05-02, 11:00–11:25 (Asia/Jerusalem), PyData Track 1
This talk will review some of the most common pitfalls that can cause otherwise perfectly good Pandas code to grind to be too slow for any time-sensitive applications, and walk through a set of tips and tricks to avoid them.
Writing performant pandas code is not an easy task, in this talk, I will explain how to find the bottlenecks and how to write proper code with computational efficiency, and memory optimization in mind.
English
Target audience –Data Scientists, Other (please specify below)
Other (target audience) –Data engineer / BI / Analyst
Enthusiastic Software Engineer👷
Who appreciates good software engineering 🙏
I have a big passion for Python 🐍, Machine Learning 🤖 , and Performance Optimisations🦸