panda grouping dates by variable with transposed value variables

Sorry about that, I thought the link would be enough.

So, I have a df containing the German regional states and its country towns and some codes indicating policies. In the columns are the dates as longitudinal indicators of the specific policies. The desired output is to transpose the date columns and the policy column. So that each policy would have a unique column. And the date range can be used as index variable which repeates for every country town. If I use

df.groupby(['bl', 'kreis', 'policy', 'd20200301', 'd20200302', ...]).size().unstack() # without 'kreisziffer' because it only indicates the 'kreis' as integer and is irrelevant in that case

the output grouped and the regional states and country towns only occur ones and the policy will group to this categories.

If I use df.transpose() I only get columns as rownames but the policies will further be replicated by each country town although I get the dates in the desired format as a single column.

If i use df.pivot(index = ['kreis', 'bl'], columns = 'policy') thte single dates are hierarical indexed with each policy value.

If I use df.melt(id_vars= ['bl', 'kreisziffer', 'kreis', 'policy]) I will get each cell value as a new column named value but the df part of the policy x dates should be something like transposed. But policies are unique columns and each value block reffering to a country town will be concatenated and the date interval is replicated each time the 'kreis' variable value changes. Something like that: