CPU-bound tasks: ProcessPoolExecutor will usually outperform ThreadPoolExecutor because it bypasses the GIL, allowing full parallelism on multiple CPU cores.

ThreadPoolExecutor will typically be slower for CPU-bound tasks because of the GIL, which limits the execution of Python code to one thread at a time in a single process. I/O-bound tasks:

ThreadPoolExecutor is typically faster because threads can run concurrently, and since I/O tasks spend most of their time waiting (e.g., for network responses), this doesn't affect performance significantly. ProcessPoolExecutor will be slower for I/O tasks due to the overhead of creating and managing separate processes, which is unnecessary when the tasks spend most of their time waiting.

Key Takeaways: CPU-bound tasks: Prefer ProcessPoolExecutor for parallelizing CPU-intensive operations. I/O-bound tasks: Prefer ThreadPoolExecutor for operations that involve waiting, such as web scraping or network requests.