Every programmer can expect to eventually encounter problems with program performance (be it memory usage or execution time), and Python programmers are no exception. Python has its own particular performance issues and methods of identifying and eliminating them. This practically oriented tutorial will examine these methods in detail, according to the following outline:

1. Using Python's built-in performance measurement tools to find and quantify performance problems.

2. How to avoid common Python performance pitfalls and effective (and ineffective) optimization techniques. This section will also include a discussion of third-party Python libraries often used in situations where high performance is required.

3. Using tools outside of the standard language (e.g., Pyrex, Psyco) to solve performance problems that would otherwise be intractable.

This tutorial is aimed at people with beginning-to-intermediate Python knowledge, but even experts may garner some useful tidbits of information.