Several external packages that extend or build on Matplotlib functionality exist. Below we list a number of these. Note that they are each maintained and distributed separately from Matplotlib, and will need to be installed individually.
Please submit an issue or pull request on Github if you have created a package that you would like to have included. We are also happy to host third party packages within the Matplotlib Github Organization.
Several projects provide higher-level interfaces for creating matplotlib plots.
seaborn is a high level interface for drawing statistical graphics with matplotlib. It aims to make visualization a central part of exploring and understanding complex datasets.
holoviews makes it easier to visualize data interactively, especially in a Jupyter notebook, by providing a set of declarative plotting objects that store your data and associated metadata. Your data is then immediately visualizable alongside or overlaid with other data, either statically or with automatically provided widgets for parameter exploration.
Two independent mapping toolkits are available.
Plots data on map projections, with continental and political boundaries. See basemap docs.
Cartopy builds on top of matplotlib to provide object oriented map projection definitions and close integration with Shapely for powerful yet easy-to-use vector data processing tools. An example plot from the Cartopy gallery:
mpl-probscale
is a small extension that allows matplotlib users to specify probabilty
scales. Simply importing the probscale module registers the scale
with matplotlib, making it accessible via e.g.,
ax.set_xscale('prob') or plt.yscale('prob').
matplotlib_iterm2 is an external matplotlib backend using iTerm2 nightly build inline image display feature.
MplDataCursor is a toolkit written by Joe Kington to provide interactive “data cursors” (clickable annotation boxes) for matplotlib.
mplcursors provides interactive data cursors for matplotlib.
mpl_toolkits.natgrid is an interface to natgrid C library for gridding irregularly spaced data. This requires a separate installation of the natgrid toolkit.
Matplotlib-Venn provides a set of functions for plotting 2- and 3-set area-weighted (or unweighted) Venn diagrams.
mplstereonet provides stereonets for plotting and analyzing orientation data in Matplotlib.
pyUpSet is a static Python implementation of the UpSet suite by Lex et al. to explore complex intersections of sets and data frames.
