I would like to change the column days (which is a datetime column) conditional on the indicator column, i.e. when indicator is equal to either DTM or AMC, I would like to add 1 day to the days column.
import pandas as pd
df = pd.DataFrame({'days': [1, 2, 3],
'indicator': ['BMO', 'DTM','AMC']})
So the result looks like this:
days indicator
0 1 BMO
1 3 DTM
2 4 AMC
Use np.where with isin:
df['days'] = np.where(df['indicator'].isin(['DTM', 'AMC']), df['days'].add(1), df['days'])
days indicator
0 1 BMO
1 3 DTM
2 4 AMC
Use a boolean mask:
df['days'] += (df.indicator.eq('AMC') | df.indicator.eq('DTM'))
print(df)
Output
days indicator
0 1 BMO
1 3 DTM
2 4 AMC
As an alternative you could use isin:
df['days'] += df.indicator.isin(('AMC', 'DTM'))
print(df)
You can add the boolean mask directly because in Python, booleans values are integers (0, 1).
Use Series.mask:
df['days']=df['days'].mask(df['indicator'].isin(['DTM','AMC']),df['days']+1)
or Series.where:
df['days']=df['days'].where(~df['indicator'].isin(['DTM','AMC']),df['days']+1)
Output
#print(df)
days indicator
0 1 BMO
1 3 DTM
2 4 AMC
