matplotlib is a python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. matplotlib can be used in python scripts, the python and ipython shell (ala matlab or mathematica), web application servers, and six graphical user interface toolkits. matplotlib tries to make easy things easy and hard things possible. You can generate plots, histograms, power spectra, bar charts, errorcharts, scatterplots, etc, with just a few lines of code.
"Sometimes a picture is worth a thousand words. Beginning Python Visualization: Creating Visual Transformation Scripts, published in February 2009 by Apress, shows how Python and its related tools can be used to easily and effectively turn raw data into visual representations that communicate effectively. The author is Shai Vaingast, a professional engineer and engineering manager who needed to train scientists and engineers to do this kind of programming work. He was looking for a tutorial and reference work, and unable to find a suitable text, wound up writing his first book. He writes in the easy and clear style of someone comfortable and engaged with the subject matter."
RadialNet is a network visualization tool developed for Umit during the Google Summer of Code 2007. In Umit it's called UmitMapper. It consists in a graphical tool to illustrate the Nmap network mapping. You can see a video demonstration based on version 0.3 here.
Mail Trends lets you analyze and visualize your email (as extracted from an IMAP server). You can see: * Distribution of messages by year, month, day, day of week and time of day * Distribution of messages by size and your top 40 largest messages * The top senders, recipients and mailing lists you're on. * Distributions of senders, recipients and mailing lists over time * The distribution of thread lengths and the lists and people that result in the longest threads
This library provides Python functions for agglomerative clustering. Its features include * generating hierarchical clusters from distance matrices * computing distance matrices from observation vectors * computing statistics on clusters *