Slides and content from my PyCon 2018 talk.
Occasionally we’ll find that some bit of Python we’ve written doesn’t run as fast as we’d like, what can we do? Performance bottlenecks aren’t always intuitive or easy to spot by reading code so we need to collect data with profiling. Once we’ve identified the bottleneck we’ll need to change our approach, but what options are faster than others?
This talk illustrates a Python performance investigation and improvements using an Advent of Code programming challenge. I’ll walk through starting from a slow (but correct) solution, look at profiling data to investigate why it’s slow, and explore multiple paths for improving performance, including more efficient algorithms and using third-party tools like Cython. You’ll leave this talk with a recipe for analyzing Python performance and information about some options for improved performance.
Talk video: https://www.youtube.com/watch?v=yrRqNzJTBjk
Slides here.
Use snakeviz slow_mode.cprof to see the
SnakeViz profile demo'd in the talk.
(Requires Python 3.)