I've been using SQL since 1996, so I may be biased. I've used MySQL and SQLite 3 extensively, but have also used Microsoft SQL Server and Oracle.
The vast majority of the operations I've seen done with Pandas can be done more easily with SQL. This includes filtering a dataset, selecting specific columns for display, applying a function to a values, and so on.
SQL has the advantage of having an optimizer and data persistence. SQL also has error messages that are clear and understandable. Pandas has a somewhat cryptic API, in which sometimes it's appropriate to use a single
[ stuff ], other times you need
[[ stuff ]], and sometimes you need a
.loc. Part of the complexity of Pandas arises from the fact that there is so much overloading going on.
So I'm trying to understand why Pandas is so popular.