ENH: unsparsify multi-index column labels
`read_csv` requires the multi-index column labels to be non-sparse in order to properly parse them. I am not sure if this can be done automatically, though maybe a keyword could trigger this.

```
In [40]: def unsparsify_labels(index):
   ....:     new_labels = []
   ....:     for label in index.values:
   ....:         if label[0].startswith('Unnamed'):
   ....:             label = list(label)
   ....:             label[0] = ll
   ....:             label = tuple(label)
   ....:         else:
   ....:             ll = label[0]
   ....:         new_labels.append(label)
   ....:     return MultiIndex.from_tuples(new_labels)
   ....: 

In [41]: unsparsify_labels(x)
Out[41]: 
MultiIndex(levels=[[u'A', u'B'], [u'1', u'2']],
           labels=[[0, 0, 1, 1], [0, 1, 0, 1]])

In [42]: data = """A,,B,
   ....: 1,2,1,2
   ....: 1,2,3,4
   ....: 5,6,7,8
   ....: """

In [43]: df = read_csv(StringIO(data),header=[0,1],index_col=None)

In [44]: df
Out[44]: 
   A  Unnamed: 1_level_0  B  Unnamed: 3_level_0
   1                   2  1                   2
0  1                   2  3                   4
1  5                   6  7                   8

[2 rows x 4 columns]

In [45]: df.columns = unsparsify_labels(df.columns)

In [46]: df
Out[46]: 
   A     B   
   1  2  1  2
0  1  2  3  4
1  5  6  7  8

[2 rows x 4 columns]
```

http://stackoverflow.com/questions/20473271/importing-multilevel-indexed-cvs-data-with-pandas-0-13/20474982#20474982
