Finding outliers in a data set

Question

I have a python script that creates a list of lists of server uptime and performance data, where each sub-list (or 'row') contains a particular cluster's stats. For example, nicely formatted it looks something like this:

-------  -------------  ------------  ----------  -------------------
Cluster  %Availability  Requests/Sec  Errors/Sec  %Memory_Utilization
-------  -------------  ------------  ----------  -------------------
ams-a    98.099          1012         678          91
bos-a    98.099          1111         12           91
bos-b    55.123          1513         576          22
lax-a    99.110          988          10           89
pdx-a    98.123          1121         11           90
ord-b    75.005          1301         123          100
sjc-a    99.020          1000         10           88
...(so on)...

So in list form, it might look like:

[[ams-a,98.099,1012,678,91],[bos-a,98.099,1111,12,91],...]

My question: What's the best way to determine the outliers in each column? Or are outliers not necessarily the best way to attack the problem of finding 'badness'? In the data above, I'd definitely want to know about bos-b and ord-b, as well as ams-a since it's error rate is so high, but the others can be discarded. Depending on the column, since higher is not necessarily worse, nor is lower, I'm trying to figure out the most efficient way to do this. Seems like numpy gets mentioned a lot for this sort of stuff, but not sure where to even start with it (sadly, I'm more sysadmin than statistician...).

Thanks in advance!

Answer 1

One good way of identifying outliers visually is to make a boxplot (or box-and-whiskers plot), which will show the median, and a couple of quartiles above and below the median, and the points that lie "far" from this box (see Wikipedia entry http://en.wikipedia.org/wiki/Box_plot). In R, there's a boxplot function to do just that.

One way to discard/identify outliers programmatically is to use the MAD, or Median Absolute Deviation. The MAD is not sensitive to outliers, unlike the standard deviation. I sometimes use a rule of thumb to consider all points that are more than 5*MAD away from the median, to be outliers.