filtration chain for DataFrames
Inspired by the R package dplyr I'd like to be able to chain together my data manipulation but can't find an elegant way to do a filter. 

For example if I have a group -> aggregate -> filter sequence, I'd do something like this

```

In [13]: df
Out[13]: 
     A      B      C      D
0  foo    one -0.193 -0.900
1  bar    one  1.505  0.223
2  foo    two  0.646 -0.025
3  bar  three -0.072  1.227
4  foo    two -1.367 -0.873
5  bar    two  1.176  2.317
6  foo    one  0.424  0.858
7  foo  three  2.129  0.038

In [14]: aggd = df.groupby('A').sum()

In [15]: final = aggd[aggd['C'] > 2]

In [16]: final
Out[16]: 
         C      D
A                
bar  2.608  3.767
```

What I'd like to be able to do is....

```
In [21]:  df.groupby('A').sum().apply_filter(lambda row: row['C'] > 0, axis=1)

Out[21]: 
         C      D
A                
bar  2.608  3.767
```

This should be a pretty simple addition basically calling DataFrame.apply and happy to open a PR for it but wanted to see what you guys thought about this? 
