In-place update in pandas: update the value of the cell based on a condition

Question

      DOB        Name      
0   1956-10-30   Anna
1   1993-03-21   Jerry        
2   2001-09-09   Peter     
3   1993-01-15   Anna   
4   1999-05-02   James 
5   1962-12-17   Jerry
6   1972-05-04   Kate

In the dataframe similar to the one above where I have duplicate names. So I am want to add a suffix '_0' to the name if DOB is before 1990 and a duplicate name.

I am expecting a result like this

      DOB        Name      
0   1956-10-30   Anna_0
1   1993-03-21   Jerry        
2   2001-09-09   Peter     
3   1993-01-15   Anna   
4   1999-05-02   James
5   1962-12-17   Jerry_0
6   1972-05-04   Kate

I am using the following

df['Name'] = df[(df['DOB'] < '01-01-1990') & (df['Name'].isin(['Anna','Jerry']))].Name.apply(lambda x: x+'_0')

But I am getting this result

      DOB        Name      
0   1956-10-30   Anna_0
1   1993-03-21   NaN
2   2001-09-09   NaN     
3   1993-01-15   NaN   
4   1999-05-02   NaN
5   1962-12-17   Jerry_0
6   1972-05-04   NaN

How can I add a suffix to the Name which is a duplicate and have to be born before 1990.

Answer 1

Problem in your df['Name'] = df[(df['DOB'] < '01-01-1990') & (df['Name'].isin(['Anna','Jerry']))].Name.apply(lambda x: x+'_0') is that df[(df['DOB'] < '01-01-1990') & (df['Name'].isin(['Anna','Jerry']))] is a filtered dataframe whose rows are less than the original. When you assign it back, the not filtered rows doesn't have corresponding value in the filtered dataframe, so it becomes NaN .

You can try mask instead

m = (df['DOB'] < '1990-01-01') & df['Name'].duplicated(keep=False)

df['Name'] = df['Name'].mask(m, df['Name']+'_0')

Answer 2

You can use masks and boolean indexing :

# is the year before 1990?
m1 = pd.to_datetime(df['DOB']).dt.year.lt(1990)
# is the name duplicated?
m2 = df['Name'].duplicated(keep=False)

# if both conditions are True, add '_0' to the name
df.loc[m1&m2, 'Name'] += '_0'

output:

          DOB     Name
0  1956-10-30   Anna_0
1  1993-03-21    Jerry
2  2001-09-09    Peter
3  1993-01-15     Anna
4  1999-05-02    James
5  1962-12-17  Jerry_0
6  1972-05-04     Kate