when all missing data,  df.count() and df.sum() maybe better to return nan than 0
```
>>> import numpy as np
>>> import pandas as pd
>>> x = np.ma.masked_all( (5,2) )
>>> df = pd.DataFrame( x, columns=('a','b'))
>>> print( df.count() )
a    0
b    0
dtype: int64
>>> print( df.sum() )
a    0.0
b    0.0
dtype: float64
```

**when all missing data, 
df.count() and df.sum() maybe better to return nan than 0.
just like df.mean.**

```
 >>> df.mean()
a   NaN
b   NaN
dtype: float64
```