What are the best practices for parallelism in Python?

What are the best practices for parallelism in Python?

When using parallelism in Python, consider using the concurrent.futures module for easier thread and process management.

Can you explain the difference between threading and multiprocessing?

Sure! Threading is suitable for I/O-bound tasks, while multiprocessing is better for CPU-bound tasks due to Python's Global Interpreter Lock.

What are some common pitfalls to avoid?

Common pitfalls include not managing shared resources properly, leading to race conditions, and using too many processes, which can cause overhead.

How can I measure the performance of my parallel code?

You can use the time module or the timeit library to measure execution time, and tools like cProfile for profiling your code.