This is mainly a pandas question, but wanted to ensure we didn't build something at pandas that used SQLAlchemy inefficiently.
There are two main approaches: - Build a DataFrame from the results of a SQLAlchemy query; i.e. pandas has no knowledge that it's a SQL Query (`pd.DataFrame(query.all())`) - Feed pandas the SQL string generated by SQLAlchemy, and it sends the query to be executied (`pd.read_sql_query(Query().selectable, engine)` or `pd.read_sql(query.statement, query.session.bind)`). - Are those paths equivalent, or is one advisable? There's a discussion <https://github.com/pydata/pandas/issues/11181#issuecomment-142915059> at pandas whether the `pd.read sql` functions could take a SQLAlchemy - would that offer any integration benefits? Thanks (NB: I'd previously posted <https://bitbucket.org/zzzeek/sqlalchemy/issues/3542/design-for-pandas-access-to-query#> this on BitBucket and Mike asked me to move it here) -- You received this message because you are subscribed to the Google Groups "sqlalchemy" group. To unsubscribe from this group and stop receiving emails from it, send an email to sqlalchemy+unsubscr...@googlegroups.com. To post to this group, send email to sqlalchemy@googlegroups.com. Visit this group at http://groups.google.com/group/sqlalchemy. For more options, visit https://groups.google.com/d/optout.
