np.where() do nothing if condition fails

Question

I have a sample from my dataframe:

       Created      Insert Time   MatchKey              In Previous    New Type
18593  2016-08-12   2018-02-19    LXGS0903         


        

Answer 1

You can use pd.Series.mask for exactly this purpose:

df['New Type'].mask(df['In Previous']=='Yes', 'In Previous', inplace=True)

Somewhat confusingly, you have to remember that pd.Series.mask updates a value when a condition is met, while pd.Series.where updates a value when a condition is not met.

Answer 2

Just return the column instead of pass this is the same as doing nothing when the condition is False:

current['New Type'] = np.where(current['In Previous']=='Yes','In Previous',current['New Type'] )

Or you can just mask those rows:

current['New Type'] = current.loc[current['In Previous']=='Yes', 'In Previous']