[SOLVED] GroupBy pandas DataFrame and select most common value

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?

Solution

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'))