replace/fill na values for some specific rows

Question

df :

address        city
BlockOf13thSt  Treasure Isla
Lincoln        Presidio
Duboce Park    Unknown
Twin Peaks     Unknown
Bernal Heights NaN
Holly Courts   Unknown
Ocean Beach    NaN
Maiden Ln      NaN
Avenue N       NaN

output

address city BlockOf13thSt Treasure Isla Lincoln Presidio Duboce Park San Francisco Twin Peaks San Francisco Bernal Heights San Francisco Holly Courts San Francisco Ocean Beach San Francisco Maiden Ln New York Avenue N New York

is there any syntax in pandas like SQL (IN)?? where address IN (Duboce Park, Twin Peaks, Bernal Heights, Holly Courts/Ocean Beach) and replacing/fillna for 'San Francisco' and 'New York'

thanks

Answer 1

import pandas as pd  
#Replace all those localities with 'San Francisco'. For this we use .isin() function
df.loc[df['address'].isin(pd.Series(['Duboce Park','Twin Peaks','Bernal Heights','Holly Courts','Ocean Beach'])),'city']='San Francisco'

#Replace all NaNs with 'New York' with fillna().
df = df.fillna('New York')

df
Out[47]: 
          address           city
0   BlockOf13thSt  Treasure Isla
1        Lincoln        Presidio
2     Duboce Park  San Francisco
3      Twin Peaks  San Francisco
4  Bernal Heights  San Francisco
5    Holly Courts  San Francisco
6     Ocean Beach  San Francisco
7       Maiden Ln       New York
8        Avenue N       New York