Pandas:drop_duplicates() based on condition in python

Question

Having below data set:

data_input:

    A     B
1  C13D  C07H
2  C07H  C13D
3  B42C  B65H
4  B65H  B42C
5  A45B  A47C

ie row 1 and row 2 in data_input are same,I just want to keep one,so drop row 2.

Want the Output as below:

data_output:

    A     B
1  C13D  C07H
2  B42C  B65H
3  A45B  A47C

Answer 1

You can create a third column 'C' based on 'A' and 'B' and use it to find duplicates as such:

df['C'] = df['A'] + df['B']
df['C'] = df['C'].apply(lambda x: ''.join(sorted(x)))
df = df.drop_duplicates(subset='C')[['A', 'B']]

Answer 2

You could use duplicated and np.sort

In [1279]: df[~df.apply(np.sort, axis=1).duplicated()]
Out[1279]:
      A     B
1  C13D  C07H
3  B42C  B65H
5  A45B  A47C

---

Details

In [1281]: df.apply(np.sort, axis=1)
Out[1281]:
      A     B
1  C07H  C13D
2  C07H  C13D
3  B42C  B65H
4  B42C  B65H
5  A45B  A47C

In [1282]: df.apply(np.sort, axis=1).duplicated()
Out[1282]:
1    False
2     True
3    False
4     True
5    False
dtype: bool