Standard Type Library as DataFrames?

[SteffenMeinecke]

Options:

1. as is: type(net.std_types["line"]["149-AL1/24-ST1A 10.0"])->dict (in dict in dict)
2. type(net.std_types["line"].loc[["149-AL1/24-ST1A 10.0"]])->dataframe (in dict)
3. type(net.line_types.loc[["149-AL1/24-ST1A 10.0"]])->dataframe

[rbolgaryn]

pp.available_std_types returns a DataFrame, with the names of types as the index. The third option is attractive because it makes the std_types more transparent, and probably would simplify learning pandapower.

[SteffenMeinecke]

one thing to consider (maybe its the same as in all other net dataframes?): There are some optional parameters, e.g. max_tap, which would destroy the integer dtype if not given and nullable integer dtypes are not used.

[rbolgaryn]

I think we should go ahead and implement option 2

[SteffenMeinecke]

I think most consistent with other net datastructure would be option 3: e.g. bus_geodata and line_geodata are also stored as dataframes directly in net.

[SteffenMeinecke]

@rbolgaryn and me agreed on option 3 but with singular names, e.g. net.line_std_type or net.trafo3w_std_type.