I now do most of my data analysis in Julia instead of pandas, but used pandas for a long time. SQL is a valuable skill and useful when you need to optimize a qu

I now do most of my data analysis in Julia instead of pandas, but used pandas for a long time. SQL is a valuable skill and useful when you need to optimize a query for performance, which can sometimes happen with pandas and is much less likely with Julia. However, even if a pandas query takes 5X longer to run than a SQL query you must consider the efficiency to a developer. You can chain pandas commands together that will accomplish something that takes 10x the lines of SQL. With SQL you’re more likely to end up with many intermediate CTEs along the way. So while you can definitely save processor cycles by using SQL, I don’t think you’ll save clock-face time by using it in most one off tasks. Datascience is usually column oriented and Julia and pandas allow you to stay in that world.