Performance regression in DataFrame.to_csv
Hello,

I recently upgraded from pandas 0.17.1 to 0.18.1 and ran into a performance regression with DataFrame.to_csv:
#### Code Sample, a copy-pastable example if possible

import pandas
import numpy
import tempfile
fd, filename = tempfile.mkstemp(prefix='to_csv_', suffix='.csv')
test = pandas.DataFrame(numpy.random.rand(2000, 5000))
%timeit test.to_csv(filename, sep=';', index=False, header=False)

In pandas **0.17.1**:
1 loop, best of 3: **6.86 s** per loop

In pandas **0.18.1**:
1 loop, best of 3: **21.9 s** per loop

You will find attached the results given by the Spyder profiler running this code in both version. 

Most of the time (in pandas 0.18.1) is spent in the pandas.isnull method.
#### output of `pd.show_versions()`
## INSTALLED VERSIONS

commit: None
python: 2.7.11.final.0
python-bits: 64
OS: Windows
OS-release: 7
machine: AMD64
processor: Intel64 Family 6 Model 60 Stepping 3, GenuineIntel
byteorder: little
LC_ALL: None
LANG: fr_FR

pandas: 0.18.1
nose: 1.3.7
pip: 8.1.1
setuptools: 20.3
Cython: 0.23.4
numpy: 1.10.4
scipy: 0.17.0
statsmodels: None
xarray: None
IPython: 4.1.2
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.5.1
pytz: 2016.2
blosc: None
bottleneck: 1.0.0
tables: 3.2.2
numexpr: 2.5
matplotlib: 1.5.1
openpyxl: 2.3.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.8.4
lxml: 3.6.0
bs4: 4.4.1
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.12
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
pandas_datareader: None
None
![to_csv_0_17_1](https://cloud.githubusercontent.com/assets/21279450/18034142/afe10e44-6d35-11e6-9a03-2392e3a1328f.png)
![to_csv_0_18_1](https://cloud.githubusercontent.com/assets/21279450/18034143/afe59e00-6d35-11e6-8ec4-79bdde2dd312.png)
