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 GroupBy + ffill / bfill:

def filler(x):
    return x.ffill().bfill()

res = df.groupby('memberID')['shipping_country'].apply(filler)

A custom function is necessary as there's no combined Pandas method to ffill and bfill sequentially.

This also caters for the situation where all values are NaN for a specific memberID; in this case they will remain NaN.