Conditionally filling blank values in Pandas dataframes

Question

I have a datafarme which looks like as follows (there are more columns having been dropped off):

    memberID    shipping_country    
    264991      
    264991         


        

Answer 1

You can use chained groupbys, one with forward fill and one with backfill:

# replace blank values with `NaN` first:
df['shipping_country'].replace('',pd.np.nan,inplace=True)

df.iloc[::-1].groupby('memberID').ffill().groupby('memberID').bfill()

   memberID shipping_country
0    264991           Canada
1    264991           Canada
2       100              USA
3      5000               UK
4      5000               UK

This method will also allow a group made up of all NaN to remain NaN:

>>> df
   memberID shipping_country
0    264991                 
1    264991           Canada
2       100              USA
3      5000                 
4      5000               UK
5         1                 
6         1                 

df['shipping_country'].replace('',pd.np.nan,inplace=True)

df.iloc[::-1].groupby('memberID').ffill().groupby('memberID').bfill()

   memberID shipping_country
0    264991           Canada
1    264991           Canada
2       100              USA
3      5000               UK
4      5000               UK
5         1              NaN
6         1              NaN