Detect presence of inverse pairs in two columns of a DataFrame

Question

I have a dataframe with two columns; source, and target. I would like to detect inverse rows, i.e. for a pair of values (source, target), if there exists a pair of values (target, source) then assign True to a new column.

My attempt:

cols = ['source', 'target']
_cols = ['target', 'source']
sub_edges = edges[cols]
sub_edges['oneway'] = sub_edges.apply(lambda x: True if x[x.isin(x[_cols])] else False, axis=1)

Answer 1

You can apply a lambda function using similar logic to that in your example. We check if there are any rows in the dataframe with a reversed source/target pair.

Incidentally, the column name 'oneway' indicates to me the opposite of the logic described in your question, but to change this we can just remove the not in the lambda function.

Code

import pandas as pd
import random

edges = {"source": random.sample(range(20), 20),
         "target": random.sample(range(20), 20)}

df = pd.DataFrame(edges)

df["oneway"] = df.apply(
    lambda x: not df[
        (df["source"] == x["target"]) & (df["target"] == x["source"]) & (df.index != x.name)
    ].empty,
    axis=1,
)

Output

    source  target  oneway
0        9      11   False
1       16       1    True
2        1      16    True
3       11      14   False
4        4      13   False
5       18      15   False
6       14      17   False
7       13      12   False
8       19      19   False
9       12       3   False
10      10       6   False
11      15       5   False
12       3      18   False
13      17       0   False
14       6       7   False
15       5      10   False
16       7       2   False
17       8       9   False
18       0       4   False
19       2       8   False