See also: [https://github.com/nim-lang/needed-libraries/issues/77](https://github.com/nim-lang/needed-libraries/issues/77)
For me I think the pressing needs would be: * A jupyter kernel. It can use hot-code reloading. * Make NimData, NimPlotly and Arraymancer work with jupyter kernel seamlessly. * Being able to delegate to Python ecosystem via nimpy when we are stuck to the respective pandas/numpy * Allowing Python folks to use Nim libraries for performance via nimpy as well * Package all that niftiness in Pip/Anaconda so that we can share the love An example of Nim benefit, I recently made SVD and PCA (Singular Value Decomposition, Principal Component Analysis) at least [2x to up to 10x faster](https://github.com/mratsim/Arraymancer/pull/384#issuecomment-536682906) than Scikit-learn and Facebook's fbpca. It also uses 2x less memory, which is quite useful given the size of current datasets.
