Sum a panel along multiple dimensions
numpy's `sum()` function allows specifying a tuple of axis values for higher-dimensional arrays, but when it is called this way on a Pandas `Panel` or `Panel4D` it raises a ValueError:

```
>>> import numpy as np
>>> import pandas as pd
>>> a = np.random.randn(3, 3, 3)
>>> a.sum(axis=(0, 1))
array([-1.91974326,  1.49781967, -1.29698099])
>>> pan = pd.Panel(a)
>>> pan.sum(axis=(0, 1))  # should return the same vector
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/core/generic.py", line 4554, in stat_func
    skipna=skipna, numeric_only=numeric_only)
  File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/core/panel.py", line 1084, in _reduce
    axis_name = self._get_axis_name(axis)
  File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/core/generic.py", line 327, in _get_axis_name
    .format(axis, type(self)))
ValueError: No axis named (0, 2) for object type <class 'pandas.core.panel.Panel'>
>>> np.sum(pan, axis=(0, 1))  # try it another way
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/numpy/core/fromnumeric.py", line 1828, in sum
    return sum(axis=axis, dtype=dtype, out=out)
  File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/core/generic.py", line 4554, in stat_func
    skipna=skipna, numeric_only=numeric_only)
  File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/core/panel.py", line 1084, in _reduce
    axis_name = self._get_axis_name(axis)
  File "/Library/Frameworks/Python.framework/Versions/3.3/lib/python3.3/site-packages/pandas/core/generic.py", line 327, in _get_axis_name
    .format(axis, type(self)))
ValueError: No axis named (0, 1) for object type <class 'pandas.core.panel.Panel'>
```

Right now the workaround is to call `np.asarray()` on the Pandas object first and then convert it back to `Series` (or `DataFrame`, I suppose) once the result is returned, being sure to apply the correct index to the resulting object.
