Potential intensity (PI) is the maximum speed limit of a tropical cyclone found by treating the storm as a thermal heat engine. Because there are significant correlations between PI and actual storm wind speeds, PI is a useful diagnostic for evaluating or predicting tropical cyclone intensity climatology and variability. Given a set of atmospheric and oceanographic conditions, one may calculate PI following an algorithm described in Bister and Emanuel (2002). The algorithm was originally hard-coded in FORTRAN and then MATLAB; in 2020 the PI code was translated for Python and carefully documented for the first time. Here I describe and demonstrate the new pyPI package (https://github.com/dgilford/pyPI). The goals of pyPI are to: (1) supply a freely available validated Python potential intensity calculator, (2) carefully document the PI algorithm and its Python implementation, and (3) to demonstrate and encourage the use of potential intensity theory in tropical cyclone analyses. In this presentation I discuss the Python implementation of the PI algorithm and I show examples which use pyPI in studies of climatological tropical cyclone intensity. I consider the potential for future improvements in pyPI and ask for feedback/suggestions from the broader climate data science community.