Understanding concurrency and parallelism is key to building efficient programs:

• Concurrency: manages multiple tasks by quickly switching between them, creating the impression they run together. Only one task is active at any instant. Example: one cashier helping several customers by switching between them;
• Parallelism: runs multiple tasks at the exact same time, using multiple processors or cores. Example: several cashiers each serving a different customer simultaneously.

Both approaches help you create programs that handle many tasks smoothly and make the most of modern hardware.

Python offers several ways to achieve concurrency and parallelism:

• Threads: use the threading module to manage concurrent tasks. Threads are ideal for I/O-bound tasks (like reading files or network operations) because they can switch between tasks while waiting.
• Processes: use the multiprocessing module for true parallelism. Each process runs in its own Python interpreter, making it suitable for CPU-bound tasks.

The Global Interpreter Lock (GIL) affects how Python handles concurrency:

• Only one thread can execute Python bytecode at a time, even on multi-core systems.
• The GIL limits parallel execution in threads, so threads do not speed up CPU-bound code.
• The multiprocessing module bypasses the GIL, allowing multiple processes to run in parallel and fully use multiple CPU cores.

Choose threads for I/O-bound tasks and processes for CPU-bound tasks to get the best performance.