How to compare 2 dataframes and then output an ID to inform if a row has changed?

I have 2 dataframes:

df1 = pd.DataFrame({"id1": ["A", "B", "C", "D"], "id2": ["1", "2", "2", "1"]})

df2 = pd.DataFrame({"id1": ["A", "B", "F", "C", "D", "E"], "id2": ["1", "1", "2", "1", "1", "2"]})

I want to find the rows which are different, here is the code I tried:

vals = set(df2.iloc[:, :2].apply(tuple, 1)) \ - set(df1.apply(tuple, 1))

The result is the following which gives me the rows which are different between the 2 dataframes:

{('B', '1'), ('E', '2'), ('C', '1'), ('F', '2')}

I would like to get an additional output which tells me which rows of df2 have been modified or not (Y for yes and N for No) :

>Solution :

You can merge and build a boolean mask that returns True if a value didn’t change (or was expanded) and map Y/N values according to it:

df2 = df2.merge(df1, on='id1', how='left', suffixes=('_',''))
df2['Modified_ID'] = (df2['id2_'].eq(df2['id2']) | df2['id2'].isna()).map({True:'N', False:'Y'})
df2 = df2.drop(columns=['id2_','id2'])

Output:

  id1 Modified_ID
0   A           N
1   B           Y
2   F           N
3   C           Y
4   D           N
5   E           N