Motivation:Binding pocket volumes are a simple yet important predictor of small molecule binding; however, generating visualizations of pocket topology and performing meaningful volume comparisons can be difficult with available tools. Current programs for accurate volume determination rely on extensive user input to define bulk solvent boundaries and to partition cavities into subpockets, increasing inter-user variability in measurements as well as time demands. Results: We developed PyVOL, a python package with a PyMOL interface and GUI, to visualize, to characterize, and to compare binding pockets. PyVOL's pocket identification algorithm is designed to maximize reproducibility through minimization of user-provided parameters, avoidance of grid-based methods, and automated subpocket identification. This approach permits efficient, scalable volume calculations. Availability: PyVOL is released under the MIT License. Source code and documentation are available through github (https://github.com/schlessingerlab/pyvol/) with distribution through PyPI