[short_sells_poo]

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 and thought out dataframe library. Without that, the language is basically dead in the water. Nim seems to have a very well thought out API that also avoids many of the annoying aspects of Pandas (e.g. the huge waste coming from eagerly computing each vectorized operation into separate arrays).

[pietroppeter]

I did quench (most of) my thirst for a Repl building a notebook system (plug): https://github.com/pietroppeter/nimib

Based on that and using a book theme, scinim getting started documentation is being built, e.g.: https://scinim.github.io/getting-started/basics/data_wrangli...

[sp33der89]

My statement was mostly an exaggeration, than an absolute truth. REPLs are really nice, but it's story with Nim is less nice.

There is: https://github.com/inim-repl/INim and the builtin `nim secret`.

There is also a Jupyter kernel: https://github.com/stisa/jupyternim

[poulpy123]

Actually, the bare minimum is a well supported and centralised numeric library providing arrays, matrix and the base tools

[short_sells_poo]

Perhaps for some things.

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.