Profiling in Python with Pyroscope's pip package is demonstrated through a ride-sharing app simulation, highlighting the package's ability to aid in performance monitoring and debugging by tagging data meaningfully. The simulation involves endpoints for ordering different vehicles and operates across three servers in various regions. Pyroscope's tagging feature allows users to distinguish data by region and vehicle, facilitating the identification of performance bottlenecks, as illustrated with the order_car function in the us-west-1 region. The package's comparison view enables users to analyze flame graphs from different time periods, revealing variations in CPU resource usage, particularly in the mutex_lock function. The post emphasizes continuous profiling as a key aspect of observability and seeks feedback for future enhancements, including integrations and memory profiling.