New pandas boolean column based on conditions for each unique value

Question

I have a dataframe with the following columns: ID, event_name, event_date

Goal: For every unique ID, if they have an event_name == 'attended book event' then I want to create a new column attended_book_event and have the value = 1 . If they do not have and event_name==' attended book event' then the value in the new column is 0 .

Sample:

ID| event_name | event_date

1| joined_club| 12-12-03

1| attended_book_event| 12-27-03

1| elite_member| 03-01-05

2| joined_club| 12-12-03

2| elite_member| 03-01-05

I tried to groupby the id and then create a new column with the condition but the results were not what I was looking for.

 df_dose['had_dose_increase'] = [1 if df_dose['event_name'] == 
  'dose_increased' else 0] 

I want a new column

ID| event_name | event_date| attended_book_event

1| joined_club| 12-12-03| 1

1| attended_book_event| 12-27-03|1

1| elite_member| 03-01-05|1

2| joined_club| 12-12-03|0

2| elite_member| 03-01-05|0

Answer 1

Using pd.Series.groupby with transform :

df['attended_book_event'] = df.groupby('ID')['event_name'].transform(lambda x: 'attended_book_event' in set(x)).astype(int)

Output:

   ID           event_name event_date  attended_book_event
0   1          joined_club   12-12-03                    1
1   1  attended_book_event   12-27-03                    1
2   1         elite_member   03-01-05                    1
3   2          joined_club   12-12-03                    0
4   2         elite_member   03-01-05                    0