Exploratory data analysis (EDA) isn’t just for data scientists. Anyone that uses a system that emits data can benefit from the tools of EDA. And since charity begins at home, what better way to motivate this than a short post using DataHaskell tools to analyse GHC profiling logs.

For developers integrating Haskell into data science workflows or interactive documentation, the Jupyter notebook is the standard interface. Currently, there are two primary ways to run Haskell in Jupyter: IHaskell and xeus-haskell.

We’re rebooting dataHaskell! We’ve collected learnings from the previous dataHaskell effort and decided to revive the effort with a simple promise: make doing data science and machine learning in Haskell feel welcoming, practical, and fast. We’ve setup an ambitious roadmap that we are excited to iterate on in the next two years.