Allow preservation of object dtype when convert_numeric=True
what's the utility here?
 The current behavior hardly seems desirable, even though there are explicit tests for it:

```
In [26]: df = pd.DataFrame({'a': ['1', '2', '3'], 'b': ['a', 1, 'b']})

In [27]: df
Out[27]:
   a  b
0  1  a
1  2  1
2  3  b

In [28]: df.convert_objects(convert_numeric=True)
Out[28]:
   a   b
0  1 NaN
1  2   1
2  3 NaN

In [29]: df.convert_objects(convert_numeric=True).dtypes
Out[29]:
a      int64
b    float64
dtype: object
```
 that's exactly the purpose
to clean a mixed series 
in effect it is a coerce a not-something to nan
 but why does "clean" mean "clobber my data if it isn't numeric dtyped"?
 well you have a mixed series 
if u are explicitly calling convert object and setting convert_numeric=True that is what you are asking
 the reason for this is you read in data and it came out object dtype because the csv was off - so eliminate non-numbers
 turned out this wasn't necessary
