Power grids are facing growing challenges, arising from the increasing share of renewable energy sources and highly variable loads, introducing significant variability into both power generation and demand. This variability can create transmission line bottlenecks, which in turn lead to overheating. Transmission system operators address this problem by rerouting the power flow via topological control, which is more cost-effective than redispatch measures. While reinforcement learning has shown promise in optimizing such topology changes, existing reinforcement learning environments exhibit notable differences from real-world operations. To support research and bridge the gap to real-world applications, we present PPTopoGym—a reinforcement learning environment for evaluating the impact of topology actions on the power flow in pandapower-based grids. It allows for simulations with variable load and generation profiles, and supports direct computation of key grid security metrics, i.e., \(\text {N}{-}0\) and \(\text {N}{-}1\) security constraints for each time step. PPTopoGym addresses key limitations of existing environments by enabling realistic grid modeling and the integration of real-world data. The code is publicly available.