Some simple benchmarks comparing the new backend to pandas at: [https://github.com/Vindaar/ggplotnim/tree/arraymancerBackend/benchmarks/pandas_compare](https://github.com/Vindaar/ggplotnim/tree/arraymancerBackend/benchmarks/pandas_compare)
Note that I ran the code on a default pandas installation on my void linux, without blas. But I also compiled the Nim code without blas support. It's just a port of a pandas / numpy comparison from here: [https://github.com/mm-mansour/Fast-Pandas](https://github.com/mm-mansour/Fast-Pandas) All in all the new backend (let's call it datamancer from now on, heh) is significantly faster for all operations, which essentially just rely on @mratim's work. For a few others, specifically unique and sorting, it's slightly slower. But given the implementation of those I'm actually rather happy with that. And especially for small data frame sizes the function call / looping overhead python has to bear is ridiculous. I'll focus on finishing up the open PR (ridgelines and a bit more) and then finish this.
