GroupBy pandas DataFrame and select most common value

Question

I have a data frame with three string columns. I know that the only one value in the 3rd column is valid for every combination of the first two. To clean the data I have to

Answer 1

If you want another approach for solving it that is does not depend on value_counts or scipy.stats you can use the Counter collection

from collections import Counter
get_most_common = lambda values: max(Counter(values).items(), key = lambda x: x[1])[0]

Which can be applied to the above example like this

src = pd.DataFrame({'Country' : ['USA', 'USA', 'Russia','USA'], 
              'City' : ['New-York', 'New-York', 'Sankt-Petersburg', 'New-York'],
              'Short_name' : ['NY','New','Spb','NY']})

src.groupby(['Country','City']).agg(get_most_common)