DataFrame.convert_objects(convert_numeric=True) is too aggressive with mixed string-integer columns
Even a single numeric-compatible field is sufficient to convert the whole column to numeric, rendering all other non-numeric fields (in that column) NaN.

The following code snippets demonstrates the problem.

``` python

In [1]: import pandas as pd

In [2]: pd.__version__
Out[2]: '0.13.1'

In [3]: df = pd.DataFrame(["x","y","10","z"])

In [4]: df[0]
Out[4]: 
0     x
1     y
2    10
3     z
Name: 0, dtype: object

In [5]: df = df.convert_objects(convert_numeric=True)

In [6]: df[0]
Out[6]: 
0   NaN
1   NaN
2    10
3   NaN
Name: 0, dtype: float64

```

The expected result is that this column should _not_ be converted to numeric, and should remain of type object. Just because that one element happens to be numeric-compatible doesn't mean that the entire column should be converted.
