boolean operation with groupby in pandas

Question

I would like to use pandas.groupby in a particular way. Given a DataFrame with two boolean columns (call them col1 and col2) and an id column, I want to add a column in the following way:

for every entry, if (col2 is True) and and (col1 is True for any of the entries with the same id) then assign True. Otherwise False.

I have made a simple example:

df = pd.DataFrame([[0,1,1,2,2,3,3],[False, False, False, False, False, False, True],[False, True, False, False, True ,True, False]]).transpose()
df.columns = ['id', 'col1', 'col2']

gives the following DataFrame:

     id   col1   col2
0    0   False   False
1    1   False   True
2    1   False   False
3    2   False   False
4    2   False   True
5    3   False   True
6    3   True    False

According to the above rule, the following should column should be added:

0    False
1    False
2    False
3    False
4    False
5     True
6    False

Any ideas on an elegant way to do this?

Answer 1

df.groupby('id').col1.transform('any') & df.col2

0    False
1    False
2    False
3    False
4    False
5     True
6    False
dtype: bool

Answer 2

This code will produce the output you requested:

df2 = df.merge(df.groupby('id')['col1'] # group on "id" and select 'col1'
                    .any()              # True if any items are True
                    .rename('cond2')    # name Series 'cond2'
                    .to_frame()         # make a dataframe for merging
                    .reset_index())     # reset_index to get id column back
print(df2.col2 & df2.cond2)             # True when 'col2' and 'cond2' are True