Explain the difference between multithreading and multiprocessing in Python.

Python’s Global Interpreter Lock (GIL) allows only one thread to execute Python bytecode at a time, which limits true parallelism for CPU‑bound tasks. Multithreading is suitable for I/O‑bound workloads (e.g., network requests, file I/O) where threads spend most time waiting, because the GIL is released during I/O operations. Multiprocessing creates separate processes, each with its own Python interpreter and memory space, bypassing the GIL and enabling true parallel execution on multiple CPU cores. However, processes incur higher overhead for creation and inter‑process communication (IPC). Use multithreading when tasks are I/O‑heavy and memory sharing is needed; use multiprocessing for CPU‑intensive computations or when you need to fully utilize multiple cores. Python’s concurrent.futures module provides ThreadPoolExecutor and ProcessPoolExecutor to abstract these differences.