How To Group By Two Column With Swapped Values In Pandas?

I want to group by columns where the commutative rule applies. For example column 1, column 2 contains values (a,b) in the first row and (b,a) for another row, then I want to grou

Solution 1:

Use numpy.sort for sorting each row:

cols = ['From','To']
df[cols] = pd.DataFrame(np.sort(df[cols], axis=1))
print (df)
  From  To  Count
0   a1  b1      4
1   a1  b1      3
2   a1  b2      2
3   a1  b3     12
4   a1  b3      6

df1 = df.groupby(cols, as_index=False)['Count'].sum()
print (df1)
  From  To  Count
0   a1  b1      7
1   a1  b2      2
2   a1  b3     18