Single cell proteomics (SCP) requires the analysis of dozens to thousands of single human cells to draw biological conclusions. However, assessing of the abundance of single proteins in output data presents a considerable challenge, and no simple universal solutions currently exist. To address this, we developed SCP Viz, a statistical package with a graphical user interface that can handle small and large scale SCP output from any instrument or data processing software. In this software, the abundance of individual proteins can be plotted in a variety of ways, using either unadjusted or normalized outputs. These outputs can also be transformed or imputed within the software. SCP Viz offers a variety of plotting options which can help identify significantly altered proteins between groups, both before and after quantitative transformations. Upon the discovery of subpopulations of single cells, users can easily regroup the cells of interest using straightforward text-based filters. When used in this way, SCP Viz allows users to visualize proteomic heterogeneity at the level of individual proteins, cells, or identified subcellular populations. SCP Viz is compatible with output files from MaxQuant, FragPipe, SpectroNaut, and Proteome Discoverer, and should work equally well with other formats. SCP Viz is publicly available at https://github.com/orsburn/SCPViz. For demonstrations, users can download our test data from GitHub and use an online version that accepts user input for analysis at https://orsburnlab.shinyapps.io/SCPViz/.