Jan Gorecki

I understand that, but it requires more branching in code. Depending if data is larger than memory on not. If having a feature requested here (to do that for user...

when resolved this SO can be answered: https://stackoverflow.com/questions/64910029/how-to-convert-pandas-dataframe-to-datatable-frame-containing-int32-nullable-in

@XiaomoWu the reason to avoid factor was not speed but problems with its levels when combining, filtering, or performing string operations like `paste`. Factors are faster than character, and can...

When implemented it might allow to read 1e9 data sets into db-benchmark for pandas, currently `to_pandas()` fails with OOM (afair). Having categoricals instead of objects could significantly reduce memory footprint....

Yes, it is big to big join where we join table of the same size, 90% of rows are matching

Other join queries have now also very unstable timings, possibly caused by #2775. For example q2 "medium inner on int": On 1e9 one time `622.36, 687.774`, another time `1592.488, 1306.6`....

any idea if this FR is addressed by [`materialize`](https://datatable.readthedocs.io/en/latest/api/frame/materialize.html)?

Similarly `colorspace` package in `color_unique_labels` examples. Regards, Jan

it seems to be no longer possible using most recent version from android store, could we reopen this issue?

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