Sum Two Rows If Two Cells Are The Same But In Different Order

Similar to below Buyer Seller Amount John Mary 3 Mary John 2 David Bosco 2 Where I want to sum John and Mary rows into one Expected out come Trade1 Trade2 Amount John Mar

Solution 1:

First sort values by numpy.sort and create boolean mask by DataFrame.duplicated and then aggregate sum:

df[['Buyer','Seller']] = pd.DataFrame(np.sort(df[['Buyer','Seller']], axis=1))

df2 = df.groupby(['Buyer','Seller'], as_index=False)['Amount'].sum()
df2.columns = ['Trade1','Trade2','Amount']
print (df2)
  Trade1 Trade2  Amount
0  Bosco  David       2
1   John   Mary       5

If dont want modify original columns use syntactic sugar - groupby with Series:

df1 = pd.DataFrame(np.sort(df[['Buyer','Seller']], axis=1))
df1.columns = ['Trade1','Trade2']

df2 = df['Amount'].groupby([df1['Trade1'],df1['Trade2']]).sum().reset_index()
print (df2)
  Trade1 Trade2  Amount
0  Bosco  David       2
1   John   Mary       5