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Python multithreading enables concurrent execution of tasks within a single process by using the threading module. The Global Interpreter Lock (GIL) limits true parallelism for CPU-bound tasks, making threads most effective for I/O-bound operations like network requests or file handling. Key concepts include creating threads with Thread class or ThreadPoolExecutor, managing thread synchronization with locks and semaphores, and understanding daemon threads. Best practices involve using context managers for locks, avoiding shared state when possible, and considering multiprocessing for CPU-intensive workloads.

The Most Practical Intro to Python Multithreading