python中idxmax_Python Pandas groupby forloop& Idxmax

I have a DataFrame that must be grouped on three levels, and would then have the highest value returned. Each day there is a return for each unique value, and I would like to find the highest return and the details.

data.groupby(['Company','Product','Industry'])['ROI'].idxmax()

The return would show that:

Target - Dish Soap - House had a 5% ROI on 9/17

Best Buy - CDs - Electronics had a 3% ROI on 9/3

was the highest.

Here's some example data:

+----------+-----------+-------------+---------+-----+

| Industry | Product | Industry | Date | ROI |

+----------+-----------+-------------+---------+-----+

| Target | Dish Soap | House | 9/17/13 | 5% |

| Target | Dish Soap | House | 9/16/13 | 2% |

| BestBuy | CDs | Electronics | 9/1/13 | 1% |

| BestBuy | CDs | Electroincs | 9/3/13 | 3% |

| ...

Not sure if this would be a for loop, or using .ix.

解决方案

I think, if I understand you correctly, you could collect the index values in a Series using groupby and idxmax(), and then select those rows from df using loc:

idx = data.groupby(['Company','Product','Industry'])['ROI'].idxmax()

data.loc[idx]

another option is to use reindex:

data.reindex(idx)

On a (different) dataframe I happened to have handy, it appears reindex might be the faster option:

In [39]: %timeit df.reindex(idx)

10000 loops, best of 3: 121 us per loop

In [40]: %timeit df.loc[idx]

10000 loops, best of 3: 147 us per loop