You're not finished with a programming task when you're done writing the code: you're finished when your code is running correctly and with acceptable performance. Testing means verifying that your code is running correctly by exercising the code under known conditions and checking that the results are as expected. Debugging means discovering the causes of incorrect behavior and removing them (the removal is often easy once you have figured out the causes).
Optimizing is often used as an umbrella term for activities meant to ensure acceptable performance. Optimizing breaks down into benchmarking (measuring performance for given tasks and checking that it's within acceptable bounds), profiling (instrumenting the program to find out what parts are performance bottlenecks), and optimizing proper (removing bottlenecks to make overall program performance acceptable). Clearly, you can't remove performance bottlenecks until you've found out where they are (using profiling), which in turn requires knowing that there are performance problems (using benchmarking).
All of these tasks are large and important, and each could fill a book by itself. This chapter does not explore every related technique and implication; it focuses on Python-specific techniques, approaches, and tools.