Can anyone suggest a blog where Variational Autoencoder has been used for time series forecasting? 
The real first question is why are people more productive with DataFrame abstractions than pure SQL abstractions.
TLDR; SQL is not geared around the (human) development and debugging process, DataFrames are.
The main reason is that DataFrame abstractions allow you to construct SQL statements whilst avoiding verbose and illegible nesting. The pattern of writing nested routines, commenting them out to check them, and then uncommenting them is replaced by single lines of transformation. You can naturally run things line by line in a repl (even in Spark) and view the results.
Consider the example, of adding a new transformed (string mangled column) to a table, then grouping by it and doing some aggregations. The SQL gets pretty ugly. Pandas can solve this but is missing some things when it comes to truly big data or in particular partitions (perhaps improved recently).
DataFrames should be viewed as a high-level API to SQL routines, even if with pandas they are not at all rendered to some SQL planner.

You can probably have many technical discussions around this, but I'm considering the user perspective below.
One simple reason why you may see a lot more questions around Pandas data manipulation as opposed to SQL is that to use SQL, by definition, means using a database, and a lot of use-cases these days quite simply require bits of data for 'one-and-done' tasks (from .csv, web api, etc.). In these cases loading, storing, manipulating and extracting from a database is not viable.
However, considering cases where the use-case may justify using either Pandas or SQL, you're certainly not wrong.  If you want to do many, repetitive data manipulation tasks and persist the outputs, I'd always recommend trying to go via SQL first.  From what I've seen the reason why many users, even in these cases, don't go via SQL is two-fold.
Firstly, the major advantage pandas has over SQL is that it's part of the wider Python universe, which means in one fell swoop I can load, clean, manipulate, and visualize my data (I can even execute SQL through Pandas...).  The other is, quite simply, that all too many users don't know the extent of SQL's capabilities.  Every beginner learns the 'extraction syntax' of SQL (SELECT, FROM, WHERE, etc.) as a means to get your data from a DB to the next place.  Some may pick up some of the more advance grouping and iteration syntax.  But after that there tends to be a pretty significant gulf in knowledge, until you get to the experts (DBA, Data Engineers, etc.).
tl;dr: It's often down to the use-case, convenience, or a gap in knowledge around the extent of SQL's capabilities.
