Partial rankings are totally ordered subsets of a set of items. They arise in different applications, such as clickstream analysis and collaborative filtering, but can be difficult to analyze with traditional data analysis techniques as they are combinatorial structures. We propose a method for creating scatterplots of sets of partial rankings by first representing them in a high-dimensional space and then applying known dimensionality reduction methods. We compare different approaches by using quantitative measures and demonstrate the methods on real data sets from different application domains. Despite their simplicity the proposed methods can produce useful visualizations that are easy to interpret.