I've been munging a bunch of data for a while. Learning to processes, etc. Here's a little something I put together after deciding I wanted to have something to show. Using Quandl as a data source I was able to grab monthly average housing prices by zip code. By comparing this to data from the Minnesota Legislature's website - I was able to plot the Average Percent Change of Housing Prices per Congressional District.
This was an excellent project to get into pandas, a requisite for deep analytics. Also, matplotlib, being another prominent tool; it also uses ggplot, just like R. Python ever expanding libraries are the impetus behind moving on from R with regards to statistical analysis. Before endeavoring to complete this small side-quest, I dug into NLTK as well. This is where the optimization, and extensibility of Python really come into play.