Pandas column pairwise difference for each possible pair [duplicate]

Question

I have the following dataframe.

df = pd.DataFrame([['a', 4], ['b', 1], ['c', 2], ['d', 0], ], columns=['item', 'value'])
df
item | value    
a    | 4
b    | 1
c    | 2
d    | 0 

I want to calculate the pairwise absolute difference between each possible pair of item to give the following output.

item| a     | b     | c     | d
a   | 0.0   | 3.0   | 2.0   | 4.0
b   | 3.0   | 0.0   | 1.0   | 1.0
c   | 2.0   | 1.0   | 0.0   | 2.0
d   | 4.0   | 1.0   | 2.0   | 0.0

After a lot of search, I could find answer only to direct element by element difference, which results in a single column output.

So far, I've tried

pd.pivot_table(df, values='value', index='item', columns='item', aggfunc=np.diff)

but this doesn't work.

Answer 1

This question has been answered here. The only difference is that you would need to add abs:

abs(df['value'].values - df['value'].values[:, None])