There are lots of interesting timeseries data in software systems: events that occur over time such as numbers of users signed up, or errors of a certain type.
I like visualizing these as a counting process, which is a cumulative count of the events over time.
Once upon a time, I would’ve done this in R, but I want to learn pandas so I can do more with IPython notebooks. However, I don’t want to give up R’s excellent ggplot2 library. Fortunately, there’s a Python port.
Here’s an IPython notebook that shows an example of a cumulative plot. The example data I used is the commit history of the OpenStack Neutron project. I suspect there’s a simpler way to do the data manipulation with pandas, but I’m just a beginner with the library.
The plot appears below: