Profiling: Measuring What Matters

Theory tells you the growth rate. Profiling tells you the actual bottleneck. An O(n) algorithm with a large constant factor can be slower in practice than an O(n²) algorithm with a tiny constant factor for inputs under 10,000. Cache behavior, memory allocation patterns, and interpreter overhead all create gaps between theoretical predictions and measured performance. Profiling bridges this gap. timeit: Precise Microbenchmarks cProfile: Finding the Real Bottleneck While timeit measures specific snippets, cProfile measures an entire program and reports which functions consumed the most time. This is essential for real applications where the slow code is not always where you expect. The Pareto principle applies: typically 5% of your code accounts for 95% of the execution time. Empirical Compl