python - Pandas Groupby Agg Function Does Not Reduce

Question

I am using an aggregation function that I have used in my work for a long time now. The idea is that if the Series passed to the function is of length 1 (i.e. the group only has one observation) then that observations is returned. If the length of the Series passed is greater than one, then the observations are returned in a list.

This may seem odd to some, but this is not an X,Y problem, I have good reason for wanting to do this that is not relevant to this question.

This is the function that I have been using:

def MakeList(x):
    """ This function is used to aggregate data that needs to be kept distinc within multi day 
        observations for later use and transformation. It makes a list of the data and if the list is of length 1
        then there is only one line/day observation in that group so the single element of the list is returned. 
        If the list is longer than one then there are multiple line/day observations and the list itself is 
        returned."""
    L = x.tolist()
    if len(L) > 1:
        return L
    else:
        return L[0]

Now for some reason, with the current data set I am working on I get a ValueError stating that the function does not reduce. Here is some test data and the remaining steps I am using:

import pandas as pd
DF = pd.DataFrame({'date': ['2013-04-02',
                            '2013-04-02',
                            '2013-04-02',
                            '2013-04-02',
                            '2013-04-02',
                            '2013-04-02',
                            '2013-04-02',
                            '2013-04-02',
                            '2013-04-02',
                            '2013-04-02'],
                    'line_code':   ['401101',
                                    '401101',
                                    '401102',
                                    '401103',
                                    '401104',
                                    '401105',
                                    '401105',
                                    '401106',
                                    '401106',
                                    '401107'],
                    's.m.v.': [ 7.760,
                                25.564,
                                25.564,
                                9.550,
                                4.870,
                                7.760,
                                25.564,
                                5.282,
                                25.564,
                                5.282]})
DFGrouped = DF.groupby(['date', 'line_code'], as_index = False)
DF_Agg = DFGrouped.agg({'s.m.v.' : MakeList})

In trying to debug this, I put a print statement to the effect of print L and print x.index and the output was as follows:

[7.7599999999999998, 25.564]
Int64Index([0, 1], dtype='int64')
[7.7599999999999998, 25.564]
Int64Index([0, 1], dtype='int64')

For some reason it appears that agg is passing the Series twice to the function. This as far as I know is not normal at all, and is presumably the reason why my function is not reducing.

For example if I write a function like this:

def test_func(x):
    print x.index
    return x.iloc[0]

This runs without problem and the print statements are:

DF_Agg = DFGrouped.agg({'s.m.v.' : test_func})

Int64Index([0, 1], dtype='int64')
Int64Index([2], dtype='int64')
Int64Index([3], dtype='int64')
Int64Index([4], dtype='int64')
Int64Index([5, 6], dtype='int64')
Int64Index([7, 8], dtype='int64')
Int64Index([9], dtype='int64')

Which indicates that each group is only being passed once as a Series to the function.

Can anyone help me understand why this is failing? I have used this function with success in many many data sets I work with....

Thanks

Answer 1

I can't really explain you why, but from my experience list in pandas.DataFrame don't work all that well.

I usually use tuple instead. That will work:

def MakeList(x):
    T = tuple(x)
    if len(T) > 1:
        return T
    else:
        return T[0]

DF_Agg = DFGrouped.agg({'s.m.v.' : MakeList})

     date line_code           s.m.v.
0  2013-04-02    401101   (7.76, 25.564)
1  2013-04-02    401102           25.564
2  2013-04-02    401103             9.55
3  2013-04-02    401104             4.87
4  2013-04-02    401105   (7.76, 25.564)
5  2013-04-02    401106  (5.282, 25.564)
6  2013-04-02    401107            5.282