Concurrency and parallelism are essential concepts in Python programming that enhance the efficiency of applications. Concurrency allows multiple tasks to make progress independently, even if they don’t run simultaneously, enabling better resource utilization. In contrast, parallelism involves executing multiple tasks at the same time across different processors or cores, significantly speeding up processing times. Python provides various tools for implementing these concepts, including multithreading, multiprocessing, and asyncio. Understanding how to leverage concurrency and parallelism can lead to more responsive and faster applications, making it a crucial skill for developers working with data-intensive tasks or real-time systems.