Concurrency in Python allows handling multiple tasks at once, with multithreading and multiprocessing being two primary approaches. Multithreading enables multiple threads within the same process to share memory, making it efficient for I/O-bound tasks like network requests and file operations. However, Python’s Global Interpreter Lock (GIL) prevents multiple threads from executing Python bytecode simultaneously, limiting its effectiveness for CPU-bound tasks. Multiprocessing, on the other hand, creates separate processes, each with its own memory space, enabling true parallel execution. This makes it ideal for CPU-intensive workloads like complex calculations but comes with higher memory usage and inter-process communication (IPC) overhead. Multithreading improves efficiency for I/O-bound tasks by allowing multiple threads to run concurrently within the same process. For example, when downloading multiple images, multithreading reduces overall waiting time by executing tasks in parallel. However, due to the GIL, threads must take turns executing Python bytecode, making multithreading inefficient for CPU-intensive tasks. In contrast, multiprocessing bypasses the GIL by creating multiple independent processes, each with its own memory space. This approach fully utilizes multiple CPU cores, making it effective for CPU-bound tasks like heavy computations. However, multiprocessing has drawbacks, including higher memory consumption due to process duplication and the complexity of inter-process communication (IPC) using tools like queues or sockets. This overhead makes it less efficient than multithreading for I/O-bound workloads.