Good, I hope this trend of shoving ML into SQL (instead of the other way around) continues. I always thought it was silly that every "data wrangling" system like Pandas and R needed to (poorly) re-invent SQL.
Unless you present clear arguments, I'd refrain from saying that Pandas is "poorly re-inventing SQL".
Pandas is now the standard for data analysis (as long as things fit into memory). It's much much easier to debug than a SQL command. You can write operations as a succession of small logical steps (instead of one huge query that is hard to debug).
It's raw Python, so you can do something like:
df.groupby('movie_id').agg(dict(ratings='median', price=lambda p : np.percentile(p, .95))).plot.bar(bins=50)
Yeah, also in Pandas you can do stuff that otherwise requires writing a custom reducer or UDAF in which case you aren't using SQL anyway.
I just use SQL to grab and if necessary aggregate the data and then do everything else in Pandas - using Python custom reducers to deployed trained models although we are migrating to GCP now so soon that won't be necessary.
Pandas is now the standard for data analysis (as long as things fit into memory). It's much much easier to debug than a SQL command. You can write operations as a succession of small logical steps (instead of one huge query that is hard to debug).
It's raw Python, so you can do something like:
df.groupby('movie_id').agg(dict(ratings='median', price=lambda p : np.percentile(p, .95))).plot.bar(bins=50)