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

You can use value_counts() to get a count series, and get the first row:

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']})

source.groupby(['Country','City']).agg(lambda x:x.value_counts().index[0])

In case you are wondering about performing other agg functions in the .agg() try this.

# Let's add a new col,  account
source['account'] = [1,2,3,3]

source.groupby(['Country','City']).agg(mod  = ('Short name', \
                                        lambda x: x.value_counts().index[0]),
                                        avg = ('account', 'mean') \
                                      )