Multithreading issues in Python often stem from the Global Interpreter Lock (GIL). Use the `threading` module wisely, and consider using `multiprocessing` for CPU-bound tasks to avoid GIL limitations.

Handling multithreading in Python can be complex, largely due to the Global Interpreter Lock (GIL), which prevents multiple native threads from executing Python bytecodes simultaneously. This means that Python's multithreading is more suitable for I/O-bound tasks rather than CPU-bound tasks. To manage multithreading issues effectively, utilize the threading module to create and manage threads. When working with I/O-bound tasks, such as network calls or file operations, threading can improve responsiveness and performance. However, for CPU-bound tasks where you need to fully utilize multiple cores, consider using the multiprocessing module, which spawns separate processes that bypass the GIL. When using threads, be cautious of race conditions and data corruption; employ thread synchronization techniques, such as Locks or Queues, to manage access to shared resources. Debugging multithreading issues can also be tricky, so ensure you have proper logging in place to trace execution paths. By understanding the implications of the GIL and using the appropriate concurrency model, you can effectively handle multithreading issues in your Python applications.