on google app engine.