The availability of geo-spatial mobility data (e.g., GPS traces, call detail records) is a trend that will grow in the near future. For this reason, understanding and simulating human mobility is of paramount importance for many present and future applications, such as traffic forecasting, urban planning, and epidemic modeling, and hence for many actors, from urban planners to decision-makers and advertising companies... scikit-mobility is a Python library for mobility analysis and simulation that allows the user to: (1) analyze mobility data by using the main measures characterizing human mobility patterns (e.g., radius of gyration, daily motifs, mobility entropy); (2) simulate individual and collective mobility by executing the most common human mobility models (e.g., gravity and radiation models, exploration and preferential return model); (3) compare all these models by a set of validation metrics taken from the literature. scikit-mobility provides an efficient and easy-to-use implementation, based on the standard Python library numpy, pandas and geopandas, of the main collective and individual human mobility models existing in literature, allowing for both the fitting of the parameters from real data and the running of the models for the generation of synthetic spatio-temporal trajectories. scikit-mobility is a starting point for the development of urban simulation and what-if analysis, e.g., simulating changes in urban mobility after the construction of a new infrastructure or when traumatic events occur like epidemic diffusion, terrorist attacks or international events. (read more)