9 nifty libraries for profiling Python code

Every programming language has two kinds of speed: speed of development, and speed of execution. Python has always favored writing fast versus running fast. Although Python code is almost always fast enough for the task, sometimes it isn’t. In those cases, you need to find out where and why it lags, and do something about it.

A well-respected adage of software development, and engineering generally, is “Measure, don’t guess.” With software, it’s easy to assume what’s wrong, but never a good idea to do so. Statistics about actual program performance are always your best first tool in the pursuit of making applications faster.

The good news is, Python offers a whole slew of packages you can use to profile your applications and learn where it’s slowest. These tools range from simple one-liners included with the standard library to sophisticated frameworks for gathering stats from running applications. Here I cover nine of the most significant, most of which run cross-platform and are readily available either in PyPI or in Python’s standard library.

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