Plotting is an essential component of data analysis. As a data scientist, I spend a significant amount of my time making simple plots to understand complex data sets (exploratory data analysis) and help others understand them (presentations).

In particular, I make a lot of bar charts (including histograms), line plots (including time series), scatter plots, and density plots from data in Pandas data frames. I often want to facet these on various categorical variables and layer them on a common grid.

To that end, I made pythonplot.com, a brief introduction to Python plotting libraries and a “rosetta stone” comparing how to use them. I also included comparison to ggplot2, the R plotting library that I and many others consider a gold standard.