Type inference problem with read_csv
I have a large CSV file that contains a single column of integers. When loading the CSV file with read_csv, nearly 2/3rds of the approximately 1.5 million values are loaded as ints while the remaining values are loaded as strings. I see no obvious problem with the file that would lead to this behavior.
The file I used is available here: https://drive.google.com/file/d/0ByZvgdTf0yfAT2dPdHRvc2hLVkU/view?usp=sharing

```
test_df = pd.read_csv('test.csv',header=0)
from collections import Counter
c = Counter()
els = []
for el in test_df['SERIALNO'].values:
    c[type(el)] += 1
print c
Counter({<type 'int'>: 952026, <type 'str'>: 524288})
```

It appears that a continguous block of rows were read as strings - a block in the middle of the file. Very curious. 

pd.show_versions()
## INSTALLED VERSIONS

commit: None
python: 2.7.9.final.0
python-bits: 64
OS: Darwin
OS-release: 13.4.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.15.2
nose: 1.3.4
Cython: 0.21
numpy: 1.9.2
scipy: 0.15.1
statsmodels: 0.5.0
IPython: 3.0.0
sphinx: 1.2.3
patsy: 0.3.0
dateutil: 2.4.1
pytz: 2014.9
bottleneck: None
tables: 3.1.1
numexpr: 2.3.1
matplotlib: 1.4.0
openpyxl: 2.1.0
xlrd: 0.9.3
xlwt: 0.7.5
xlsxwriter: 0.5.7
lxml: 3.4.0
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
rpy2: None
sqlalchemy: 0.9.7
pymysql: None
psycopg2: None
