Series.describe() fails for empty and None series.
Running `describe()` raises and exception for `Series([])` and `Series([None])`.  See the following examples:

```
In [416]: Series([]).describe()
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-416-162185f07510> in <module>()
----> 1 Series([]).describe()

/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.8.0rc2-py2.7-macosx-10.4-x86_64.egg/pandas/core/series.pyc in describe(self, percentile_width)
  1363                      'max']
  1364 
-> 1365             data = [self.count(), self.mean(), self.std(), self.min(),
  1366                     self.quantile(lb), self.median(), self.quantile(ub),
  1367                     self.max()]

/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.8.0rc2-py2.7-macosx-10.4-x86_64.egg/pandas/core/series.pyc in min(self, axis, out, skipna, level)
  1067         if level is not None:
  1068             return self._agg_by_level('min', level=level, skipna=skipna)
-> 1069         return nanops.nanmin(self.values, skipna=skipna)
  1070 
  1071     @Substitution(name='maximum', shortname='max',

/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.8.0rc2-py2.7-macosx-10.4-x86_64.egg/pandas/core/nanops.pyc in f(values, axis, skipna, **kwds)
    41                 result = alt(values, axis=axis, skipna=skipna, **kwds)
    42         except Exception:
---> 43             result = alt(values, axis=axis, skipna=skipna, **kwds)
    44 
    45         return result

/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.8.0rc2-py2.7-macosx-10.4-x86_64.egg/pandas/core/nanops.pyc in _nanmin(values, axis, skipna)
    152             result = __builtin__.min(values)
    153     else:
--> 154         result = values.min(axis)
    155 
    156     return _maybe_null_out(result, axis, mask)

ValueError: zero-size array to minimum.reduce without identity

In [388]: s = pandas.Series([None])

In [389]: s.describe()
---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
<ipython-input-389-97bd840d09f5> in <module>()
----> 1 s.describe()

/usr/local/Cellar/python/2.7.3/Frameworks/Python.framework/Versions/2.7/lib/python2.7/site-packages/pandas-0.8.0rc2-py2.7-macosx-10.4-x86_64.egg/pandas/core/series.pyc in describe(self, percentile_width)
  1334 
  1335             objcounts = Counter(self.dropna().values)
-> 1336             top, freq = objcounts.most_common(1)[0]
  1337             data = [self.count(), len(objcounts), top, freq]
  1338 

IndexError: list index out of range

In [395]: s = pandas.Series([np.nan, np.nan])

In [396]: s.describe()
Out[396]: 
count     0
mean    NaN
std     NaN
min     NaN
25%     NaN
50%     NaN
75%     NaN
max     NaN
```

I have included `Series([np.nan])` because its output is affected by the pull request.

After the pull request the output is as follows:

```
In [3]: Series([]).describe()
Out[3]: count    0

In [4]: Series([None]).describe()
Out[4]: 
count     0
unique    0

In [5]: Series([np.nan]).describe()
Out[5]: count    0
```

The pull request fixes the `ValueError` from the call to `values.min(axis)` and the `IndexError` from accessing `most_common(1)[0]` by only returning a small amount of count data when the Series object is empty or contains all NaN or None values.  Is this the right approach?  It changes the output for Series objects with all NaN values.  

Test coverage was written but I couldn't figure out how to run the tests.  Any hints?

Regards,
Todd
