TOP 5 Learning pandas

TOP 5 Learning pandas

People at conferences and meetups often ask me what I would recommend to learn X or Y. And I’m always happy to give some suggestions depending on the experience level of the person that asked. Unfortunately, this doesn’t scale very much, so here are my general recommendations on learning something very effective. This time: pandas.

Mining performance hotspots with JProfiler, jQAssistant, Neo4j and Pandas – Part 2: Root Cause Analysis

Mining performance hotspots with JProfiler, jQAssistant, Neo4j and Pandas – Part 2: Root Cause Analysis

All the work before was just there to get a nice graph model that feels more natural. Now comes the analysis part: As mentioned in the introduction, we don’t only want the hotspots that signal that something awkward happened, but also

the trigger in our application of the hotspot combined with
the information about the entry point (e. g. where in our application does the problem happen) and
(optionally) the request that causes the problem (to be able to localize the problem)…

A simple demo on how to use Python Pandas with jQAssistant / Neo4j

A simple demo on how to use Python Pandas with jQAssistant / Neo4j

I’m a huge fan of the software analysis framework jQAssistant. It’s a great tool for scanning and validating various software artifacts. But I also love Python Pandas as a powerful tool in combination with Jupyter notebook for reproducible Software Analytics.

Combining these tools is near at hand. So I’ve created a quick demonstration for “first contact” 🙂