We present SVVAMP, a Python package dedicated to the study of voting systems with an emphasis on manipulation analysis. Voters' preferences can be imported from external files or generated by a variety of probabilistic models. SVVAMP currently implements more than 20 voting systems, and its object-oriented design facilitates the implementation of new voting systems. Special attention has been paid to Coalitional Manipulability (CM) and its variants. Algorithms for Condorcet efficiency, Individual Manipulability (IM) and Independence of Irrelevant Alternatives (IIA) are also implemented.