I've only touched enough Pandas to get a feel for it, so I'm talking more here about dplyr, but I think most of this applies to Pandas as well. Yes, SQL is inc

I've only touched enough Pandas to get a feel for it, so I'm talking more here about dplyr, but I think most of this applies to Pandas as well. Yes, SQL is incredibly battle-tested, and has a ton of features that newer systems need to catch up on, and there's a ton of work sunk into performance on too-big-for-RAM datasets that standalone Pandas and dplyr doesn't have. (But see dbplyr, for example.) But actually programming in SQL sucks. There's no such thing as code reuse, as parameterization, as composability, etc. Everything about DRY goes right out the window when writing SQL and copy-pasta is the law of the land. Writing dplyr is so much more comfortable for me with just a little bit of learning about it. And I think it's going to be so much easier to extend dplyr to cover the gaps between it an SQL than it would be to try and fix the ergonomics of SQL.