Python and Go handle concurrency in fundamentally different ways. Python uses threads and multiprocessing to manage concurrent tasks. Due to the Global Interpreter Lock (GIL), threads in Python don't achieve true parallelism, limiting their effectiveness for CPU-bound tasks. To overcome this, Python offers the multiprocessing module, which spawns separate processes to bypass the GIL, allowing better CPU utilization. For I/O-bound tasks, Python also supports asyncio, which facilitates asynchronous programming by running tasks cooperatively rather than in parallel.

Go, on the other hand, is designed with concurrency as a core feature. It uses goroutines, which are lightweight, managed by the Go runtime, and can handle thousands of concurrent tasks without the heavy overhead of traditional threads. Goroutines communicate using channels, a built-in feature that facilitates safe data exchange between tasks, making concurrency in Go simpler and more efficient. Unlike Python, Go can handle both CPU-bound and I/O-bound tasks effectively in parallel, thanks to its design philosophy.

In summary, Go’s concurrency model is more robust for large-scale concurrent operations, while Python's concurrency is often more complex and less efficient for parallel processing. If you’re looking to improve your Python skills, a Python certification course can help you master these concepts and more.