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How is Julia in terms of data science dev experience? Nothing ever felt as good as the R+tidyverse combo to me, at least in Python.

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Julia is pretty good at basic data science. Working with dataframes is comparable to R's data.tables with the benefit that I don't need to switch languages if I want to run a fast loop over some data as part of a calculation or use a custom data structure.

I'm not a fan of pandas, so I'd say Julia and R beat python at basic dataframe manipulation. Nothing beats kdb+/q at dataframes though imo.

Have you tried Polars in Python? When you get going it's pretty similar to tidyverse, except you're chaining methods instead of piping, and it's lazily evaluated + parallel because of the underlying Rust engine. IME it's tidyverse > polars > pandas > data.table in terms of ergonomics

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