Track 16 · Lesson 3 of 12

Measuring the cost of code

Count the work an algorithm does, describe how it grows with Big-O, and time code honestly.

You will be able to

• Count the basic operations a function performs for inputs of different sizes
• Describe growth as O(1), O(log n), O(n), O(n log n) or O(n²) and recognise each in code
• Spot hidden loops such as `in` on a list, slicing and `sum()` inside a loop
• Time code with time.perf_counter and timeit, and interpret the numbers carefully