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

Hey Todd-- thanks. Couple points
- For things like this, it's best to work in feature branches, e.g.: http://pandas.pydata.org/developers.html#getting-started-with-git. Otherwise you get merge commits like above in your master branch and it becomes harder to collaborate by pull request.
- For the test suite, use `nosetests path/to/test_foo.py` or `python path/to/test_foo.py`. Either way you need nose
 I'll cherry pick your fixes and and fix things up
 I decided to make min/max and everything else return NaN when length 0
 Hi Wes,

Thanks for the pointer to the "getting started" link.  I'll use a feature
branch next time.

Regards,
Todd

On Thu, Jul 19, 2012 at 8:09 PM, Wes McKinney <
reply@reply.github.com

> wrote:
> 
> Hey Todd-- thanks. Couple points
> - For things like this, it's best to work in feature branches, e.g.:
>   http://pandas.pydata.org/developers.html#getting-started-with-git.
>   Otherwise you get merge commits like above in your master branch and it
>   becomes harder to collaborate by pull request.
> - For the test suite, use `nosetests path/to/test_foo.py` or `python
>   path/to/test_foo.py`. Either way you need nose
> 
> ---
> 
> Reply to this email directly or view it on GitHub:
> https://github.com/pydata/pandas/pull/1650#issuecomment-7119033

## 

Todd DeLuca
http://todddeluca.com
http://wall.hms.harvard.edu/
