feature request : lead/lag values based on timedeltas
Hello the Pandas team,

It's me again ;-)

Consider the following:

```
rng = pd.date_range('1/1/2011', periods=5, freq='T')
ts = pd.DataFrame({'A' : np.random.randn(len(rng))}, index=rng)
ts.drop(ts.index[[2,3]],inplace=True)

# note how I delete some rows so that the index contains holes

ts
Out[37]: 
                            A
2011-01-01 00:00:00  0.873475
2011-01-01 00:01:00  0.035362
2011-01-01 00:04:00 -0.903640
```

I can shift my data in many different ways

```
ts.A.shift(1)
ts.A.shift(1, freq='T')
ts['index']=ts.index.shift(1, freq='T')
```

but I am unable to generate a variable that contains the lagged values, say, **1 minute ago** (which is different from one observation ago as in `ts.A.shift(1)`). 

In other words, is it possible to get the following output?

```
                            A
2011-01-01 00:00:00  NaN
2011-01-01 00:01:00  0.873475
2011-01-01 00:04:00  NaN
```

I get a `NaN` at row 3 because there is no data at `2011-01-01 00:03:00` (1 minute ago)
I thought a timeindex was able to understand the time component of the index. Am I missing something here?

Thanks!
