When reading data with read_table, columns with ints and NAs are turned into floats
Minimal case:

ID Data
1  45.0
NA NA
2  34

I use read_clipboard in this example, but it affects also read_table:

``` python

In [4]: pandas.read_clipboard(sep="\s+")
Out[4]: 
   ID   Data
0  1.0  45. 
1  NaN  NaN 
2  2.0  34.

In [9]: pandas.read_clipboard(sep="\s+").dtypes
Out[9]: 
ID      float64
Data    float64
```

Is this working as intended? I ended up writing a wrapper to pandas.read_table to work around this issue (I tried writing a converter function, but it fails with the NAs).

As a clarification, the behavior I'm mentioning is the one in the ID column (the Data column contains floats).
