Teetool is a Python package which models and visualises motion patterns found in two- and three-dimensional trajectory data. It models the trajectories as a Gaussian process and uses the mean and covariance of the trajectory data to produce a confidence region, an area (or volume) through which a given percentage of trajectories travel. The confidence region is useful in obtaining an understanding of, or quantifying, dispersion in trajectory data. Furthermore, by modelling the trajectories as a Gaussian process, missing data can be recovered and noisy measurements can be corrected. Teetool is available as a Python package on GitHub, and includes Jupyter Notebooks, showing examples for two- and three-dimensional trajectory data. Funding statement: The authors gratefully acknowledge the funding provided under research grant EP/L505067/1 from the Engineering and Physical Sciences Research Council and Cunning Running Software Ltd. The research data and code generated as part of this study are openly available at https://doi.org/10.5281/zenodo.251481.