matplotlib is a 2D graphics package that produces plots from python scripts, the python shell, or embeds them in your favorite python GUI -- wx, gtk, tk, fltk and qt. Unlike many python plotting alternatives is written in python, so it is easy to extend. matplotlib is used in the finance industry, web application servers, and many scientific and enginneering disciplines. With a large community of users and developers, matplotlib is approaching the goal of having a full featured, high quality, 2D plotting library for python. A lot of development has gone into matplotlib since the last major release, which I'll summarize here. For details, see the notes for the incremental releases at http://matplotlib.sf.net/whats_new.html. Improvements since 0.70 -- contouring: Lots of new contour funcitonality with line and polygon contours provided by contour and contourf. Automatic inline contour labeling with clabel. See http://matplotlib.sourceforge.net/screenshots.html#pcolor_demo -- QT backend Sigve Tjoraand, Ted Drain and colleagues at the JPL collaborated on a QTAgg backend -- Unicode strings are rendered in the agg and postscript backends. Currently, all the symbols in the unicode string have to be in the active font file. In later releases we'll try and support symbols from multiple ttf files in one string. See examples/unicode_demo.py -- map and projections A new release of the basemap toolkit - See http://matplotlib.sourceforge.net/screenshots.html#plotmap -- Auto-legends The automatic placement of legends is now supported with loc='best'; see examples/legend_auto.py. We did this at the matplotlib sprint at pycon -- Thanks John Gill and Phil! Note that your legend will move if you interact with your data and you force data under the legend line. If this is not what you want, use a designated location code. -- Quiver (direction fields) Ludovic Aubry contributed a patch for the matlab compatible quiver method. This makes a direction field with arrows. See examples/quiver_demo.py -- Performance optimizations Substantial optimizations in line marker drawing in agg -- Robust log plots Lots of work making log plots "just work". You can toggle log y Axes with the 'l' command -- nonpositive data are simply ignored and no longer raise exceptions. log plots should be a lot faster and more robust -- Many more plotting functions, bugfixes, and features, detailed in the 0.71, 0.72, 0.73 and 0.74 point release notes at http://matplotlib.sourceforge.net/whats_new.html http://matplotlib.sourceforge.net JDH