{"id":1428,"date":"2014-03-04T21:02:22","date_gmt":"2014-03-05T02:02:22","guid":{"rendered":"http:\/\/unitstep.net\/?p=1428"},"modified":"2014-03-21T21:06:34","modified_gmt":"2014-03-22T02:06:34","slug":"analysis-of-the-2013-chicago-marathon-results","status":"publish","type":"post","link":"https:\/\/unitstep.net\/blog\/2014\/03\/04\/analysis-of-the-2013-chicago-marathon-results\/","title":{"rendered":"Analysis of the 2013 Chicago Marathon results"},"content":{"rendered":"

With close to 39,000 results, the 2013 Chicago Marathon Results<\/a> combine two of my favourite topics, statistics and running. I decided to take this opportunity to learn more about pandas<\/a> by using it to analyze the result set to provide some insight into how people run marathons. (I myself ran this race)<\/p>\n

The result of my work is in a GitHub repo<\/a> and published as an IPython Notebook<\/a>. I’ve extracted some of the more interesting parts.<\/p>\n

<\/p>\n

Mundane numbers<\/h2>\n