Speeding Up Your Python Programs with Concurrency

What Is Concurrency? At its core, concurrency means a program can juggle multiple sequences of work. In Python, these sequences go by different names — threads, tasks, and processes — but they all share the same basic idea: each one represents a line of execution that can be paused and resumed. The important distinction is that threads and asynchronous tasks run on a single processor, switching between each other cleverly rather than truly running side by side. Processes, on the other hand, can run on separate CPU cores simultaneously — that's true parallelism. Python offers three main tools for concurrency: I/O-Bound vs CPU-Bound Problems Before choosing a concurrency approach, it's important to understand what kind of problem you're solving. I/O-bound problems are those where your program spends most of its time waiting — for a network response, a file to load, or a database query to return. The CPU sits idle during these waits, so overlapping them with other work can bring big gains