Python pandas idxmax for multiple indexes in a dataframe

Question

I have a series that looks like this:

            delivery
2007-04-26  706           23
2007-04-27  705           10
            706         1089
            708         


        

Answer 1

Your example code doesn't work because the idxmax is executed after the groupby operation (so on the whole dataframe)

I'm not sure how to use idxmax on multilevel indexes, so here's a simple workaround.

Setting up data :

import pandas as pd
d= {'Date': ['2007-04-26', '2007-04-27', '2007-04-27', '2007-04-27',
             '2007-04-27', '2007-04-28', '2007-04-28'], 
        'DeliveryNb': [706, 705, 708, 450, 283, 45, 89],
        'DeliveryCount': [23, 10, 1089, 82, 34, 100, 11]}

df = pd.DataFrame.from_dict(d, orient='columns').set_index('Date')
print df

output

            DeliveryCount  DeliveryNb
Date                                 
2007-04-26             23         706
2007-04-27             10         705
2007-04-27           1089         708
2007-04-27             82         450
2007-04-27             34         283
2007-04-28            100          45
2007-04-28             11          89

creating custom function :

The trick is to use the reset_index() method (so you easily get the integer index of the group)

def func(df):
    idx = df.reset_index()['DeliveryCount'].idxmax()
    return df['DeliveryNb'].iloc[idx]

applying it :

g = df.groupby(df.index)
g.apply(func)

result :

Date
2007-04-26    706
2007-04-27    708
2007-04-28     45
dtype: int64