Fastest way to set Pandas Dataframe elements based on function with index and column value as input

I have one Pandas dataframe column:

s = 
      VALUE
INDEX
A     12
B     21
C     7
...
Y     21
Z     7

      

        
        
        
      

    

I want to render it into a square matrix mask with the same index and columns as and s.index

, with each element either True

if the column and index value is the same in s

or False

otherwise.

mask = 
      A     B     C ...      Y     Z 
A  True False False ...  False False
B False  True False ...   True False
C False False  True ...  False  True
...
Y False  True False ...   True False
Z False False  True ...  False  True

      

        
        
        
      

    

My actual one s

has 10K + lines. What's the fastest way to create this mask

DataFrame?

One way I tried is to create a two-level dictionary with two loops for

. (for example, dict['A']['B'] = dict['B']['A'] = True if s.loc['A'] == s.loc['B'] else False

etc.). Then convert the bottom layer dict

to a Pandas series (for example row = pd.Series(dict[A])

), then add that series to mask

. mask

line by line is repeated line by line.

This is very time consuming and must obviously go through 10K x 10K / 2 = 50M elements ... Not ideal?