groupby bug when we use the filter '==' .
```
dff = DataFrame({'A': np.arange(8), 'B': list('aabbbbcc')})
dff['C'] = np.arange(8)
dff.groupby('B').filter(lambda x: x['C'] == 6)
```

the result is as below:

```
A,B,C  
6,6,c,6,
7,7,c,7

2 rows × 3 columns 

```

the column C with the row `7,7,c,7` is not 6 but it was selected!

```
for name, group in dff.groupby('B'):
    print '###########'
    print group['C']==6
```

the result is as below:

```
###########
0    False
1    False
Name: C, dtype: bool
###########
2    False
3    False
4    False
5    False
Name: C, dtype: bool
###########
6     True
7    False
Name: C, dtype: bool

```

So I guess the filter '==' may only test the on the first element, not on the whole group!
The same things also occured when we use the following commands:

```
dff.groupby('B').filter(lambda x: x['C'] <= 6)
dff.groupby('B').filter(lambda x: x['C'] >= 6)
```
