Fill a new column if within group a value is present in pandas

Question

I have a dataframe such as

Groups subgroups Species Value 
G1     subg1     Sp1     NO 
G1     subg1     Sp1     YES 
G1     subg1     Sp2     NO 
G2     subg5     Sp7     YES
G2     subg6     Sp7     NO
G2     subg7     Sp7     NO 
G2     subg7     Sp7     YES 
G3     subg1     Sp1     NO 
G3     subg1     Sp1     NO

and I would like to fill a new Value2 column where within each Groups, subgroups and Species with "YES" if there is at least one YES within this group, and NO is there is none.

I should then get :

Groups subgroups Species Value Value2
G1     subg1     Sp1     NO    YES
G1     subg1     Sp1     YES   YES
G1     subg1     Sp2     NO    NO
G2     subg5     Sp7     YES   YES
G2     subg6     Sp7     NO    NO
G2     subg7     Sp7     NO    YES
G2     subg7     Sp7     YES   YES
G3     subg1     Sp1     NO    NO
G3     subg1     Sp1     NO    NO

Gotta be a dup: `df.groupby(['Groups', 'subgroups', 'Species'])['Value'].transform('max')` — Quang Hoang, Dec 07 '21 at 20:43
This [previous answer](https://stackoverflow.com/a/61242093/15497888) of yours is similar @QuangHoang There's also the standard [transform max](https://stackoverflow.com/q/35640364/15497888) — Henry Ecker, Dec 07 '21 at 20:49

score 2 · Accepted Answer · answered Dec 07 '21 at 20:47

Use groupby, transform:

df['Value2'] = (df['Value']=="YES").groupby([df['Groups'], 
                                             df['subgroups'], 
                                             df['Species']])\
                                   .transform('any')\
                                   .map({True:"YES", False:"NO"})
print(df)

Output:

  Groups subgroups Species Value Value2
0     G1     subg1     Sp1    NO    YES
1     G1     subg1     Sp1   YES    YES
2     G1     subg1     Sp2    NO     NO
3     G2     subg5     Sp7   YES    YES
4     G2     subg6     Sp7    NO     NO
5     G2     subg7     Sp7    NO    YES
6     G2     subg7     Sp7   YES    YES
7     G3     subg1     Sp1    NO     NO
8     G3     subg1     Sp1    NO     NO

score 1 · Answer 2 · answered Dec 07 '21 at 21:40

use np.where, groupby, transform

  df['Value2']=np.where(df.groupby(['Groups','subgroups','Species'])['Value'].transform(lambda x: (x=='YES').any()), 'YES','NO')

  Groups subgroups Species Value
0     G1     subg1     Sp1    NO
1     G1     subg1     Sp1   YES
2     G1     subg1     Sp2    NO
3     G2     subg5     Sp7   YES
4     G2     subg6     Sp7    NO
5     G2     subg7     Sp7    NO
6     G2     subg7     Sp7   YES
7     G3     subg1     Sp1    NO
8     G3     subg1     Sp1    NO

Fill a new column if within group a value is present in pandas

2 Answers2