4 ms·

> who needs interpretation when compile times are that fast! Well, interpretation is pretty useful for a REPL. And a REPL is not just useful to avoid compilati

> who needs interpretation when compile times are that fast! Well, interpretation is pretty useful for a REPL. And a REPL is not just useful to avoid compilation, but also as a way to explore a new API. And, most importantly, to preserve the results of long computations when you do not know yet what to do with it. If computing a value takes half an hour, you certainly don't want to recompute it each time you change something. Rather, you keep an open session, such as a REPL or a notebook, and keep computing with the already existing value

short_sells_poo 5y ago
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/6908c038707c7d8293049edb3d204f84 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).
- sp33der89 5y ago
  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 https://github.com/inim-repl/INim and the builtin `nim secret`. There is also a Jupyter kernel: https://github.com/stisa/jupyternim https://github.com/stisa/jupyternim
- pietroppeter 5y ago
  I did quench (most of) my thirst for a Repl building a notebook system (plug): https://github.com/pietroppeter/nimib 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_wrangling.html https://scinim.github.io/getting-started/basics/data_wrangli...
- poulpy123 5y ago
  Actually, the bare minimum is a well supported and centralised numeric library providing arrays, matrix and the base tools
  - short_sells_poo 5y ago
    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.
sp33der89 5y ago
See my comment from the other reply on this question for potential solutions, but as an fyi for those curious, Nim does come with a VIM that comes in very handy for such purposes: https://nim-lang.org/docs/nims.html https://nim-lang.org/docs/nims.html
h2odragon 5y ago
> don't want to recompute it each time you change something True... May I introduce you to the filesystem?
- pathsjs 5y ago
  Wow, what a great invention I have been missing! You made my day! :-)
  - h2odragon 5y ago
    Had a project once where 70% or so of the 8 month runtime was de/serialization. 800gb or so data wad and 16gb of ram; all messily multiply interlinked and not even the indexes would fit into ram. it sucked. but the architecture that imposed meat we were surprisingly resilient to power outages.

short_sells_poo 5y ago

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/6908c038707c7d8293049edb3d204f84 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).

sp33der89 5y ago
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 https://github.com/inim-repl/INim and the builtin `nim secret`. There is also a Jupyter kernel: https://github.com/stisa/jupyternim https://github.com/stisa/jupyternim
pietroppeter 5y ago
I did quench (most of) my thirst for a Repl building a notebook system (plug): https://github.com/pietroppeter/nimib 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_wrangling.html https://scinim.github.io/getting-started/basics/data_wrangli...
poulpy123 5y ago
Actually, the bare minimum is a well supported and centralised numeric library providing arrays, matrix and the base tools
- short_sells_poo 5y ago
  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.

sp33der89 5y ago

See my comment from the other reply on this question for potential solutions, but as an fyi for those curious, Nim does come with a VIM that comes in very handy for such purposes: https://nim-lang.org/docs/nims.html https://nim-lang.org/docs/nims.html

h2odragon 5y ago

> don't want to recompute it each time you change something True... May I introduce you to the filesystem?

pathsjs 5y ago
Wow, what a great invention I have been missing! You made my day! :-)
- h2odragon 5y ago
  Had a project once where 70% or so of the 8 month runtime was de/serialization. 800gb or so data wad and 16gb of ram; all messily multiply interlinked and not even the indexes would fit into ram. it sucked. but the architecture that imposed meat we were surprisingly resilient to power outages.