BUG: max rows in read_csv
I have 5e9 rows csv file that I'm trying to load with pandas, which appears to load it only partially and produces 2852516352 rows data frame.

``` py
#Python 2.7.3 (default, Jun 22 2015, 19:33:41) 
#[GCC 4.6.3] on linux2
#Type "help", "copyright", "credits" or "license" for more information.
import pandas as pd
import os
import gc
import timeit
x = pd.read_csv("X5e9_2c.csv")
x.head()
#          KEY          X2
#0  2632858426 -4008534609
#1  3176486913 -3394302982
#2  3235589527 -4982919314
#3  1017229071  3793469468
#4  1039519144  2740983791
gc.collect()
#21
t_start = timeit.default_timer()
ans = x.sort_values('KEY')
print ans.shape[0]
#2852516352
t = timeit.default_timer() - t_start
x.shape[0]
#2852516352
#t
6073.774994134903
```

Expected behaviour would be either properly read csv or an error, so it would save ~100 minutes of processing in my case.  

my `pd.show_versions()`

```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.3.final.0
python-bits: 64
OS: Linux
OS-release: 3.13.0-61-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.1
nose: 1.3.7
pip: 8.0.3
setuptools: 17.0
Cython: None
numpy: 1.11.1
scipy: 0.16.0
statsmodels: 0.6.1
xarray: None
IPython: None
sphinx: 1.3.1
patsy: 0.4.0
dateutil: 2.5.3
pytz: 2016.6.1
blosc: None
bottleneck: None
tables: None
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: None
xlrd: 0.9.3
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: None
httplib2: 0.9.1
apiclient: None
sqlalchemy: None
pymysql: None
psycopg2: None
jinja2: 2.7.3
boto: 2.38.0
pandas_datareader: None
```
