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The lack of mature libraries in the style of pandas, sklearn or the tidyverse, and the lack of a stable REPL is a major drawback for "production" data science.
by ploika 7y ago
The lack of mature libraries in the style of pandas, sklearn or the tidyverse, and the lack of a stable REPL is a major drawback for "production" data science. On the other hand though I've had fun playing with it in a personal capacity precisely because of the rough edges.
If I want to fit a particular kind of model I'll probably have to roll my own, meaning I have to be familiar with what the algorithm is doing and why, rather than just importing something straight from sklearn.
I also need to pay more attention to the code as I'm writing it instead of lazily debugging by trial and error in the REPL like I can with Python.
Basically, I wouldn't recommend it at work, but it's been fun and useful at home.
- V1ndaar 7y agoSince you mention tidyverse, I might shamelessly mention that I'm currently working on a sort of port of ggplot2 and some dplyr features in Nim [1]. It's super WIP, but for most plots and simple data frame operations (complicated stuff I do beforehand) I need it has mostly replaced plotly [2] (or in some cases matplotlib). However, don't expect the included data frame to be fast. That in combination with a dynamic nature has been out of scope for me alone. [1] https://github.com/Vindaar/ggplotnim/tree/addDocs https://github.com/Vindaar/ggplotnim/tree/addDocs [2] https://github.com/brentp/nim-plotly https://github.com/brentp/nim-plotly