Earlier quoted context omitted.
This is exactly right. What is the REPL story with NIM? Having used a REPL, I cannot even imagine doing research & analytics without one. FWIW, this comparison between R, Pandas and Nim dataframes is quite encouraging: https://gist.github.com/Vindaar/6908c038707c7d8293049edb3d20... This is one of the aspects that self professed R/Python datascience contenders often get wrong. The very bare minimum is a well supported…
Actually, the bare minimum is a well supported and centralised numeric library providing arrays, matrix and the base tools
Most of my work is time series analysis and I refuse to use an environment where samples are not explicitly labelled/timestamped and where the tooling does not support seamless operations that take this labeling into account.
So for my use case, a fully featured dataframe library is indeed a must.