fillna on a string column 
Using pandas version 0.16.0.
  I read a CSV file that has a string column with some missing values, and pandas loads those missing string values as NaN.  Pandas won't let me group on that string value, so I'm trying to fillna() with the value "<blank>". But that doesn't work either, pandas overwrites the good values then too.
   Yes, I understand fillna is apparently trying to convert the strings to numbers. But then there's no intuitive way to handle missing string values.  Can we change fillna to handle missing string values?

``` python
import pandas
import numpy
#The real use case is reading a CSV with missing values, but this demos the issue
df = pandas.DataFrame({ 'x': ['first', numpy.nan, 'third'], 'y' : [1, 2, 3]})
df #Shows we have a missing value in the string column

#I want to group by 'x'. The following won't work because pandas doesn't
#correctly sum the value for the missing row (it ought to be "2" but shows blank).
#Yes, I understand NaN != NaN, so I get why it fails, but then I need a workaround.
df.groupby('x').sum() #shows blank for the missing value, instead of "2".

#OK, so I'll fillna the missing value and then group.
df['x'] = df['x'].fillna('<blank>', inplace=True)
df #BAD! That overwrote all my good string values!!!
```

Since the column was already an object (with just strings) and I'm fillna with a string, it should not have to drop values.

pandas.show_versions()
## INSTALLED VERSIONS

commit: None
python: 3.4.3.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel
byteorder: little
LC_ALL: None
LANG: None

pandas: 0.16.0
nose: None
Cython: None
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.6.1
IPython: 5.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.4.2
pytz: 2015.2
bottleneck: None
tables: None
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 2.2.2
xlrd: 0.9.3
xlwt: None
xlsxwriter: 0.7.2
lxml: None
bs4: 4.3.2
html5lib: None
httplib2: 0.9.1
apiclient: None
sqlalchemy: 1.0.3
pymysql: None
psycopg2: None
