Column transform in pyspark dataframe

I'm new in pyspark.

I want to do some column transforms.

My dataframe:

import pandas as pd df = pd.DataFrame([[10, 8, 9], [ 3, 5, 4], [ 1, 3, 9], [ 1, 5, 3], [ 2, 8, 10], [ 8, 7, 9]],columns=list('ABC')) 

df:

 A B C 0 10 8 9 1 3 5 4 2 1 3 9 3 1 5 3 4 2 8 10 5 8 7 9 

In df, each row is a triangulation, columns 'ABC' are the vertex index of the triangulations.

I want to get the dataframe of all the triangles' edges.

Under conditions:

1. For each edge, always lesser vertex index first.
2. Remove duplicate edges.
3. Edge[8, 9] and edge[9, 8] are seen as same edge, only remain [8,9]. (always lesser vertex index first)

My desire dataframe edge_df:

1 3 1 5 1 9 2 8 2 10 3 4 3 5 3 9 4 5 7 8 7 9 8 9 8 10 9 10 

I try to join 'AB', 'AC', 'BA', 'BC', 'CA', 'CB', then distinct(), and drop() the lesser vertex index on the right column.

Is there any way more effective?