I've personally grown to love SQL and I think it is by far the clearest (if verbose) way to describe data transformation work. I learned traditional programming (e.g. languages like Python) long before I stumbled on SQL, but I'm not sure I could've understood Pandas/R as well without having learned SQL, particularly the concepts of joins.
That said, my affection for SQL correlates with my overall growth as a software engineer, particularly grokking the concept of piping data between scripts: e.g. writing a SQL script to extract, filter, and transform data, and then a separate Python/R script that creates a visualization. I think SQL seems so unattractive to data scientists (i.e. people who may not be particularly focused on software development) because it seems so alien and separate from the frameworks (R/Pandas) they use to do the actual analysis and visualization. And so it's understandable to just learn how to do it all in R/Pandas, rather than a separate language and paradigm for data wrangling.
That said, my affection for SQL correlates with my overall growth as a software engineer, particularly grokking the concept of piping data between scripts: e.g. writing a SQL script to extract, filter, and transform data, and then a separate Python/R script that creates a visualization. I think SQL seems so unattractive to data scientists (i.e. people who may not be particularly focused on software development) because it seems so alien and separate from the frameworks (R/Pandas) they use to do the actual analysis and visualization. And so it's understandable to just learn how to do it all in R/Pandas, rather than a separate language and paradigm for data wrangling.