I've found sparse good for many-columns (10K+). However, for graphistry's graph workloads, we do most scipy-size graphs at much higher speeds via cudf + cugraph

I've found sparse good for many-columns (10K+). However, for graphistry's graph workloads, we do most scipy-size graphs at much higher speeds via cudf + cugraph. Similar apis, more speed. Ex: Preprocess many cols with scikit sparse (genomes, many-featured fraud, ...) -> cudf (= gpu dataframes) for most of workload -> infer relationships with gpu UMAP then gpu k-nn -> graphistry for visual analysis. We're starting to experiment with bigger-than-memory (TB etc) scale via the gpu-enabled dask libs. I definitely recommend trying. If interested in adding visual analytics here, these'll be hitting our free + self-hosted layers soon, and drop a contact method if you'd like early access. Exciting times!