pivot_table: precision lost
#### Code Sample, a copy-pastable example if possible
```python
In [29]: df = pd.DataFrame([[1, 2, 11111111111111111]], columns=['index', 'type', 'value'])

In [30]: df.dtypes
Out[30]: 
index    int64
type     int64
value    int64
dtype: object

In [31]: df.pivot_table(index='index', columns='type', values='value')
Out[31]: 
type                   2
index                   
1      11111111111111112
```
#### Problem description
Since `value` is a 64-bit integer, we should be able to present it with zero precision loss.

#### Expected Output
```python
In [31]: df.pivot_table(index='index', columns='type', values='value')
Out[31]: 
type                   2
index                   
1      11111111111111111
```
#### Output of ``pd.show_versions()``

<details>
# Paste the output here pd.show_versions() here


INSTALLED VERSIONS
------------------
commit: None
python: 3.5.2.final.0
python-bits: 64
OS: Linux
OS-release: 3.10.0-514.2.2.el7.x86_64
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8

pandas: 0.18.0
nose: 1.3.7
pip: 9.0.1
setuptools: 28.8.0.post20161110
Cython: 0.23.4
numpy: 1.11.2
scipy: 0.17.0
statsmodels: 0.6.1
xarray: None
IPython: 5.1.0
sphinx: 1.3.5
patsy: 0.4.0
dateutil: 2.6.0
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: 0.9.2
apiclient: 1.5.2
sqlalchemy: 1.0.15
pymysql: None
psycopg2: None
jinja2: 2.8
boto: 2.39.0
</details>
