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

Formally, the correct answer is the @eumiro Solution. The problem of @HYRY solution is that when you have a sequence of numbers like [1,2,3,4] the solution is wrong, i. e., you don't have the mode. Example:

>>> import pandas as pd
>>> df = pd.DataFrame(
        {
            'client': ['A', 'B', 'A', 'B', 'B', 'C', 'A', 'D', 'D', 'E', 'E', 'E', 'E', 'E', 'A'], 
            'total': [1, 4, 3, 2, 4, 1, 2, 3, 5, 1, 2, 2, 2, 3, 4], 
            'bla': [10, 40, 30, 20, 40, 10, 20, 30, 50, 10, 20, 20, 20, 30, 40]
        }
    )

If you compute like @HYRY you obtain:

>>> print(df.groupby(['client']).agg(lambda x: x.value_counts().index[0]))
        total  bla
client            
A           4   30
B           4   40
C           1   10
D           3   30
E           2   20

Which is clearly wrong (see the A value that should be 1 and not 4) because it can't handle with unique values.

Thus, the other solution is correct:

>>> import scipy.stats
>>> print(df.groupby(['client']).agg(lambda x: scipy.stats.mode(x)[0][0]))
        total  bla
client            
A           1   10
B           4   40
C           1   10
D           3   30
E           2   20