The topology of zeolite frameworks and of associated tetrahedral sites (T-sites) are commonly characterized by their associated rings, typically defined as some set of closed paths or cycles through a framework that cannot be decomposed into shorter cycles. These ring descriptors have been used to identify feasible zeolite topologies, to describe the similarity and differences between zeolites, to identify sites or voids of catalytic relevance, and as machine learning fingerprints. Numerous definitions and algorithms for finding zeolite rings have been proposed and applied throughout the literature. Here we report an analysis of rings and T-sites in a large number of zeolite frameworks using Zeolite Simulation Environment, a Python package that implements an efficient algorithm presented by Goetzke and Klein for finding rings in arbitrary frameworks. We compare the result of a number of common and new ring definitions applied to a large number of common zeolite frameworks. We discover previously unrecognized rings in a number of frameworks. We show that the vertex symbol, a common approach used to characterize T-sites, misses important parts of the stereochemistry around a T-site, and propose an alternative definition. This tool provides an effective platform for characterizing zeolite and T-site structures useful for building models and doing machine learning. We further relate the topology of zeolite frameworks to ion exchange energy siting preferences for various monovalent cations in a number of common frameworks. We show that cation size and ring distortion both influence the cation location in the framework. Finally, we combine information about topology and ion siting preferences to help rationalize experimental findings from our collaborators that show differences in infrared IR spectra between protons associated with isolated and proximal Al atoms in the CHA framework, and that the first-order protolytic propane cracking rate constants in CHA increase with the fraction of Al atoms in pairs.