Improve missing value handling logic of ptable_heatmap
Our ptable_heatmap has suboptimal missing value handling logic, to close #230:
Currently Non-PTableData typed data would be converted, but with the missing_strategy hard-coded as mean, meaning we're artificially creating non-existent data by default also user cannot control this behaviour, perhaps showing missing value as is (-) would be better.
Is there a better way to resolve this than simply adding one more argument (on_empty sort of overlaps, perhaps we could partly reuse it)
Import performance check show current import time, also round to int (should suffice as import time is on the 1000 level)
TODO list:
[ ] Make sure a warning would be emitted (only) when data contains NaN/None
[ ] Test missing value handling for both PTableData and others (pd.DataFrame) when containing missing values (NaN/None)
Improve missing value handling logic of
ptable_heatmapOur
ptable_heatmaphas suboptimal missing value handling logic, to close #230:PTableDatatyped data would be converted, but with themissing_strategyhard-coded asmean, meaning we're artificially creating non-existent data by default also user cannot control this behaviour, perhaps showing missing value as is (-) would be better.on_emptysort of overlaps, perhaps we could partly reuse it)1000level)TODO list:
NaN/NonePTableDataand others (pd.DataFrame) when containing missing values (NaN/None)