DataFrame.duplicated detects duplicates when none exist
Hello, 

I'm running into what I think is a bug in DataFrame.duplicated where it detects duplicates, but the data frame does not actually have any duplicated rows. It seems to only happen with integer columns, and somewhat large datasets (>600,000 rows). 

I created a test data set to show the issue: 

```
df = pd.read_table(
  "https://www.dropbox.com/s/vkw8bzxp290jitz/test.tab?raw=1",
  dtype = {"chrom" : "int64","pos" : "int64"}
)
```

If you ask for duplicates, it will detect them: 

```
df.duplicated().any() # returns True
```

However, there are no duplicates: 

```
In [5]: from collections import Counter

In [6]: counter = Counter(zip(df.chrom,df.pos))

In [7]: counter.most_common(5)
Out[7]:
[((0, 13704091), 1),
 ((0, 201539008), 1),
 ((0, 8573433), 1),
 ((0, 127434927), 1),
 ((0, 247829766), 1)]
```

If I convert one of the columns to float, and then ask for duplicates, it is correct: 

```
df.loc[:,"pos"] = df.pos.astype("float64")
df.duplicated().any() # returns False
```

Strangely, converting the first column `chrom` to float or string does not seem to matter. 

I had a difficult time in constructing this data frame to illustrate the example. It seems to only occur: 
- With at least two columns
- One column (integer, string, float) that has a small number of unique values
- One column (must be integer) that has a wide range of values, but in many instances, the values are close to each other 
- The dataset must be somewhat large, but it's hard to pin down. Somewhere on the order of > 500,000 rows, it seems. 

From looking quickly at the `DataFrame.duplicated` code, it looks like it is using a hash table of some kind, and using integer columns differently than other columns - perhaps it's ending up with collisions? 

Apologies if I'm missing something obvious here. Please let me know if I can be of any help in investigating further. My pandas version information is below. 

```
INSTALLED VERSIONS
------------------
commit: None
python: 2.7.10.final.0
python-bits: 64
OS: Darwin
OS-release: 14.5.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.17.0
nose: 1.3.7
pip: 7.1.2
setuptools: 18.4
Cython: 0.22.1
numpy: 1.10.1
scipy: 0.16.0
statsmodels: 0.6.1
IPython: 3.2.0
sphinx: 1.3.1
patsy: 0.3.0
dateutil: 2.4.2
pytz: 2015.6
blosc: None
bottleneck: None
tables: 3.2.0
numexpr: 2.4.3
matplotlib: 1.4.3
openpyxl: 2.0.2
xlrd: 0.9.4
xlwt: 1.0.0
xlsxwriter: 0.7.3
lxml: 3.4.4
bs4: 4.3.2
html5lib: None
httplib2: None
apiclient: None
sqlalchemy: 1.0.7
pymysql: None
psycopg2: None
```
