I’ve been using matplotlib for nearly 5 years at least once a week. I’m still learning things that exist within the pylab interface… not the most ideal UX. For instance, I just learned about plt.axvline, which I could use to draw vertical lines in my code instead of what I usually use, plt.vlines(coordinate_list, *plt.gca().get_ylim()), but it’s not as general as plt.vlines since it actually plots a rectangle. Still, though, for most of what I do (which is a single vertical line for drawing specific axes/time breaks in a plot) it’s easier.

I found this function super useful in my dissertation and more generally in my work. What it does is take x,y coordinates and a set of strings and annotates a scatterplot using those labels. For example, here’s a figure from my dissertation where I use it to annotate a plot of regression leverage by year. I jitter the points a little to provide legibility to the text labels, but basically it’s just a call like annoscatter(df.

I looked into using spatial autocorrelation functions in my dissertation to characterize the ``scale’’ at which processes operate electorally. I did an analysis of presidential vote by county, trying to identify where, exactly, clusters of votes tend to become decorrelated. The typical diameter at which the so-called “spatial autocorrelation function” goes to zero denotes how wide a typical spatial cluster might be, and the partial spatial autocorrelation function gives an anticipated order at which spatial autocorrelation may hold.

All of my old posts are still live on Yet another geographer (at least until Tumblr decides to cave in on itself!) I may continue to update there with smaller reflections.