[FIXED] Keep only data in pandas dataframe where column value occurs more than once for a groupby arrangement

Issue

I have a dataframe like so:

ID 1          ID 2
1             5
1             5
1             6
2             7
2             5
2             7
3             8
3             9
3             10

What I am trying to do is only keep the rows that, when the df is grouped by ID 1, ID 2 exists more than once. I have used .groupby and .value_counts in this method:

df_temp = df.groupby('ID 1')
df_output = df_temp['ID 2'].value_counts()[df_temp1['ID 2'].value_counts() > 1]

This returns something like so:

ID 1          ID 2
1             5         2
2             7         2

Is there a way I can use this in order to only keep the rows in the initial df with the ID 2 in this groupedby object? To get a result like this:

ID 1          ID 2
1             5
1             5
2             7
2             7

Solution

Don't groupby, perform boolean indexing with duplicated:

out = df[df.duplicated(['id1', 'id2'], keep=False)]

Output:

   id1  id2
0    1    5
1    1    5
3    2    7
5    2    7

Answered By - mozway