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I use R most of the time and I find R notebooks very data exploration friendly. It makes it easy to back and forth just like Jupyter notebook. Producing HTML files from Rmarkdown files is also analysis friendly.

99% of the time I use tidyverse with no noticeable impact on the performance. For that occasional 1%, I must admit datatable package works out really well. tidyverse pipes are so unixy that makes it easy to transition to command such as cut, head, sort and column if needed without any mental contortion.

I have used Python occasionally and with method chaining, it can almost simulate the "dplyr" like syntax. However, it is hard to find some obscure statistical test out of the box which is easy in R.






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