["python","pandas","dataframe","group-by","mode"]
GroupBy pandas DataFrame and select most common value

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 group by data frame by first two columns and select most common value of the third column for each combination. My code: import pandas as pd from scipy import stats 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: stats.mode(x['Short name'])[0]) Last line of code doesn't work, it says KeyError: 'Short name' and if I try to group only by City, then I got an AssertionError. What can I do fix it?
You can use value_counts() to get a count series, and get the first row: 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'))