{"id":1213,"date":"2012-06-07T17:32:06","date_gmt":"2012-06-07T22:32:06","guid":{"rendered":"http:\/\/rud.is\/b\/?p=1213"},"modified":"2018-03-10T07:51:48","modified_gmt":"2018-03-10T12:51:48","slug":"slopegraphs-in-python-log-scales-spam-data-analysis","status":"publish","type":"post","link":"https:\/\/rud.is\/b\/2012\/06\/07\/slopegraphs-in-python-log-scales-spam-data-analysis\/","title":{"rendered":"Slopegraphs in Python \u2013 Log Scales & Spam Data Analysis"},"content":{"rendered":"

Given the focus on actual development of the PySlopegraph tool in most of the blog posts of late, folks may be wondering why an infosec\/inforisk guy is obsessing so much on a tool and not talking security. Besides the fixation on filling a void and promoting an underused visualization tool, I do believe there is a place for slopegraphs in infosec data analysis and will utilize some data from McAfee’s recent Q1 2012 Threat Report<\/span> [PDF] to illustrate how one might use slopegraphs in interpreting the “Spam Volume” data presented in the “Messaging Threats” section (pages 11 & 12 of the report).<\/p>\n

The report shows individual graphs of spam volume per country from April of 2011 through March of 2012. Each individual graph conveys useful information, but I put together two slopegraphs that each show alternate and aggregate views which let you compare spam volume data relative to each country (versus just in-country).<\/p>\n

When first doing this exploration, the scale problem reared it’s ugly head again since the United States is a huge<\/i> spam outlier and causes the chart to be as tall as my youngest son when printed. I really<\/em> wanted to show relative spam volume between countries as well as the increase or decrease between years in one chart and \u2014 after chatting with @maximumyin a bit \u2014 decided to test out using a log scale option for the charting (click for larger image<\/i>):<\/p>\n

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This chart \u2014 Spam Volume by Country \u2014 instantly shows that:<\/p>\n