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

A slightly clumsier but faster approach for larger datasets involves getting the counts for a column of interest, sorting the counts highest to lowest, and then de-duplicating on a subset to only retain the largest cases. The code example is following:

>>> import pandas as pd
>>> source = pd.DataFrame(
        {
            'Country': ['USA', 'USA', 'Russia', 'USA'], 
            'City': ['New-York', 'New-York', 'Sankt-Petersburg', 'New-York'],
            'Short name': ['NY', 'New', 'Spb', 'NY']
        }
    )
>>> grouped_df = source\
        .groupby(['Country','City','Short name'])[['Short name']]\
        .count()\
        .rename(columns={'Short name':'count'})\
        .reset_index()\
        .sort_values('count', ascending=False)\
        .drop_duplicates(subset=['Country', 'City'])\
        .drop('count', axis=1)
>>> print(grouped_df)
  Country              City Short name
1     USA          New-York         NY
0  Russia  Sankt-Petersburg        Spb