Merge two dataframes with subheaders

So I have my first dataframe that has countries as headers and infected and death values as subheaders,

df
Dates       Antigua & Barbuda      Australia
          Infected      Dead      Infected   Dead
2020-01-22    0          0            0        0...
2020-01-23    0          0            0        0...
...

then I have my second dataframe,

df_indicators
Dates       Location      indicator_1      indicator_2 .....
2020-04-24  Afghanistan      0                  0
2020-04-25  Afghanistan      0                  0
...
2020-04-24  Yemen            0                  0
2020-04-25  Yemen            0                  0

I want to merge the dataframes so that the indicator columns become subheaders of the countries column like in df with the infected and dead subheaders.

What I want to produce is something like this,

df_merge
Dates        Antigua & Barbuda
        Infected    Dead   indicator_1   indicator_2....
2020-04-24  0         0        0             0...

There are so many indicators that are all named something different that I don’t feel I can call them all so not sure if theres a way I can do this easily.

Thank you in advance for any help!

Answer

Because there are duplicates first aggregate by mean and then reshape by Series.unstack with DataFrame.swaplevel:

df2 = df_indicators.groupby(['Dates','Location']).mean().unstack().swaplevel(0,1,axis=1)

Or with DataFrame.pivot_table:

df2 = (df.pivot_table(index='Dates', columns='Location', aggfunc='mean')
         .swaplevel(0,1,axis=1))

And last join with sorting MultiIndex in columns:

df = pd.concat([df, df2], axis=1).sort_index(axis=1)