We use pandas-gbq a lot for our daily analyses. It is known that memory consumption can be a pain, see e.g. https://www.dataquest.io/blog/pandas-big-data/

I have started to write a patch, which could be integrated into an enhancement for read_gbq (rough idea, details TBD):

• Provide boolean optimize_memory option
• If True, the source table is inspected with a query to get min, max, presence of nulls and % of unique number of strings for INTEGER and STRING columns, respectively
• When calling to_dataframe this information is passed to the dtypes option, downcasting integers to the appropriate numpy (u)int type, and converting strings to pandas category type at some threshold (less than 50% of unique values)

I already have a working monkey-patch, which is still a bit rough. If there is enough interest I'd happily make it more robust and submit a PR. Would be my first significant contribution to an open source project, so some help and feedback would be appreciated.

Curious to hear your views on this.