@chriddyp having the precise same issue described by @chubukov as I’m using a reasonable sized table. Doesn’t feel right to be passing such a huge data frame back&forward, specially if the table is not editable.
Would be interesting to have the original indexes returned & always tracked. Think that’s how it works in R shiny, we get the original dataframe indexes selected. Adding, that it would be also useful to get pandas index support, if so the pandas original index would be returned, not the position from dictionary.
(think this would probably also avoid triggering callbacks if the actual selected_indexes don’t change with sorting or filtering)
What I am doing now:
-load a reasonable sized dataframe, with hundreds of columns
-sample small percentage, sampled dataframe keeps the original index
-create a data frame to show in dash, one column is the original index, subset few number of columns to show in dash (not all the original columns)
-then load in dash, index column is visible (hack),
-so any sorting,filtering, I get the dash selected_rows+selected_indexes, with that I get the index column values, then use that to select on my master data frame, to get all columns data and do what I need then
should be way easier this is one of my typical patterns for this apps
or if anyone has better workaround!