(Disclaimers: I don't have much experience using Python to build data science products; potentially silly questions)
In industry, does Pandas tend to power the application layer, or does it find more use as an exploratory data tool?
If the latter, do people prefer to push computation down into OLAP databases for performance reasons?
And if so, what impact will the convergence of libraries and database functionality have on product development? These features strike me as things that you'd find in a database, e.g. query optimizer. I know in the past couple years there have been a couple commercial acquisitions of in-memory execution engines, e.g. Hyper by Tableau.
In industry, does Pandas tend to power the application layer, or does it find more use as an exploratory data tool?
If the latter, do people prefer to push computation down into OLAP databases for performance reasons?
And if so, what impact will the convergence of libraries and database functionality have on product development? These features strike me as things that you'd find in a database, e.g. query optimizer. I know in the past couple years there have been a couple commercial acquisitions of in-memory execution engines, e.g. Hyper by Tableau.