BUG: .nlargest with unsigned integers
#### Code Sample, a copy-pastable example if possible

```python
pd.Series(np.array([0, 0, 0, 100, 1000, 10000, 100], dtype='uint32')).nlargest(5) 
0        0
1        0
2        0
5    10000
4     1000

```
#### Problem description
`nlargest` favours `0` above positive values. Common to both `uint32` and `uint64` types and possibly others. 

#### Expected Output

```
5    10000
4     1000
3      100
6      100
0        0
dtype: uint32
```

#### Output of ``pd.show_versions()``
<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.6.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-327.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_GB.UTF-8
LOCALE: en_GB.UTF-8

pandas: 0.20.3
pytest: 3.2.1
pip: 9.0.1
setuptools: 36.4.0
Cython: None
numpy: 1.13.1
scipy: 0.19.1
xarray: None
IPython: 6.1.0
sphinx: None
patsy: 0.4.1
dateutil: 2.6.1
pytz: 2017.2
blosc: None
bottleneck: None
tables: 3.4.2
numexpr: 2.6.2
feather: None
matplotlib: 2.0.2
openpyxl: 2.4.8
xlrd: None
xlwt: None
xlsxwriter: None
lxml: None
bs4: None
html5lib: 0.9999999
sqlalchemy: 1.1.13
pymysql: 0.7.9.None
psycopg2: None
jinja2: 2.9.6
s3fs: None
pandas_gbq: None
pandas_datareader: None


</details>
