Hi Vikram, Sounds like you got it working--great! Also the LLMs are terrific for explaining language concepts if you are stuck conceptually.
If you need a dataframe package that scales to big data (as it turns out parsing floating point numbers is a very slow operation), I wrote a use-all-cores fast parallel loading dataframe for Go called SlurpDF. I was envious of how fast R's data.table could read in CSV files in parallel. See https://github.com/glycerine/slurpdf See slurp_test.go for an example of writing back to CSV on disk. (this was in service of a little Xgboost-like gradient boosted decision tree ensemble machine learner, e.g. https://github.com/glycerine/gocortado) Enjoy, Jason -- You received this message because you are subscribed to the Google Groups "golang-nuts" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To view this discussion visit https://groups.google.com/d/msgid/golang-nuts/a6bf2f0f-4775-4e03-a69e-c567e45d8db1n%40googlegroups.com.
