How to aggregate duplicate timestamps with pandas?

Question

I am working on python (pandas specifically) to analyze a dataset. (Python is too awesome, the power of open source is amazing). I am having trouble with a specific part of my dataset.

I have the following data set,

time,contract,ticker,expiry,strike,quote,price,volume
08:01:08,C,PXA,20100101,4000,A,57.8,60
08:01:11,C,PXA,20100101,4000,A,58.4,60
08:01:12,C,PXA,20100101,4000,A,58,60
08:01:16,C,PXA,20100101,4000,A,58.4,60
08:01:16,C,PXA,20100101,4000,A,58,60
08:01:21,C,PXA,20100101,4000,A,58.4,60
08:01:21,C,PXA,20100101,4000,A,58,60

and it goes on ...

I am using pandas to load the data. After this, I would like to be able to do the following, take a volume weighted average of the time there are duplicates.

i.e. since there are two asks at time 08:01:16, I would like to calculate the average price based on volume which would be (58.4*60 + 58*60)/(60+60) and an average of the volume on the volume column which would be (60+60)/2.

Answer 1

In [28]: a = pd.read_csv('aa.csv')

In [29]: a
Out[29]: 
       time contract ticker    expiry  strike quote  price  volume
0  08:01:08        C    PXA  20100101    4000     A   57.8      60
1  08:01:11        C    PXA  20100101    4000     A   58.4      60
2  08:01:12        C    PXA  20100101    4000     A   58.0      60
3  08:01:16        C    PXA  20100101    4000     A   58.4      60
4  08:01:16        C    PXA  20100101    4000     A   58.0      60
5  08:01:21        C    PXA  20100101    4000     A   58.4      60
6  08:01:21        C    PXA  20100101    4000     A   58.0      60

In [30]: pd.DataFrame([{'time': k,
                        'price': (v.price * v.volume).sum() / v.volume.sum(),
                        'volume': v.volume.mean()}
                       for k,v in a.groupby(['time'])],
                      columns=['time', 'price', 'volume'])
Out[30]: 
       time  price  volume
0  08:01:08   57.8      60
1  08:01:11   58.4      60
2  08:01:12   58.0      60
3  08:01:16   58.2      60
4  08:01:21   58.2      60