How to add a string prefix to non-missing dataframe values?

June 8, 2022

Example dataframe:

df = pd.DataFrame(dict(age=[5, 6, np.NaN], born=[pd.NaT, pd.Timestamp('1939-05-27'), pd.Timestamp('1940-04-25')], name=['Alfred', 'Batman', ''], toy=[None, 'Batmobile', 'Joker']))

I can detect non-missing values:

df.notna()

Let’s say I want to add ‘+’ as the string prefix to all non-missing values:

    age         born     name         toy
0  +5.0          NaT  +Alfred        None
1  +6.0  +1939-05-27  +Batman  +Batmobile
2   NaN  +1940-04-25        +      +Joker

How can I use the returned boolean (dataframe) values to add a string prefix to every non-missing value?

>Solution :

What about:

df.mask(df.notna(), '+'+df.astype(str))

output:

    age         born     name         toy
0  +5.0          NaT  +Alfred        None
1  +6.0  +1939-05-27  +Batman  +Batmobile
2   NaN  +1940-04-25        +      +Joker