Doing a groupby and rolling window on a Pandas Dataframe with a multilevel index leads to a duplicated index entry

If I do a groupby() followed by a rolling() calculation with a multi-level index, one of the levels in the index is repeated - most odd. I am using Pandas 0.18.1

import pandas as pd
df = pd.DataFrame(data=[[1, 1, 10, 20], [1, 2, 30, 40], [1, 3, 50, 60],
                        [2, 1, 11, 21], [2, 2, 31, 41], [2, 3, 51, 61]],
                  columns=['id', 'date', 'd1', 'd2'])

df.set_index(['id', 'date'], inplace=True)
df = df.groupby(level='id').rolling(window=2)['d1'].sum()
print(df)
print(df.index)

The output is as follows

id  id  date
1   1   1        NaN
        2       40.0
        3       80.0
2   2   1        NaN
        2       42.0
        3       82.0
Name: d1, dtype: float64
MultiIndex(levels=[[1, 2], [1, 2], [1, 2, 3]],
           labels=[[0, 0, 0, 1, 1, 1], [0, 0, 0, 1, 1, 1], [0, 1, 2, 0, 1, 2]],
           names=[u'id', u'id', u'date'])

What is odd is that the id column now shows up twice in the multi-index. Moving the ['d1'] column selection around doesn't make any difference.

Any help would be much appreciated.

Thanks Paul

It is bug.

But version with apply works nice, this alternative is here (only d1 was moved to apply):

df = df.groupby(level='id').d1.apply(lambda x: x.rolling(window=2).sum())
print(df)
id  date
1   1        NaN
    2       40.0
    3       80.0
2   1        NaN
    2       42.0
    3       82.0
Name: d1, dtype: float64