In recent years, Unmanned Aerial Vehicles (UAVs) have emerged as effective tools for traffic monitoring and control by offering high-resolution, aerial observations of vehicular movement. Although UAV simulation is well established, tools to capture microscopic traffic measurements from UAV-based observations remain limited. This paper introduces SUMO-UAV-Py, an open-source SUMO plugin that integrates UAV-based sensing into microscopic traffic simulations in Python. SUMO-UAV-Py captures detailed vehicle observations by dynamically employing multiple UAVs to observe traffic measurements based on their position and field-of-view (FoV). Performance evaluations on a mid-sized network demonstrate that SUMO-UAV-Py maintains simulation performance comparable to standard post-processing methods, confirming its suitability for large-scale traffic monitoring research.