ENH: Generalize cut/melt to handle datetime input
see also #6434

Originally from:
http://stackoverflow.com/questions/22286930/is-it-possible-to-use-cut-on-a-collection-of-datetimes?noredirect=1#comment33862629_22286930

Would it be possible to enhance the cut function to handle datetime inputs?

For example, say I have the following setup:

```
import pandas as pd
import numpy as np

np.random.seed(0)
obs_list = ['John', 'Paul', 'George', 'Ringo']

date_indices = [pd.date_range('2014-01-01', '2014-02-01', freq=f) for f in ['1d', '3h', '25min']]

dfs = [pd.DataFrame({'observer': np.random.choice(obs_list, len(di)),
                     'value': np.random.randn(len(di))+ (100*i)},
                    index=di)
       for i, di in enumerate(date_indices)]

bigframe = pd.concat(dfs)
```

I would like to split up `bigframe` into 7 "bins" and explore the properties within each bin.  I'd like the bins to be sequential and for each to contain (approxmiately) the same number of data points.

Ideally, I'd be able to do something like:

```
means = bigframe.groupby(pd.cut(bigframe.index, 7))['value'].mean()
```

I think I can get something like that by doing:

```
bigframe['dt_value'] = bigframe.index.map(lambda x: x.value)
means = bigframe.groupby(pd.cut(bigframe['dt_value'], 7))['value'].mean()
```

but that doesn't seem particularly elegant.  And it winds up with bins labelled with the datetime values instead of the more-nicely-readable datetimes themselves.
