Once I have found that the code is taking a long time, what strategies would I use to reduce the runtime?

When a Python program is slow, start by identifying the hot spots with a profiler such as cProfile or line_profiler. Once you know which functions consume most time, consider algorithmic improvements: replace O(n²) loops with O(n log n) sorting, use hash tables for O(1) lookups, or switch to generators to reduce memory overhead. Data structures matter; a list for frequent insertions is slower than a deque or linked list. If the work is CPU‑bound, parallelize with multiprocessing or use libraries like NumPy that run in compiled C. For I/O‑bound tasks, use asyncio or threading to overlap waiting. Caching results with functools.lru_cache can avoid recomputation. Finally, ensure you’re not doing unnecessary work inside tight loops—pre‑compute constants, avoid repeated attribute lookups, and use local variable references. Each of these steps can reduce runtime from seconds to milliseconds, depending on the bottleneck.